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Cranbrook Kitchen Sink
The Weekly Blog of the Cranbrook Center for Collections and Research
The Skyline is a Promise
Cranbrook Archives houses an impressive collection of motion picture films, many of which offer depictions of student life at Cranbrook Schools. These films uniquely capture what it was actually 'like' to be on campus at a given moment in time, and potentially present perspectives not captured in official written documentation. One such film, The Skyline is a Promise, from the Horizons-Upward Bound Records, is an excellent example.
Filming on Cranbrook School campus, 1966. Copyright Cranbrook Archives.
The Horizons-Upward Bound (HUB) program, then in its 2nd year, was self-described as "An Experimental Enrichment Program." In conjunction with representatives from Detroit Public Schools and Oakland County Schools, the program's objective was to provide low-income Detroit area high school students with opportunities for future success in academics and in life. The creation of Cranbrook School teacher, Ben Snyder, who served as its director for twenty-four years, HUB was the only program of its kind at that time.
Skyline was produced, directed, and filmed by Wayne State University Audio-Visual Productions during the summer of 1966, and was intended both as a promotional piece and an educational aid. The 16-minute short film captured every aspect of the program. Raymond Maloney, a HUB English instructor from Cranbrook School, wrote of the experience in the 1966 Annual Report: "At times, the dining hall, classrooms or dormitories took on all the aspects of a movie set complete with eighty-eight willing actors." Students not only were eager participants in front of the camera but also learned about what was involved behind the camera, thanks to one of the film's producers.
Wayne State University film crew, 1966. Copyright Cranbrook Archives.
Funding for the film was provided by an anonymous donor, supplemented by funds from the program's Ford Foundation grant. According to a May 25, 1966 letter, the script was written by George H. Bouwman, Director of Development, Horace Mann School, Bronx, NY. The soundtrack was a mix of guitar music, sound effects, and voice-overs from both the narrator and student interviews, also conducted by the film crew. The film's title was taken from The Wanderer of Liverpool by John Masefield, Poet Laureate of the United Kingdom (1930-1967):
Go forth to seek: the quarry never found
Is still a fever to the questing hound
The skyline is a promise, not a bound.
Beginning in the Summer of 1967, the film was circulated for a $5 rental fee throughout the U.S. (with accompanying report) to other independent schools considering establishing a similar program. Made at the suggestion of the National Association of Independent Schools, it was shown in its first year at 36 member schools in 18 states, 6 school conferences across the country, and 14 other organizations including the Education Departments of Wayne State University and the University of Michigan.
That same summer saw a watershed moment in Detroit history: civil unrest with profound ramifications for the city's inhabitants, which included sixty Horizon students and their families. A full page in HUB's 1967 Annual Report expresses appreciation to those who particularly helped navigate the complexities of the situation, including Detroit educators, clergy, business leaders, and local figures, such as Detroit Tigers player Al Kaline. Cranbrook would continue to have ties to Detroit institutions through its HUB program, like the relationship it formed with New Detroit in 1968. This summer, HUB will celebrate its 55th anniversary, remaining an important link between Cranbrook and its Metro Detroit neighbors.
Fifty-four years later, Skyline transcends its original intent and gives us a window into the experiences of a specific group of students at Cranbrook during a tumultuous time in our region's history. The film, like many others in the Archives, is currently undergoing review for reformatting to digital media for access and preservation of the originals, so that their stories are not lost to future generations.
– Deborah Rice, Head Archivist
2 years ago Short URL Comments
Archives/Horizons Upward Bound/Images/Schools
Detroit/Education/film/Wayne State University
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Christian Democrat Allan Green evasive on the Millenium Development Goals?
If you care about balanced Christian representation feel free to share this on. I had an interesting Facebook conversation this week with Allan Green, the CDP candidate for Greenway, where I asked him whether the Christian Democratic Party would support groups like Micah Challenge in pushing the major parties to meet their Millennium Development Goal commitments, specifically the commitment to raise aid to 0.7% GNI by 2015.
Rather than answering yes to that question, even though I'd asked for a clear yes or no answer, Allan Green evaded and instead touted his support for an aid agency called GK Philippines.
So I take that as a no, that Christian Democratic Party are not willing to support Christians groups like Micah Challenge.
In this respect Allan Green's response was much like that of labour, who have also declined to give a clear timetable for implementation of the 0.7% commitment, except in this respect: his claim to represent Christians, without qualification.
So, just so it's clear whom I represent and whom I don't, I'll let SEO and SMO speak for itself. I've taken the conversation off the Facebook group site where it was originally hosted and placed it here, on my personal site. And I'll let you speak for yourselves, if you are a Christian who cares about this, join me in calling for Greenway candidate Allan Green and Christian Democratic Party to make a clear commitment to Micah Challenge and the Millennium Development Goals.
I have not been evasive. Actually I said that neither major party was doing enough. GK is a highly respected proven poverty housing program which is spreading across Asia. My involvement in GK proves that I am much more than just talk when it comes to World poverty. In fact the goal should be higher than the Micah challenge. I think its sad when people make slanted comments against sincere Christian candidates.
I think Matt you should show more respect politness in your communication if you are a sincere Christian.
Allen, I find you evasive because you're consistently avoiding the question. I'm wanting to know if the Christian Democratic Party are willing to commit to the specific goal of 0.7% GNI by 2015.
If you're serious that the goal "should be much higher than Micah Challenge" then you can disarm me at once by naming a concrete policy commitment on aid. What are we talking? 0.8%? A 2014 timetable? That would be indeed be a substantial commitment and I'd only be too pleased to acknowledge it and publicize it. But so far you have not said what you're prepared to fight for on aid. The only party who've made a commitment to meet the Millenium Development Goal commitments so far are the Greens, the party the Christian Democrats seem to be placing last on their how to vote sheets they're mailing around. How is it that you're allowing yourselves to be upstaged by the Greens on this?
Personally I find this very dissappointing. Because I disagree with the Greens on many other issues. I'd like to be able to say the Christian Democrats have taken a bold stance on aid. I have no issue with GK, it sounds great, but what I'd like to see is the personal experience matched by some policy commitments on foreign aid. Something that clearly differentiated the Christian Democrats from Liberal and Labour and put you above the Greens, demonstrating a political commitment to the global poor and more than just a personal one.
If you think its impolite for me to scrutinize political candidates that's unfortunate, but I do not think the Christian Democrats should be exempt from scrutiny just because they are Christian. If you claim your policies are the more Christian this needs to be tested. I regret if testing that offends.
PS. Since policy scorecards are all the rage this week here's an alternative one that specifically focuses on the Millenium Development Goals that Micah Challenge is championing on behalf of concerned Christians.
Note: I am not endorsing any of the parties here in toto, as this scorecard only covers a fraction of their respective policies, but I am interested in what commitments the Christian Democrats would make.
You have twisted what I said in mean spirited way.
You have ignored the real work I have done to address World Poverty. I have used my own time and resources to support GK which is a best practice poor housing and self actualistaion project which is highly respected thoughout Asia.
I never said I would not support the Micah Challenge.
I can see a lot of merit in the Micah challenge and would like to see it achieved by the next Government.
I wonder if you have been to Asia and seen the problem first hand like I have. I hope one day you will mature enough to communicate with proper manners and Christian consideration for your fellow man.
I can see you have high enthusiam but you lack maturity.
I wish you well and I will pray for your future development.
Allen, again you avoid the question and now you're resorting to slander. I simply want a straight answer on the Christian Democrats foreign aid policy, on what level you think aid should be set at. Why is that too much to ask? I'm seeing a distinct lack of transparency here. Why is it too much to ask a Greenway candidate what his party's policy is on this? Why the slander rather than a straight answer?
The CDP Entrance Exam for Candidates.
Kalessin, I hope to one day have the opportunity to shake your hand.
For reference, I have asked the same question of Michelle Rowlands, the Labour candidate for Greenway. She conceeded that Labour does not have a clear timetable for 0.7% GNI, stated her reasons why and what her party was prepared to commit to.
Wow, thanks for posting this Matt and allowing a forum for some open discussion.
Unfortunately this seems to be the attitude taken by far-right wing Christians – and they just don't seem to be able to see how immature they look to those looking in, despite the fact that they freely throw around similar accusations to those who are simply asking questions.
I had a similar confrontation on my blog here: http://www.timmalone.id.au/2010/08/07/warning-be-careful-with-some-christian-voting-literature/ (the strange behaviour starts at about the 13th comment down).
I'll certainly be writing more along these lines – I'll link to your post as another example. (By the way, and I suspect you're the same, I write about this sort of thing publicly simply out of a desire to expose something which is going fairly unchecked in the wider Christian community, and is both misleading non-politically aware Christians, and providing a warped view to the public of what Christ represents).
Tim, interesting conversation. Yes, similarly I simply seek to draw attention to some imbalances and inequities. A discussion of Christian values that dismisses questions about how much we assign to foreign aid (as immature nitpicking) is not balanced.


