Credit Score

Blog | Turning Gini into Profits

Written by Rodrigo Sanabria, Director Partner Success, Latin America

On a prior post by Carlos del Carpio (“The Economics of Credit Scoring”), we discussed the business considerations to assess the merit of a risk model. In this post, I will address how a good origination model impacts the bottom line of a company’s P&L.

These principles may be adapted to look into other types of models used at later stages of a loan life, but on this post we will only address loan origination.

From a business point of view, an origination model is a tool that helps us aim at the “sweet spot”: where we maximize profits. A simple way to think about it is as a trade-off between the cost of acquisition (per loan disbursed) and cost of defaults (provisions, write-offs): The higher the approval rate, the lower the cost of acquisition, but the number of defaults go up.

How do we go about finding the sweet spot? I’ll try to explain it below.

Figure 1

Figure 1

A good model has a good Gini. A “USEFUL” model creates a steep probability of default (also known as PD) curve – we usually refer to it as a “risk split”.

 

Figure 1 shows the performance of a model based on psychometric information used by an MFI. The Gini (not shown in the graphic) is pretty good (0.28). The risk split is great: the people in the lower 20% of the score ranking are about 9 times more likely to default than those in the top 20%.

 

Knowing the probability of default for a given group, we may set a credit policy. Basically, we need to answer: “what would the default look like given an acceptance rate?”

 

Figure 2

Figure 2

 

We have re-plotted the same data in Figure 2, but now we express the probability of default in accumulated terms. Basically, the graph shows that if we were to accept 80% of this population sample, we would have a 4.5% PD, but if we were to accept 40%, the PD would go down 2 points to 2.5%.

Now, from a business point of view, we still do not have enough information to decide. Do we?


 

Where would the profit be maximized?

The total cost of customer acquisition is mainly fixed. Whatever we spend on marketing and sales to attract this population, will not change if we reject more or fewer applicants. So, the cost per loan disbursed would grow as we reduce the acceptance rate.

Of course, the higher the acceptance rate, the larger the portfolio, and the more interest revenue we get. BUT, the higher the provisions and write-offs. The combination of these 2 variables (cost of acquisition and net interest income) produces an inverted U-shaped curve that uncovers the “sweet spot”