A war veteran who survived an explosion in Iraq is using his life to spread inspiration, hope and faith. He's starting a new chapter in his life that will be witnessed by millions of people.
SAN ANTONIO — Army Staff Sgt. Shilo Harris has used his scars to help other veterans and inspire thousands of people since 2007. He sustained devastating injuries on February 19th in Iraq. He was traveling in an armored vehicle with 5 other soldiers, when they were hit by an IED. The driver and Harris were the only ones who survived the explosion.
A third of Harris' body was burned, he lost several of his fingers, his nose and ears. He had to undergo 80 surgeries. During his time at Brooke Army Medical Center, Harris said he would greet other wounded military members.
"I didn't seek out wanting to be a motivational speaker. I really didn't. I did what I do around the hospital in bandages," he said. "I'd usually be one of those faces that would welcome them to the trauma center and what they may be facing on a day-to-day basis."
Throughout the years, he said he's learned to wear his scars and doesn't focus on what's on the outside.
"I've tried to steer away from how I look. What was more important is how I feel. I feel very successful in my recovery. I feel very proud of my military service and I feel very blessed with being back home," he said.
Harris began speaking at events and eventually, he made it into a career. Now, he travels across the country as a motivational speaker. Harris said his family and faith have helped him every step of the way.
Harris is celebrating another blessing in his life. He will be getting married to his fiancee, Jamie PK. The ceremony will be broadcast live on television. The couple will be featured on "My Great Big Live Wedding with David Tutera" on Lifetime. The show will give dream weddings to eight couples that have remarkable stories.
"Many people need hope in their life and it's nice that there's going to be a show that provides hope for people not just for love but after something unique or tragic has happened," said PK.
She first, got connected to Harris because one of her best friends happened to be his care nurse at BAMC. PK said in the beginning, she wanted to help Harris in his career.
"He asked me out several times but I said nope. Working relationship only. I'm sorry, don't want to mix business with personal. But one night, in a flooded storm, he asked me out in a really romantic way. and I couldn't say no," said PK.
The couple works together to continue helping veterans. They have several projects in the works.
"You surround yourself with tribes throughout your life and this new chapter in our life and journey together is to bring joy to as many tribes as we can," said PK.
"I'm so proud that she's been in my life. We've been working on a book, we've been working on another movie, it's like a whirlwind and it's all happening at once," said Harris.
You can catch the couple's live wedding March 12th, 10 p.m. ET/PT. If you would like to get more information on Harris and his initiatives to help veterans, click here.


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\section{Introduction}
Financial institutions operate in a complex and dynamic environment, where they are exposed to multiple risk sources, with credit risk being the most significant \citep{Apostolik2009}.
Credit risk management is necessary and should be part of each lending decision; adequate risk management avoids financial losses and is a crucial element for the profitability and well-being of the financial institution and its borrowers \citep{Brown2014}.
One of the main objectives of credit risk management is to determine if the borrower will repay a loan and meet the agreed terms \citep{Basel2000}. For this, it is necessary to have policies, procedures, experience, and the expertise to extract knowledge from massive data sources \citep{Brown2014}.
Researchers and practitioners have defined various types of credit scoring problems to manage credit risk, depending on the circumstances and background of each borrower \citep{Paleologo2010, Thomas2017}. In this research, we are interested in learning about application and behavioral scoring. Application Scoring supports the loan granting decision. Its objective is to assess the creditworthiness of new applicants; this is accomplished by combining the applicant's demographic information, loan repayment history, borrower historical data, and credit bureau data, along with data collected in the application form \citep{Anderson2022}. The credit risk management decision associated with application scoring is to grant loans to those borrowers who will be able to pay and avoid granting credits to those who will not.
Similarly, behavioral scoring models are used in credit risk management, but it is applied to existing customers \citep{Paleologo2010, Anderson2022}. In this way, all the loan payment behavior of the borrowers is available to develop an active portfolio management process. It enables lenders to take preventive actions with borrowers with high default likelihood, such as reducing the financial burden of those borrowers who have difficulties complying with the payment schedule and established obligations.

Research on credit scoring is extensive but mainly focused on application scoring. Although some researchers describe behavioral scoring knowledge as limited and scarce \citep{Liu2001, Kennedy2013, Goh2019}, we are interested in delving into what we already understand about application scoring and behavioral scoring. First, both credit scoring problems combine borrower demographic data, historical information, and features obtained from multiple data sources. Repayment history emerges as one of the main creditworthiness predictors. The effect of this feature set is seen mainly in behavioral scoring; in application scoring, the payment behavior often is not available, or the applicant often does not have it \citep{munoz2021}.
Second, better performance of credit scoring models leads to more accurate decision-making and allows more efficient and profitable credit risk management \citep{Verbraken2014, Djeundjie2021}.
To increase the discriminatory power of these models, financial institutions have used alternative data, especially information from borrowers' relationships and interactions \citep{Ruiz2018, Oskarsdottir2019, Roa2021eswa, munoz2021}. This type of information adds value to both types of credit scoring. However, it is in application scoring where it achieves the most significant performance enhancement, especially in applicants whose repayment history is not available.

We know the effect of repayment behavior and social-interaction data on application and behavioral scoring problems based on the above. Concerning repayment behavior, this is gaining relevance as the relationship becomes entrenched; the more and more information on behavior is collected, the more accurately the borrower's creditworthiness can be predicted. In the case of social interaction data, the opposite occurs; at some point, the borrower's behavior and repayment history replace the knowledge provided by this alternative data source.
Both relationships are engaging to study. However, to date, we do not know a study on the dynamics of this phenomenon. Research in credit scoring only knows what it occurs at the beginning (application scoring) and at some point during the loan payment schedule (behavioral scoring).

Consequently, this research endeavors to answer the following research questions:

\begin{enumerate}
    \item We know that borrowers' repayment history increases creditworthiness assessment performance. At what point does this information become meaningful? How long do we need to observe borrowers' repayment history?
    \item We know that social interaction data adds higher value in application scoring when the behavioral information is scarce. How long is it convenient to use these sources of information?
    \item What insights and value to credit risk management are obtained for studying this phenomenon?
\end{enumerate}

To this end, we have analyzed a massive multi-source credit dataset containing borrower information and social interaction data in the form of graphs. Then, we carried out an experiment where we observed individuals and companies from the moment they obtained their first loan their subsequent credit history repayment behavior for the next 24 months. The analysis of the results was carried out in considering credit history, repayment behavior, and alternative data and their impact on the creditworthiness assessment perfomance.

This work contributes to the growing research on credit scoring and the use of social network data. We challenge the current division of the credit risk management process through our analysis by investigating what happens between application scoring and behavioral scoring. Focusing the analysis on the borrower rather than the business process lets us discover how the credit scoring models' performance varies as the borrower begins to have a credit history. Additionally, we analyzed contribution repayment behavior features and social network features' contribution and how their value decreases in the presence of behavioral attributes.

Furthermore, our dataset is novel because it includes information for individuals and companies from the moment they obtain their first loan and their subsequent credit history repayment behavior, together with social network data to characterize them. In this way, it handles the low availability of data for behavioral models research stated by \citet{Kennedy2013} and \citet{Goh2019}. It allows us to carry out the first study, as far as we know, on credit assessment performance dynamics.

This paper is structured as follows. In the next section, the literature review of credit scoring in application and behavioral scoring models is presented. The proposed methodology is presented in Section \ref{sec:Methodology}. In Section \ref{sec:ExpDesign}, the experimental design and datasets are described.  Section \ref{sec:Results} is on results and their implications. The last section includes the conclusions, research findings, and future work.

\section{Background}\label{sec:relatedWork}

Credit scoring models enable and support credit risk management in financial institutions. For more than half a century, they have been part of the decisions throughout the credit risk management cycle  \citep{Thomas2017}. Decisions on whom to grant a loan to, portfolio management, preventive collection actions, and even pricing are not conceived today without the support of credit scoring models \citep{Bundi2016, Anderson2022}. Academics and practitioners have developed different credit scoring tools to address the different decisions at each credit risk management cycle stage. application scoring is used to decide whether to grant a loan to an applicant. In contrast, behavioral scoring allows to characterize those borrowers who have already been granted a loan, and it is used mainly for portfolio management. Finally, collection scoring allows optimizing the collection and recoveries policies and strategies \citep{Paleologo2010}.

The Application scoring models are used to decide whether to grant a loan or not. In this way, they are understood as the gateway to the lender institution and the financial system. Its correct implementation and usage allow the implementation of the risk appetite policies and define the applicant universe with whom to operate. Hence the importance of having models that allow to correctly quantify the borrower risk level and predict with high certainty whether the applicant will default or not. One of the most used approaches to enhance the creditworthiness assessment performance is to improve the modeling techniques, from the traditional logistic regression to other techniques such as support vector machines \citep{Huang2007}, bayesian models \citep{Kao2021}, genetic algorithms \citep{Kozeny2015}, ensemble classifiers \citep{Garcia2019, Radovic2021, Moscato2021}, and deep learning models \citep{West2000, Gunnarsson2021} and deep belief networks \citep{Luo2017, Gunnarsson2021}. While improving the performance is not restricted to an application scoring problem, most studies have been made on this particular problem. Another approach to enhance the creditworthiness assessment is to include alternative data sources. More and better data leads to better decisions, and in the case of application scoring, the researchers have analyzed the contribution of alternative data sources such as satellite and geospatial data \citep{Simumba2021},  psychometric data \citep{Shoham2004, Rabecca2018, Djeundjie2021}, mobile phone data and communication networks \citep{Oskarsdottir2018, Oskarsdottir2019}, network data \citep{Wei2015, Masyutin2015, Freedman2017, Cnudde2019, Giudici2020}, and written risk assessments \citep{Stevenson2020}. These studies have in common that most of the increase in creditworthiness assessment performance occurs when applicant traditional information is scarce or non-available.

Behavioral scoring is used mainly for portfolio risk management, understanding what happens after the credit has been granted. These models assess actual customers' creditworthiness and enable lenders to take preventive actions with borrowers with a high default likelihood. Unlike the application models, there are no extended research about behavioral scoring \citep{Liu2001, Goh2019}, \citet{Kennedy2013} suggest that the reason for scarce research on behavioral scoring is the large volume of data required and the difficulty of accessing the data.
However, the research lines to increase the performance of these models are the same as in application scoring. \citet{Septian2020} investigated the value of social network data to predict bankruptcy, and \citet{Letizia2019} included a corporate payments network to assess an internal rating. Our previous research \citep{munoz2021} shows that social network data generate a much more significant performance enhancement in application scoring than behavioral scoring, considering the same population and features. Moreover, this result is consistent when the credit scoring model is applied to both individuals and companies, personal scoring and business scoring, respectively.

Among other studies that address the behavioral scoring problem, the work of \citet{Hsieh2004} developed a behavioral scoring to manage credit card customers through an RFM-based segmentation model and then defined marketing strategies for each group using association rules. \citet{Biron2013} studied what happens when infringing the logistic regression independence assumptions in behavioral scoring. \citet{Kao2021} concluded that increasing the APR (annual percentage rate)  significantly increases the probability of default using the credit cardholder database from Taiwan.

The Behavioral scoring models include additional information: repayment behavior, and credit history \citep{Thomas2000}; this data is not necessarily available in application scoring. The repayment behavior and banking data preceding the observation point is defined as the performance period \citep{Thomas2000}. The behavior during this period is added as features - for instance, number of missed payments and average balance.
There is still no consensus on the performance period length. \citet{Thomas2000, Liu2001} gives 12 months as an example and \citet{Djeundjie2021} stated \say{Behavioural scoring models are applied to accounts that have been open for a sufficient period} (p.2), but without details of the sufficient period. \cite{Kennedy2013} analyzed the performance period length comparing windows of 6-months, 12-months, and 18-months. Concluding the best performance is achieved using the 12-months performance windows but limited to shorter outcomes windows; in longer outcome windows, it is harder to find optimum performance windows. Therefore, the performance window selection only affects the short-term creditworthiness assessment.

Despite all of this, we do not fully understand how long the performance period should be and how the discrimination power varies as more knowledge on repayment behavior becomes available. Additionally, the role and contribution of network data in the shift between application and behavioral scoring remain an open question.

\section{Methodology}\label{sec:Methodology}
We use an approach based on machine learning classification models to analyze the creditworthiness assessment performance's dynamics and how it is affected by credit history, repayment history, and social network features.

We analyze the performance dynamics by studying multiple machine learning static models; this is achieved by creating twelve credit scoring datasets varying the number of months since the borrower obtained his first credit. The first dataset only includes borrowers in the first month after granting. The first dataset corresponds to a credit scoring application problem; the second dataset includes the same group of borrowers, but they are observed two months after granting. It continues until the last dataset contains the borrowers' information 12 months after granting.
It is necessary to point out that although the models to be trained are independent, the training data is not. In this way, it is possible to obtain insights into performance dynamics through multiple static models.
The borrowers, individuals, and companies in the study remain invariant during the 12 months of analysis. As the months go by after the loan is granted, borrowers can be excluded from the training dataset due to default or full credit payment. Borrowers are described using the same feature set, regardless of the analysis time. However, these features reflect diverse behaviors as the borrower repays the loan or shows signs of credit deterioration.

This study uses gradient boosted trees because they have consistently shown state-of-the-art performance in different problems \citep{Friedman2001, Tianqi2016, munoz2021}. Additionally, in order to quantify the performance, we use the area under the receiver operating characteristic curve \citep[AUC]{Bradley1997}  and the Kolmogorov-Smirnov statistic \citep[KS]{Hodges1958} as performance measures.

The dataset used to train these models is built by applying the feature engineering process described in section \ref{sec:featEng}. To obtain insights based on the generalization capability of these models and avoid overfitted models, we split the dataset into two parts. The first dataset that contains 30\% of the original dataset is used for feature selection and hyper-parameter optimization. The remaining dataset is used to train the final models, including the features and parameters previously obtained. The results and conclusions are based on the average of a 10-fold cross-validation. The comparison between models is made using a t-test, according to the recommendations introduced in \citet[Ch.\ 12]{Flach2012}.

The feature selection and hyper-parameter optimization start discarding those features with low or almost null predictive power; for this, we calculate the KS and AUC in a univariate way and discard those attributes with a $KS <= 0.01$ or an $AUC <= 0.53$ and then apply a method to drop out highly correlated features. This method begins by selecting the feature with the highest predictive power and then discards those whose absolute value of the correlation is greater than a parameter $\rho = 0.7$; this process is repeated until all the target features are evaluated. We use this method twice, first considering attributes that belong only to the feature sets defined in \ref{sec:featEng}; thus, we ensure a representative mix of attributes for each dimension analyzed. Then, we apply it again by considering all the remaining features. Finally, to find the best hyper-parameters, we apply an exhaustive search over specific parameters, the number of boosted trees to fit and their learning rate, and the minimum data needed in leaves.

Our methodology's second and last stage consists of using the remaining 70\% of the original dataset to train models using N-Fold Cross-Validation together with the previously obtained hyper-parameters and features.

\section{Experimental Setup}\label{sec:ExpDesign}
\subsection{Dataset}\label{sec:datadescr}

We have used data provided by a Latin American bank. The information was anonymized to guard customer confidentiality and not compromise any customer's identification and relationships; there is no possibility that this study can leak private data.
The data includes 97,044 customers who obtained their first loan between January 2018 and December 2018 and contains their repayment behavior until December 2019. Our approach involves building a machine learning classification model. For this, each borrower is labeled as a defaulter or non-defaulter. A borrower to be considered a defaulter must be more than 90 days past due within the next 12 months from the moment it sampled; if during this outcome period the borrower is not more than 90 days past due, it is considered non-defaulter. Those borrowers with arrears of more than 90 days at the time of observing them are not considered in the analysis.

The data sources used to construct this dataset have already been used to develop credit scoring models \citep{munoz2021}. Application and behavioral scoring models were trained. In both cases, the borrower information and social interaction data proved to be a statistically significant contribution to increasing creditworthiness assessment performance. Additionally, in this study, we are interested in analyzing credit scoring models according to the type of borrower. We will refer to personal credit scoring when borrowers are individuals and business credit scoring when they are companies. This classification is complementary to the one previously defined. In this way, it is possible to assess individuals' creditworthiness (personal credit scoring) at the application or after that, i.e., application or behavioral scoring, and the same applies to companies.

Table \ref{table:datasetdesc}  describes the available information; this is grouped into business credit scoring data and personal credit scoring data to distinguish between companies and individuals. It shows the borrower number in the first observation month and the features number corresponds to those provided by the financial institution and those built for this research. Each of the 12 datasets is characterized based on the same attributes and contains the same borrowers observed $i$ months ($i = 1, \ldots, 12$) after their loans were granted.

\begin{table}[ht]
\footnotesize
\centering
\caption{Dataset description. Borrowers correspond to the total number of individuals and companies that are part of our analysis, which will be observed from the moment they obtain a loan until 12 months later.}
\label{table:datasetdesc}
\resizebox{0.5\textwidth}{!}{
\begin{tabular}{|p{4cm}|c c|}
\hline
{\centering Model} & {\centering Borrowers} & {\centering Features} \\ \hline
Business Credit Score & 20,835 & 585 \\
 Personal Credit Score & 76,209& 936\\ \cline{1-3}
\end{tabular}}
\end{table}

An overview of the construction of the datasets can be seen in Figure  \ref{fig:expSetup}. The timeline shows how many months have passed since the credit was granted; in this way, $dataset_i$ contains all the borrowers after $i$ months having obtained credit. During the following 12 months from the observation month $i$, their payment behavior is analyzed to label the borrower as a defaulter or non-defaulter if they have exceeded 90 days in arrears during this observation period.

\begin{figure}[ht]
\centering
\resizebox{0.80\textwidth}{!}{%
\input{Fig_exp_setup}}
\caption{Dataset construction}
\label{fig:expSetup}
\end{figure}

Figure \ref{fig:datasetdesc} shows the borrower's number and the default rate for each of the 12 datasets, grouped by business scoring and personal scoring.  Both charts start with borrowers at the moment of application, 20,835 and 76,209 borrowers for business and personal scoring, respectively. These borrowers are the ones that are part of the 12 dataset. The observations number decreases for two reasons, the default or the total payment of the loan. These datasets are mutually dependent since they contain information on the same borrowers but with diverse progress in repaying their loans. This aspect allows us to gain insights into performance dynamics from independently trained models.

\begin{figure}[ht]
  \centering
  \noindent
  \resizebox{1\textwidth}{!}{
  \begin{tabular}{cc}
    \subfigure[Business Scoring]{\includegraphics[width=0.5\linewidth]{ResultsAppBeh02/PJ_Stats.png}}&
    \subfigure[Personal Scoring ]{\includegraphics[width=0.5\linewidth]{ResultsAppBeh02/PN_Stats.png}}
  \end{tabular}}
    \caption{Dataset Statistics. The X-axis displays the number of months elapsed since the loan granting. The left Y-axis shows the observation samples, and the right Y-axis the default rate.}
    \label{fig:datasetdesc}
\end{figure}

\subsubsection{Traditional data sources: Borrower Data}\label{sec:nodedata}
To describe the borrowers, we have a massive dataset with the financial information of 7.65 million people and almost a quarter-million companies between January 2018 and March 2020. The financial information provided in this dataset corresponds to the monthly debt decomposition by type and by days past due grouped into buckets. This particular dataset contains all of the study subjects described in Table \ref{table:datasetdesc}.

\subsubsection{Alternative data sources: Social Interaction Data}\label{sec:networkdata}

This study will characterize the companies with network data information originating from their economic and social interactions. The network used for this purpose is composed of Transactional services, the Enterprise's ownership, and the Employment relationship. This network tries to build an ecosystem where companies, business owners, and employees interact. We will call this network \textbf{EOWNet}. On the other hand, people will be mainly characterized by combining marriages, parents, and children. We will call this network \textbf{FamilyNet}. The EownNet is also used in the personal scoring problem since many of these borrowers are part of the EOWNet. However, due to the partial coverage of this dataset, it is expected a limited added value as we observed in \citet{munoz2021}.
The \textbf{EOWNet} is a dynamic network since the interactions that constitute it changes monthly, while the \textbf{FamilyNet} is a static network, fixed at the beginning of the study period.

\subsection{Borrower and social network features}\label{sec:featEng}
The people and companies of this study will be characterized by features created through a feature engineering process. This process combines the node information, the repayment history \ref{sec:nodedata} and the network data \ref{sec:networkdata}. We will classify these features into the following subset:

\begin{itemize}
    \item \textbf{Borrower's Financial Features:} Correspond to borrower features based on the information provided by the financial institution and allows us a characterization of the financial situation of each borrower. Contains the debt decomposition by type (consumer, commercial, and mortgage) and by delinquency situation, and the amount in revolving loans. We will call this feature set $X_{Fin}$. It exclusively represents the borrower's situation at the moment of observation. Additionally, we include another feature set with the borrower's historical information and their repayment history; we will call this feature set $X_{FinHist}$. This borrower's historical features include the mean and SD for each $X_{Fin}$ feature for the last 3 and 6 months. In this way, both $X_{Fin}$ and $X_{FinHist}$ feature set describes the borrower's financial situation. However,  $X_{Fin}$ describes the borrower's credit situation at the observation point, and $X_{FinHist}$ summarizes the historical borrower's financial situation for the last 3 and 6 months.

    \item \textbf{Node Statistics:} This feature set considers each borrower as a node within the network. Therefore, these features correspond to nodes' statistics based on their position and characteristics within the network. For each node in the network, we calculate the Degree, Degree Centrality, number of Triads, PageRank Score, authority and hub score from hits algorithm and an indicator of whether the node is an articulation point.  We will call this feature set $X_{NodeStats}$, and it is part of the features that utilizing alternative data sources provides us.

    \item \textbf{Social Interaction Features:} We utilize the borrower's social interactions to characterize each borrower based on their neighborhood's financial information, i.e.,  the individuals and companies to which they are connected. Formally, we use the borrower ego network to characterize a borrower using social network data corresponding to all the nodes the borrower is connected to. We aggregate the egonet financial features in the $X_{SocInt}$ feature set, calculating the mean and SD for the nodes' features in the borrower's egonet \citet{Nargesian2017, Roa2021eswa}. As we did with borrowers' features, we aggregate historical social interaction features by calculating the mean and SD of the last 3 and 6 months. We will call this additional feature set $X_{SocIntHist}$.

\end{itemize}

\subsection{Experiments} \label{sec:RfeatSetup}
We define a series of experiments to analyze the performance dynamics effect of credit history, repayment history, and social network features. For this, we generate different sets of characteristics detailed in Table \ref{table:featuresGroup}. With each of these feature sets will train twelve models, one for each month that has elapsed since the loan granting.

\begin{table}[ht]
\footnotesize
\centering
\caption{Experiments Setup}
\label{table:featuresGroup}
\begin{tabular}{cl}
\toprule
   Experiment Id  &    Feature Group\\ \midrule
    E1 &  $X = \{ X_{Fin} \}$ \\
    E2 &  $X = \{ X_{Fin} + X_{FinHist}\}$ \\
    E3 & $ X = \{ X_{Fin} + X_{FinHist} + X_{NodeStats} + X_{SocInt} + X_{SocIntHist}\}$ \\ \bottomrule
\end{tabular}
\end{table}

\section{Results and Discussion}\label{sec:Results}

This section presents the results obtained after applying our methodology to study creditworthiness assessment performance's dynamics. First, we present how the borrower's credit history affects the model's performance (Experiment \textbf{E1}). Then we show how this effect changes when the repayment features are incorporated into the analysis (Experiment \textbf{E2}). Finally, we study the effect on the model's performance incorporating the social interaction features, the borrower's credit history and repayment features (Experiment \textbf{E3}).

A further relevant analysis is to understand how much the social interaction features influence the creditworthiness assessment compared to the borrower's features and how this impact varies over time. The results of this analysis are presented in \ref{res:featureImportance}.

\subsection{Experiment \textbf{E1}:  borrower credit history}

The first goal is to understand how the borrower's credit history affects the creditworthiness assessment performance. We will do this by analyzing the behavior of the borrowers' financial features $X_{Fin}$ over time. The Figures \ref{fig:NodeHistory}(a) and \ref{fig:NodeHistory}(b) show this effect for the business scoring problem and the  Figures \ref{fig:NodeHistory}(c) and \ref{fig:NodeHistory}(d) for personal scoring. For each problem, performance is evaluated using the KS and AUC scores.

For each elapsed month since the loan granting, the initial feature set at the beginning of the training is the same. However, as after applying the methodology defined in Section \ref{sec:Methodology}, the final variables may vary between one period and another since we select those that better improve default assessment.
It is observed that the discriminatory power increases as the borrower credit history increases, and this increment is at a decreasing rate. Furthermore, the increase ceases to be consistently statistically significant after six months, so the gains in discriminatory power are relevant in the first months.

\begin{figure}[ht]
  \centering
  \noindent
  \resizebox{0.85\textwidth}{!}{
  \begin{tabular}{cc}
    \subfigure[Business Scoring (KS)]{\includegraphics[width=0.5\linewidth]{ResultsAppBeh02/Single/PJ_AppBeh_AB_KS_V2.png}}&
    \subfigure[Business Scoring (AUC)]{\includegraphics[width=0.5\linewidth]{ResultsAppBeh02/Single/PJ_AppBeh_AB_ROC_V2.png}} \\
        \subfigure[Personal Scoring (KS)]{\includegraphics[width=0.5\linewidth]{ResultsAppBeh02/Single/PN_AppBeh_AB_KS_V2.png}}&
    \subfigure[Personal Scoring (AUC)]{\includegraphics[width=0.5\linewidth]{ResultsAppBeh02/Single/PN_AppBeh_AB_ROC_V2.png}}
  \end{tabular}}
     \caption{Results in terms of KS and AUC scores for the Business Scoring and Personal Scoring Problem. The X-axis displays the number of months elapsed since the loan granting. The blue line shows the creditworthiness assessment performance (left Y-axis) for the Experiment \textbf{E1}, using only $X_{Fin}$: Node Features. The dotted gray line (right Y-axis) shows the percentage increment between consecutive periods; when this increment is statistically significant, the dots are colored red. Otherwise, they are colored gray.}
    \label{fig:NodeHistory}
\end{figure}

In business scoring, we observe a high increase in the second month, higher than 25\% in KS and 10\% in AUC; however, additional credit history produces relatively minor increases. Something different happens in personal scoring, where the increases, although lower, are consistent in the first six months.

These results confirm what academics and practitioners already know: the importance of borrower credit history in the creditworthiness assessment. The value of these results is that they reveal the discrimination power dynamics produced by the availability borrower history. The credit history produces increases in performance at a decreasing rate. After six months, the gains are marginal; this suggests that the transition from an application scoring problem to a behavioral scoring problem, in terms of discriminatory power, occurs in these six months. Our results challenge the imprecise definition of the performance window in behavioral scoring from a sufficient period \citep{Djeundjie2021} or the proposed 12 months \citep{Thomas2000, Liu2001, Kennedy2013} and reduce them to the six-month performance window observed in this study.

A smaller performance window allows reducing the volume of information necessary to research behavioral scoring models, which, as we have seen, is a limitation in this area \citep{Kennedy2013, Goh2019}. Furthermore, these results broaden the adequate target population to apply these models, going from borrowers with 12 months of credit history to needing only six months.
The third point favoring a six-month window performance is that it allows faster defaulter recovery. That is to say, the credit evaluation for borrowers who had negative events in the past and now present a good payment behavior, being this of great help in financial inclusion and access at lower interest rates. Finally, other benefits include more straightforward technological implementations, reducing storage costs, and generating behavior models that quickly capture the portfolio's trends and shifts.

\subsection{Experiment \textbf{E2}:  borrower credit history and repayment features}

Another advantage of the borrower's credit history is to build attributes that reflect the temporal evolution of its characteristics. For this, we create a set of features that summarize the credit information of the last three and the last six months. In the first period of analysis, these attributes do not add value since there is no previous history; however, these attributes make sense as the months' pass since loan granting.

We will call the  repayment history features  $X_{FinHist}$, as mentioned in section \ref{sec:featEng}. Figure \ref{fig:NodeHistoryHist}, shows the comparison between the experiments \textbf{E2} and \textbf{E1} to analyze the effect of incorporating the attributes $X_{FinHist}$ to the creditworthiness assessment problem.

\begin{figure}[ht]
  \centering
  \noindent
  \resizebox{0.85\textwidth}{!}{
  \begin{tabular}{cc}
    \subfigure[Business Scoring (KS)]{\includegraphics[width=0.5\linewidth]{ResultsAppBeh02/Pairs/PJ_AppBeh_AB_A1B1_KS_V2.png}}&
    \subfigure[Business Scoring (AUC)]{\includegraphics[width=0.5\linewidth]{ResultsAppBeh02/Pairs/PJ_AppBeh_AB_A1B1_ROC_V2.png}} \\
        \subfigure[Personal Scoring (KS)]{\includegraphics[width=0.5\linewidth]{ResultsAppBeh02/Pairs/PN_AppBeh_AB_A1B1_KS_V2.png}}&
    \subfigure[Personal Scoring (AUC)]{\includegraphics[width=0.5\linewidth]{ResultsAppBeh02/Pairs/PN_AppBeh_AB_A1B1_ROC_V2.png}}
  \end{tabular}}
   \caption{Results in terms of KS and AUC scores for the Business Scoring and Personal Scoring Problem. The X-axis displays the number of months elapsed since the loan granting. The blue line and the green line show the creditworthiness assessment performance (left Y-axis) for Experiment \textbf{E2} and Experiment \textbf{E1}. The dotted gray line (right Y-axis) shows the percentage increment between  \textbf{E2} and \textbf{E1}; when this increment is statistically significant, the dots are colored red. Otherwise, they are colored gray.
   }
\label{fig:NodeHistoryHist}
\end{figure}

The repayment history features affect creditworthiness assessment performance. Discrimination power, measured as KS, increases as customer history increases and repayment features recollect the borrower's payment behavior. The most meaningful improvements for personal scoring and Business Scoring occur six months after the credit is granted. When performance is measured based on AUC, this relationship is not clear, and the benefits of incorporating repayment history features are observed from the second month. The preceding confirms the importance of incorporating repayment history features.

\subsection{Experiment \textbf{E3}:  borrower credit history, repayment features and social interaction features}

One of the questions that motivate this study is to delve into the value delivered by social interaction features, and the impact on creditworthiness assessment performance as the borrower's credit history and repayment features become available. Figure \ref{fig:NodeHistoryNetwork} shows the comparison of experiments \textbf{E3} and \textbf{E2}; this comparison allows us to analyze the added value of social interaction features as the credit history and repayment behavior becomes available.

\begin{figure}[ht]
  \centering
  \noindent
  \resizebox{0.85\textwidth}{!}{
  \begin{tabular}{cc}
    \subfigure[Business Scoring (KS)]{\includegraphics[width=0.5\linewidth]{ResultsAppBeh02/Pairs/PJ_AppBeh_A1B1_A1B1CD1_KS_V2.png}}&
    \subfigure[Business Scoring (AUC)]{\includegraphics[width=0.5\linewidth]{ResultsAppBeh02/Pairs/PJ_AppBeh_A1B1_A1B1CD1_ROC_V2.png}} \\
        \subfigure[Personal Scoring (KS)]{\includegraphics[width=0.5\linewidth]{ResultsAppBeh02/Pairs/PN_AppBeh_A1B1_A1B1CD1_KS_V2.png}}&
    \subfigure[Personal Scoring (AUC)]{\includegraphics[width=0.5\linewidth]{ResultsAppBeh02/Pairs/PN_AppBeh_A1B1_A1B1CD1_ROC_V2.png}}
  \end{tabular}}
 \caption{Results in terms of KS and AUC scores for the Business Scoring and Personal Scoring Problem. The X-axis displays the number of months elapsed since the loan granting. The blue line and the green line show the creditworthiness assessment performance (left Y-axis) for Experiment \textbf{E3} and Experiment \textbf{E2}. The dotted gray line (right Y-axis) shows the percentage increment between  \textbf{E3} and \textbf{E2}; when this increment is statistically significant, the dots are colored red. Otherwise, they are colored gray.}
\label{fig:NodeHistoryNetwork}
\end{figure}

Incorporating alternative data also allows increasing the creditworthiness assessment performance. The most significant increase is observed in the first month when we face an application scoring problem and the borrower's credit information is not available or it does not exist.

In personal scoring, social interaction features increase the power of discrimination during the 12 months of studies. However, its impact decreases as the borrower's credit history is available, and repayment features express the borrower's payment behavior better.
In business scoring, social interaction features increase by about 8\% and 10\% for KS and AUC, respectively. This significant enhancement in discrimination power occurs at loan applications, a clear sign that the creditworthiness assessment of a firm should not only count on its features. This assessment should consider the behavior and attributes of its owners and analyze its supply chain with customers, suppliers, and employees.

\subsection{Importance of social interaction features over time}\label{res:featureImportance}

This section analyzes the borrower's ego network characteristics, i.e, social interaction features and how this impact varies as credit history and repayment behavior become available. To do this, we consider the relative importance of each attribute in order to predict creditworthiness; this relative importance is grouped into two categories, borrower features ($X_{Fin} + X_{FinHist}$) and the features obtained from social network data, i.e, node statistics and social interaction features ($X_{NodeStats} + X_{SocInt} + X_{SocIntHist}$). For each feature, the importance is calculated as the Shapley Values' average from a subset of the dataset and then aggregated according to the two categories previously defined. The Shapley values were obtained using a tree-based SHAP Explainer \citep{Lundberg2017}.

The Figure \ref{fig:ImpShap}(a) show the feature importance using Shapley values for the business scoring problem and the Figure \ref{fig:ImpShap}(b) for personal scoring.

\begin{figure}[ht]
  \centering
  \noindent
  \resizebox{0.85\textwidth}{!}{
  \begin{tabular}{c}
    \subfigure[Business Credit Scoring]{\includegraphics[width=0.95\linewidth]{ResultsAppBeh02/PJ_AppBeh_A1B1CD1_SHAP_V3.png}}\\
    \subfigure[Personal Credit Scoring ]{\includegraphics[width=0.95\linewidth]{ResultsAppBeh02/PN_AppBeh_A1B1CD1_SHAP_V3.png}}
  \end{tabular}}
\caption{Feature Importance Analysis using Shapley Values. Figure (a) presents the Business Scoring problem and Figure (b) Personal Scoring Problem, in both using the Experiment \textbf{E3} feature set. The features are grouped into two categories, the borrower's features ($X_{Fin} + X_{FinHist}$), and the social interaction features ($X_{NodeStats} + X_{SocInt} + X_{SocIntHist}$). The X-axis displays the number of months elapsed since the loan granting. The Y-axis shows the relative feature importance. The boxplots show the feature importance in the 10-fold cross-validation, and the red line is a LOWESS regression fitted using these results.}
\label{fig:ImpShap}
\end{figure}

The importance of network features in business scoring is 63.8\% in the first month, the same month where this information generates its maximum discrimination power enhancement. Something similar happens in personal scoring; however, network information is less important, and so is its increase in discrimination power.

Figure \ref{fig:ImpShap}(a) shows the importance of social interaction features in the creditworthiness assessment; when a company is applying for a loan, this alternative data contributes the most to the credit evaluation. Its value decreases as the company's information become available during the following months; despite this decrease, the importance of social interaction features stabilizes around 40\% from the first six months. This result confirms what is already known by practitioners. The credit evaluation of a firm, especially in small and medium companies, must consider its owners and the business ecosystem where the company interacts.

On the other hand, in personal scoring, the importance of social interaction features diminishes almost linearly as time passes (See Figure \ref{fig:ImpShap}(b)). The most significant enhancement persists at application, but unlike business scoring, the importance of these attributes is considerably smaller, as is their increase in discrimination power. Parental relationships and marriages do not have the same impact on creditworthiness assessment as transactional and economic relationships on business scoring. Despite this, social interaction features allow increasing the power of discrimination. They are fundamental support in the financial inclusion of those people whom traditional credit scoring models cannot evaluate since they do not have a credit history.

An interesting relationship to analyze is the one presented in Figure \ref{fig:ImpTrend}. This figure shows on the same scale the increase in discrimination power measured in KS and AUC and the importance of social interaction features. A high correlation is observed between the increase in discrimination power and the importance of social interaction features in both cases. In business scoring, this correlation is almost perfect during the first six months of the study, and in personal scoring, a strong pseudo-linear correlation is observed. This relationship shows that the contribution of the social interaction features in the creditworthiness assessment is directly translated into an increase in discrimination power.

\begin{figure}[ht]
  \centering
  \noindent
  \resizebox{1\textwidth}{!}{
{\includegraphics[width=0.7\linewidth]{ResultsAppBeh02/TendenciasImportancia.png}}
}
\caption{Feature importance and predictive power relationship. (Left) Business Scoring problem; (right) Personal Scoring Problem.
in both using the Experiment \textbf{E3} feature set. The blue and green lines show the relative increase between \textbf{E3} and \textbf{E2} experiments for the KS and the AUC. The red line is the Social Interaction features' importance in experiment \textbf{E3}. A \textit{MinMaxScaler} was applied to all series to limit the results between 0 and 1. The X-axis corresponds to the months elapsed after the first loan granting. The Y-axis shows the relative increment.}
\label{fig:ImpTrend}
\end{figure}

\section{Conclusions}\label{sec:Conclusions}

This study analyzes how credit history, loan repayment features, and social network data influence the performance of credit scoring models. We use traditional financial data and graph data originating from borrowers' economic and social interactions. Additionally, our novel dataset allows us to analyze all the financial behavior of individuals and companies from the moment they obtain their first loan until 24 months after it. Furthermore, we analyze the performance dynamics based on the results of multiple independent creditworthiness assessment models trained with time-dependent datasets. These models are trained with features representing the borrower's credit history, repayment behavior, and social interactions. The performance of these models is measured in terms of AUC and KS; the feature importance is quantified using Shapley values.

Our findings showed that as more borrowers' credit history is available, creditworthiness assessment performance increases at a decreasing rate. This effect is observed up to six months from the loan granting, when it stabilizes. This finding is meaningful since it reduces the temporal extent of the datasets necessary to train and research behavioral credit scoring models. Also, it increases the population that can be part of these models from the necessary 12 months to only six months. Furthermore, it enhances financial inclusion and leverages second chance banking allowing those borrowers with good credit behavior but with a negative credit history to have a briefer reintegration into the financial system.
An additional noteworthy finding is that the features that summarize the borrower's repayment behavior, the repayment history features $X_{FinHist}$, enhance the creditworthiness assessment performance, especially after the first six months, and consequently, they increase performance when the contribution of the credit history stabilizes.
Finally, The social interaction features allows higher performance and its most significant added value when the borrower is applying for the loan. In personal scoring, this effect decreases almost entirely as the customer's history is available. In business scoring, the increment in discrimination power by incorporating social network features remains stable, at least during the first year.

The results obtained allow us to analyze the dynamics of creditworthiness assessment performance and how it is influenced by the borrower's credit history, the repayment behavior, and the social interaction features. These results are important since it proposes a six-month performance window, reducing it from the current recommendations of 12 months. In addition, they show us how the importance and discrimination power enhancement of social interaction features changes over time. Both insights allow us to improve credit risk management by establishing when and how long to use social network data; similar conclusions are drawn about the performance windows and the contribution of repayment features.

Our study suggests numerous lines of investigation. First, we would like to extend our research period; our 24-month dataset only allows us to have insights from the first 12 months of the borrowers' behavior. Based on our results, it is feasible that the impact of social interaction networks stabilizes after 12 months in the business scoring problem. A more extended observation period would also allow us to study mortgages that have a slower evolution than consumer and commercial credits. Secondly, we would like to understand what happens in other domains, either using other types of networks or studying microcredits or peer-to-peer lending. Finally, in this work, we study the creditworthiness assessment performance dynamics through multiple independent models trained with time-dependent datasets; we would like to design an framework that inherently handles time dependency.

\section*{Acknowledgments}

This work would not have been accomplished without the financial support of CONICYT-PFCHA / DOCTORADO BECAS CHILE / 2019-21190345. The second author acknowledges the support of the Natural Sciences and Engineering Research Council of Canada (NSERC) [Discovery Grant RGPIN-2020-07114]. This research was undertaken, in part, thanks to funding from the Canada Research Chairs program.
The last author thanks the partial support of  FEDER funds through the MINECO project TIN2017-85827-P and the European Union's Horizon 2020 research and innovation program under the Marie Sklodowska-Curie grant agreement No 777720.

\bibliographystyle{apalike}

redpajama_set_name: RedPajamaArXiv


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The Big Mo. No, I'm not talking about Nigel Mansell. Or Keke Rosberg for that matter. Or even Harald Ertl (Google him. Really, you won't regret it).
I'm talking about the intangible yet large-scale sense of momentum that can be established, whether it be in social shifts, politics, sport or anything else. And once it is established it can be very hard to halt let alone reverse. In F1 right now, Keke's son Nico one way or another seems to have established it.
And this weekend around the Red Bull Ring in Austria it continued. It was by no means a classic weekend for Nico; a messy qualifying (one way or another) meant that he started a mere P3. In the race he didn't look much of a threat to the Williams ahead in the opening stint, and when he got ahead he didn't ever look like running away. He also came close to giving Williams the lead right back at mid-distance by running off the track at turn one. But he remained resolute both then and throughout - a skill that he's displaying in abundance this campaign, far from the former 'Britney' moniker - and most importantly by the end he remained in the lead. Oops he did it again. And now his championship lead over his team mate and sole rival Lewis Hamilton is an ever-more vast 29 points. Overturning that already looks like a big job.
To underline this point, so long as Nico stays out of trouble (both personally and mechanically) and Merc continue to bag one-twos even if Nico follows his team mate home everywhere Lewis still wouldn't lead the table until after our autumnal visit to the Royal Park in Monza.
It was a race that confounded the expectations in advance of many. The much anticipated Williams tyre woes didn't really manifest themselves, and the two with Massa ahead led the pack in fine style for the first stint. It was just like old times. However the Grove pit wall seemed oddly slow to react to Rosberg pitting first time around - word had it that Williams was instead determined to 'run its own race'. Whatever was the case though the top four of two Williams then two Mercs who'd run in close company for the early part was close to reversed by the opening stops, with Nico clearing both FW36s and Lewis Hamilton managing to dive down the inside of Massa at turn two from what seemed several miles back just after the pole man had emerged from his own stop, to claim P3 behind Valtteri Bottas.
Ah Lewis, who lest we forget started down in P9. But he got himself into the front-running mix almost before you could blink, with immense bravery and aggression on lap one. He was P5 in advance even of reaching turn two, and then he gobbled up Fernando Alonso at the fast turn eight before the opening tour was out. This didn't confound expectations as such, but perhaps even the greatest Lewis acolyte will have been taken aback by the double-quick nature of it all.
Lewis managed to clear Bottas finally at the second stops (both of his pit halts were tardier than Nico's - further grist to the conspiracy theory mill apparently but both as outlined by Mercedes explainable) and left him with Nico in sight and a clear run at him. Both had to nurse brakes, but otherwise it was gloves off. These, combined with the fact that yet another seven-point net loss wasn't much good to Lewis, meant most donned their tin hats. But Nico deployed some preventative measures, managing to just about peg the gap that his team mate had to make up to him. By the end it was fairly close, but Nico still kept his foe out of striking distance.
While Nico once again was ecstatic: 'It wasn't the easiest of races, trying to manage certain things which were a bit on the limit on the car, but I had a very fast car again, so it was fantastic to win today'.
There's a long way to go of course, and things can change very quickly in this game. But do you ever get the impression that in this 2014 campaign that Nico is the one with the Big Mo?
Another way in which expectations were confounded today was that, despite this being an ever-so familiar one two for Merc, and that some got a bit overexcited by the fact that the team's first non-win of the season in Montreal was followed by its first non-pole in Austria, still today was by far the closest the rest have got to the Mercs without the crippling mechanical woes experienced in Canada. By the end Valterri Bottas completed the podium and did so but eight seconds adrift of Rosberg, without safety cars or anything else. As Martin Brundle noted this equates to around a tenth per lap only. Perhaps with more of a toe-to-toe strategy with the Mercs he'd have been even closer.
And not only did this result in Williams' first podium finish of the year, it also marked the first F1 podium ever for Bottas. Somehow both marks seem a little overdue. Massa meanwhile followed him in nine seconds later in fourth.
Next up was the astonishing Fernando Alonso, who as usual pushed his car as fast as it would go for the entire distance, and clung to the top four's coattails as a consequence. For much of the way it looked like he may even get close enough to seize the scalp of Massa, though actually passing the swift-in-a-straight-line Williams would have been a challenge, and in the end even he had to give best. But as if to emphasise what Alonso personally is delivering to the Scuderia right now Kimi Raikkonen had another subdued and frustrated run, finishing a distant tenth.
While it was anticipated that Red Bull Ring wouldn't suit the Red Bull, not even Cassandra herself would have predicted the extent of its struggles in actuality. Of the four Red Bull owned cars none were much of a factor and worse three of them conked out before the end - again including Sebastian Vettel who lost a lap early and then parked it partway through due to mechanical gremlins. Only Daniel Ricciardo made the finish, but running wide at the first corner meant he was swimming against the tide, and P8 was all he could do. He at least had the satisfaction of a fine ambush of Nico Hulkenberg on the final lap.
Nico Rosberg is one with more cause for satisfaction than most right now however. Probably even Nico would admit while being fully frank that he is shaded by Lewis when it comes to sheer pace and bravery (though the gaps aren't quite as wide on these as some foresaw). But right now he's added all up of his own attributes to total a clear drivers' championship lead. Somehow, and quickly, Lewis has to halt, and reverse, Nico's Big Mo.


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home Movies & TV Shows 'Avengers: Endgame' Likely To Shatter The Opening Weekend Box Office Record
'Avengers: Endgame' Likely To Shatter The Opening Weekend Box Office Record
Published Sun Mar 03 2019 By Andrew
A deadly storm is swirling around and is on its way to the cinemas and could destroy everything in its way including the Box Office Collection as we know it.
All hell shall break loose in the MCU's biggest superhero reunion ever as well as the box office collection scene. In fact, the fourth Avengers movie is one of the most awaited movies of all time and Box office analysts are predicting that Avengers: Endgame will gain phenomenal grounds financially and beat all the opening weekend record domestically.
With Avengers: Infinity War hitting theaters last April, it destroyed the box office with a $258 million opening weekend. Now, the opening weekend for Avengers: Endgame is following the same lead and is set to create box office history.
Early estimates maintain that the initial weekend collection of the movie will stand at the whopping $280 million at the least.
Avengers: Endgame is speculated to smash box office collection
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Exhibitor Relations senior box-office analyst Jeff Bock told Business Insider,
"Is $300M even possible?"
He added,
"That's what a lot of people are wondering and we may find out, as 'Endgame' looks headed towards a mind-boggling $280M debut."
According to Robbins, the superhero movie is garnering a consistently higher level of Twitter and Facebook activity in comparison to its predecessor.
Directed by Joe and Anthony Russo, Avengers: Endgame holds the astounding acting contributions of talents like Robert Downey Jr., Josh Brolin, Mark Ruffalo, Tom Hiddleston, Chris Evans, Chris Hemsworth, Jeremy Renner, Chris Pratt, Elizabeth Olsen, Chadwick Boseman, Sebastian Stan, Benedict Cumberbatch, Paul Bettany, Samuel L. Jackson, Cobie Smulders, Benedict Wong, Zoe Saldana, Karen Gillan, Vin Diesel, Dave Bautista, Pom Klementieff, Scarlett Johansson, Tom Holland and Anthony Mackie. The film will premiere across theatre on April 26.
Recommended: Marvel is Releasing Captain Marvel Too Close To Avengers: Endgame
To actually get a gist of how impressive that speculated number is, the Marvel Cinematic Universe's previous 2018 venture Ant-Man and the Wasp ended grossing $216.6 million in the domestic market. To add up, the fourth Avengers movie is expected to make $64 million more in one weekend than Ant-Man and the Wasp made in the entirety of its domestic run.
As a matter of fact, the speculation spiral for the fourth edition of Avengers film started swirling within split seconds of the ending of Infinity War and the MCU may have even more fans by the time Endgame hits theaters.
With the last rendition of the superhero movie ending in a massive cliffhanger, the loyal fans of MCU are sure to be retained to the upcoming movie as well. Moreover, Endgame will also attract new audience members as it is the most talked about movie in today's scene as the fourth Avenger movie is set to release after one-year long anticipation.
On a contradictory note, the only reason that could hold the numbers from spreading its wings to the fullest is speculated to be the fact that the movie might have a runtime of over three hours.
If 'Endgame' shall break all the opening weekend record, it will be in the league to contend with Avenger's Infinity war's gross worldwide collection of a staggering $2.048 billion.

redpajama_set_name: RedPajamaCommonCrawl


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Home > Sports Nutrition >
NOW Krill Oil 1000mg (60 gels)
Everyday Low Price: $55.99
Product Code: NOWKRILOIL
Product Info Supplement Facts Details/FAQ
Neptune Krill Oil (NKO®) is known for its unique fatty acid (FA) profile, which includes EPA and DHA, plus phospholipids. In addition, NKO® naturally has Astaxanthin, a powerful free radical scavenger and immune-supporting carotenoid. Clinical studies have shown that NKO® can help to support joint comfort, healthy blood lipid levels already within normal range, and may help to maintain healthy feminine balance. The Phospholipid-bound forms of EPA and DHA from Krill Oil have also demonstrated exceptionally high bioavailability.
1000 mg – double strength
Phospholipid-Bound Omega-3
Supports joint health
Recommended use: As a dietary supplement, take 1 softgel 1 to 3 times daily with food.
1 softgel
Servings Per Container:
Neptune Krill Oil (NKO®) 1 g (1,000 mg) †
Omega-3 Fatty Acids 230 mg †
Eicosapentaenoic Acid (EPA) 120 mg †
Docosahexaenoic Acid (DHA) 70 mg †
Phospholipids 390 mg †
Esterified Astaxanthin 750 mcg †
* Percent Daily Values are based on 2,000 calorie diet.
Other Ingredients: Softgel Capsule (gelatin, glycerin, enteric coating, water) and Glycerin. Contains shellfish (krill).
Not manufactured with yeast, wheat, gluten, milk, egg, fish or tree nut ingredients. Produced in a GMP facility that processes other ingredients containing these allergens
(how to read a supplement label)
How is NOW's Krill harvested?
Krill is a generic term used to designate a number of deep water marine crustacean species; a form of shellfish. It looks like miniature shrimp, ranging between one and six centimeters in length. These 85 species represent the planet's most abundant animal biomass, found in active zones that may extend over several square kilometers. Our Neptune Krill Oil (NKO®) is a patented material that comes from Neptune Biotech. Neptune sources its krill from the cold waters of the Antarctic Ocean.
Many studies have been undertaken which show that the biomass of Antarctic krill, one of the world's most abundant biomasses, may amount to 400-420 million tonnes (tons). Its annual natural reproduction cycle provides krill to offset natural mortality, feeds predators, provides harvest for animal feed, and supplies food for people.
The management and control of krill harvesting is overseen by the 25-state member Commission for the Conservation of Antarctic Marine Living Resources (CCAMLR), which is the official international organization responsible for krill fishery management. The precautionary catch limits on the krill fishery are under constant review by the Scientific Committee of CCAMLR, which uses an ecosystem approach to management.
The precautionary catch limit set by the CCAMLR is 6.6 million tons for 2010 and is reviewed annually. It was increased from 4.9 million tons in 2005/06 based on the results of recent surveys. Less than 120,000 tons of the annually set total catch limit of 6.6 million tons are harvested by fisheries. By contrast, Antarctic baleen whales are estimated to consume 85 million tons per year. Less than 3% of the precautionary catch limit is actually harvested, and only a tiny percentage of that harvest is used to make krill oil dietary supplements.
Not only is the krill harvest overseen by scientists stationed on each ship who report to an independent regulatory commission, but the amount of krill harvested for human nutrition is only about 2% of the total harvest, with the vast majority going into animal feed.
NOW Foods is a leader in sustainability
Of course, the people at NOW Foods are concerned about the environmental impact of the materials utilized to produce our natural products. We have won a number of environmental awards, including the Earth Flag and the Illinois Governor's Sustainability Award. In the case of krill oil, customer concerns were aired and we took those concerns seriously. Our subsequent investigation has produced the information provided here. This gives us confidence that our krill is sourced from well-managed fisheries and does not have a significantly negative impact on the ocean's ecosystem. The fact that CCAMLR, the responsible regulatory authority, has continued to increase its scrutiny and tighten the rules builds confidence in its ability to respond to any changes in ways that maintain environmental protections. In fact, it appears that krill harvesting from well-managed fisheries is one of the most sustainable ways to obtain essential omega-3 fatty acids from our oceans. The unique nutritional profile of krill, with three synergistic components (omega-3 fatty acids, astaxanthin, and phospholipids), makes it an especially bioavailable source of these important nutrients.
NOW L-Arginine 1000mg (120 caps) NOW Vitamin D-3 1000iu (180 gels) ALLMAX Creatine (100 g) Ripped Freak Hybrid Fat Burner (60 caps)
**EXCLUSIVE Price**: $6.97
Everyday Low Price: $6.99
NOW Curcumin 665mg (60 vcaps) ALLMAX D-Aspartic Acid (100 g) NOW Iron 18mg (120 vegcaps) Progressive Complete Calcium - Adult Men (60 caps)


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ExposeFacts
Blinken: AIPAC is Pleased
Several media outlets are reporting that Joe Biden will announce Tuesday that Tony Blinken is his nominee for Secretary of State.
ZAREFAH BAROUD, zarefahbaroud@gmail.com, @ZarefahBaroud
Baroud is with American Muslims for Palestine. She recently wrote the piece "The Leahy Laws: Why Biden's Promise to Israel is Illegal," which states: "Biden and [Kamala] Harris are pledging to break U.S. law for Israel. … Essentially, the [Leahy laws state] that foreign military assistance must be suspended or discontinued if there exists credible information that the recipient foreign security force unit has committed a gross human rights violation. … According to Tony Blinken, Joe Biden's senior advisor, 'He [Biden] would not tie military assistance to Israel to any political decisions that it makes. Period. Full stop. He said it; he's committed to it.'"
RICHARD SILVERSTEIN, richards1052@gmail.com, @richards1052
Silverstein at Tikun Olam just wrote the piece: "Biden Names Blinken Secretary of State, Israel Lobby Pleased," which notes: "During the campaign, Blinken's most notable comments were made in a conference call to pro-Israel leaders hosted by the Democratic Majority for Israel. DMI is a hawkish group which is the Democratic Party version of AIPAC. Though the latter claims it is bipartisan, it's common knowledge that the vast majority of its membership, donors and leaders are Republican. Thus, DMI was manufactured to ensure that Democrats would not stray too far from the pro-Israel party line. The group has regularly acted as an enforcer within the party when candidates espouse positions considered anti-Israel.
"Among those who've been taken to the woodshed are Bernie Sanders, Ilhan Omar and Alexandria Ocasio-Cortez. The group sunk over $1 million in ads in the Iowa primary attacking Bernie Sanders … the ads didn't mention Sanders' views on Israel."
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1. A lightweight TruSkin moisturizing cream packed with a healthy punch of vitamin c, which is here to naturally run the dark spots and acne scars (grrr) right off your face.
This cruelty-free brightening blend contains 15% vitamin c, vitamin b, organic jojoba oil, and green tea!
2. A cruelty-free Pacifica face wash made from 100% vegan ingredients: coconut water, sea algae complex, and antioxidants, y'all! Plus, it smells like the beach and will take off allllll your makeup.
3. A Radha rosehip oil currently negotiating a movie deal with Marvel — this little bottle of magic is a superhero worthy of its own franchise. This 100% pure rosehip oil moisturizes your skin without making it greasy, evens out scarring, and helps keep the evil pimple monsters away.
4. A coconut + coffee Yes To cleansing stick you'll want to add to your AM routine. This stuff will ~wake up~ your skin like a cup of caffeine and is super hydrating, meaning your foundation will GLIDE on, I tell ya!
This gentle scrub and cleanser stick efficiently gets rid of impurities, while the coconut, avocado, and meadow foam seed oils work to leave your complexion feeling hydrated and supple.
5. A vegan cold-pressed oil that is truly a skincare jack-of-all-trades. This stuff softens skin, helps heal acne, aids in ridding scarring and stretch marks, and provides a large dose of MOISTURE.
This blend of extra virgin olive, almond oil, grape seed oil, lavender essential oil, and vitamin e can also be used to moisturize your cuticles, strengthen your nails, and can be used on dry, itchy scalps.
6. A cucumber Mario Badescu toner lotion with a non-drying, non-irritating formula to disinfect your pores, prevent excess shine, and reduce icky bacteria — the kind that causes angry pimple apocalypses.
8. A sea algae Alba Botanica enzyme scrub free of parabens, phthalates, and harsh sulfates. This cruelty-free concoction slathers away dullness with the help of (a gentle) walnut shell powder.
Get it from Amazon for $6.57.
9. A Nivea intensive cream capable of ~duping~ you into thinking it's a much more expensive brand (alas, it is actually affordable). This rich formula melts into your skin and is the perfect overnight treatment for anyone with dry, patchy skin.
Get it from Amazon for $7.12.
10. A coconut sugar Handmade Heroes lip scrub to prep your pout for bold lipstick. This will give you smooth, moisturized lips free of any flakes — and not the delicious, breakfast cereal kind.
This vegan and cruelty-free scrub is formulated with avocado oil, jojoba oil, and sweet almond oil for the ultimate dose of hydration.
11. A gentle La Roche-Posay cream cleanser that easily picks up makeup and impurities...all while maintaining your natural pH balance/not stripping your complexion of moisture. People with dry-to-normal skin, break out the party hats because it's time to REJOICE.
This daily cleanser contains prebiotic thermal water, ceramide-3, niacinamide, and glycerin.
Promising review: "This is very gentle on my sensitive skin. My face feels very clean after I use it without being overly tight or dry. This one is working out really well so far after several months of daily use." —P.
12. A rosewater Mario Badescu facial spray with so many uses, it's hard to keep track of 'em all! This can set and refresh your makeup, give foundation a dewy appearance, add refreshing hydration to dull-looking skin, and help keep breakouts at bay.
This facial spray is packed with aloe, herbs, and rose water to give a glowy, dewy look that will make even a full face of foundation look au naturale.
13. An SPF 25-infused Jack Black lip balm made with beeswax, green tea leaf extract, shea butter, and cocoa seed butter to provide intense, long-lasting moisture to even the most chapped of pouts.
Get it from Amazon for $8 (available in five scents).
14. A chamomile-infused Body Shop makeup remover to get rid of all the mascara that's STILL lurking on your eyelashes — even after double cleansing and toning with passion.
15. A deep pore-cleaning Aztec Secret clay mask with a powerful calcium bentonite clay to karate chop the crap out of cystic acne. If you're trying to keep breakouts at bay, this tub is guaranteed to become your new go-to.
Be sure to dilute this mask with either apple cider vinegar or water for the best results! AND! You can apply it with this golden facial mask brush.
16. A detoxifying Yes To facial cleanser that — thanks to an effective trio of charcoal, tomato extract, and salicylic acid — kicks the derrieres of acne, blackheads, and excess shine.
17. Solaroil to banish the bane of your existence — weak and brittle nails with cuticles that look like they were mauled by an animal. This formula is so effective, I guess you can say it really nails it. Eh?
Just massage this oil into your nails and cuticles every night before going to bed. With daily use, your nails will begin to grow and strengthen (seriously, just read the overwhelmingly positive reviews) and your ripped-up cuticles will begin to heal/not look completely terrible.
18. A vegan Acure brightening scrub made with argan extract and chlorella to gently remove dirt AND restore your skin cells. This will get rid of all the sneaky, remaining makeup that REFUSED to be slowed with your face wipe.
Psst, this cult-favorite product has over 2,000 positive reviews!
19. A lightweight Clinique cleansing balm that transforms from a solid balm to oil to milk. This cleansing balm is to makeup what water is to the Wicked Witch of the West — it'll have your foundation, mascara, and lipstick melt.
Get it from Sephora for $9.50+ (available in two sizes).
Psst, this product is a WINNER! Check out our full review on BuzzFeed Reviews.
20. A Glossier chemical exfoliator designed to transform your skin in just one (!!!) month. In fact, 94% of users with acne-prone skin said their skin was cleaner.
This cruelty-free chemical exfoliator uses several acids (AHA, BHA, and PHA) to unclog pores, fight acne, and do away with dark spots — all while moisturizing, conditioning, and exfoliating your skin. HOORAY!
Check out our full review of the Glossier Solution.
21. A Garnier gel-cream specifically formulated for people with combination complexions. This will give the dry patches on your skin some sweet, sweet relief, alllll while balancing out the excess oil on your t-zone.
This formula (packed with vitamin C and antioxidants) feels super cool when it goes on your skin, and is non-comedogenic so it won't clog your pores.
22. A daily face (and body!) Cerave moisturizing cream that is a miracle product for dryness, redness, and breakouts. This hypoallergenic, noncomedogenic, fragrance-free face cream is made with hyaluronic acid and ceramides to provide nongreasy 24-hour hydration.
23. An oil-free, non-greasy, fast-absorbing Olay eye cream here to reduce puffiness, fine lines, and dreaded dark circles. An impressive blend of vitamins b and e work overnight to make sure of it!
24. A tinted Australian Gold face lotion that contains SPF50, which you should already be WEARING EVERY DARN DAY. This hypoallergenic sunscreen is also oil-free, dye-free, reef-safe, and adds sheer coverage for a flawless complexion.
Get it from Amazon for $8.54.
25. An Origins toner lotion packed with alcohol-free, nutrient-rich ingredients. World, say hello to ermented chaga and coprinus mushrooms — they can do everything from calming cystic acne to fading scarring.
26. A Lancôme eye makeup remover that any smoky eye, waterproof mascara connoisseur should seriously consider investing in. This stuff gets rid of everything.
Get it from Sephora for $15+.
27. Vegan and cruelty-free Acure facial oils, each with a different skin concern in mind — marula oil to correct signs of aging, rosehip to feed and restore cells, and argan to improve texture and tone.
Get it from Amazon for $9+.
28. A silicone massage brush that works to *boost* the effectiveness of your daily cleanser by adding some gentle exfoliation à la BPA-free bristles, which ensure your pores have been stripped of all remaining makeup.
This silicone cleanser dries quickly, meaning it doesn't act as a breeding ground for germs and bacteria (you know, the kind that can cause pimples and other havoc).
29. A daily Vanicream moisturizer ideal for both your face and body! If you've ever struggled with eczema, you need to try this paraben-, formaldehyde-, dye-, and fragrance-free formula ASAP — over 2,000 people swear by it.
Psst, this has over 2,500 positive reviews!
30. A hydrating Summer Fridays cream face mask created to give tired, dull, stressed skin some serious pampering. Serious moisture and serious glow? Pass that marriage certificate because I ~dew~.
31. A red clay First Aid Beauty cleanser that — thanks to an impressive blend of rosemary leaf oil, glycerin, and bisabolol — acts as a facial detox. Consider this a must-have weapon to fight blackheads, enlarged pores, excess oil, and acne.
Promising review: "This face wash is the only one that doesn't break me out or irritate my rosacea-prone skin. Yes, it is a little pricy compared to other face washes, but a little goes a long way. Literally just a pea-sized amount does your whole face. I use this every single day and only need to purchase two tubes a year. I'll keep buying this product for as long as First Aid Beauty makes it!" —Chelsea H.
32. A Proraso shaving cream containing eucalyptus and menthol, which work overtime to tone, purify, and refresh skin. Razor burn? Consider that a thing of the past. ANCIENT HISTORY, I tell ya!
Psst, this shaving cream has over 2,000 (!!!) glowing reviews on Amazon.
33. A splurge-worthy Estée Lauder renewal serum that is basically a bottled version of the Harry Potter "Lumos" spell. This will give your skin glowing, healthy luminosity like a charm.
This serum also reduces the appearance of fine lines and wrinkles, helps even out skin tone, and fights dryness.
Get it from Sephora for $16+ (available in three sizes).
34. Tony Moly sheet masks available in 23 scents, all of which serve a different skin purpose. Want some dewy glow? Go for the lotus scent. Need nutrition and overall TLC? Add the avocado option to your cart.
Get them from Amazon for $3+ (available in 23 scents).
35. A gel-like Glossier cleanser that'll have your waterproof mascara quivering in fear. This gently dissolves all the makeup and grime on your face, no harsh rubbing required.


Fulvic shale is formed over the course of millions of years. As plants grew, died and became preserved in today's deposit areas, their organic content became more and more compressed into the earth. This formed layers of shale that are rich in mineral content and contained the moisture of the preserved, prehistoric plant materials. The deposits continued to compress as mud, sand and rocks were piled on top of the fulvic shale.
Today, these shale deposits have been found in regions that once featured thick, lush forests and plant life. Though this type of plant life may no longer exist there, the fulvic shale remains, still containing the abundance of minerals and trace elements that are vital to a human diet.
In the areas with fulvic shale deposits, the soil is also richer in minerals and trace elements. As opposed to the average of 30 minerals and trace elements found in a typical soil sample, these locations boast over 77 trace minerals and elements which produce extremely nutritious crops for human consumption. This varies greatly from the soil which used to exist on Earth and produced the plants that now are incorporated into fulvic shale deposits. Millions of years ago, all the Earth's soil was rich in minerals and trace elements needed for survival. Today, we can obtain this same type of nutrition through fulvic shale deposits.
Many people wonder how the plant materials in fulvic shale deposits became so rich in minerals and trace elements. In fact, it was photosynthesis that enriched these materials with the nutritious elements we need. Photosynthesis is a biochemical process which converts the sun's energy into a chemical energy. As sunlight is absorbed by a plant, the energy is used to form organic materials like nitrogen, water, oxygen and minerals. The energy is also used to convert metallic minerals to organic molecules which can be used by the body for nutrition.
Once the minerals have been formed through photosynthesis, they travel through the folial membranes to become available for ingestion. Meanwhile, microorganisms in the surrounding soil prepare inorganic minerals to be absorbed by the plant. Once the microorganisms convert the minerals to a lower molecular weight, they can be assimilated into the plant more easily. This creates a plant which is rich in fulvic acid, minerals and trace elements.
These plants eventually decayed and became a part of fulvic shale deposits. Today, we have the technology to use water to extract the fulvic acid from the shale deposits. Using the fulvic colloid resulting from the extraction process, a mineral supplement can be formed that is suitable for human ingestion. These supplements are a rich source of minerals and trace elements which fulfill our nutritional requirements, creating a happier, healthier lifestyle.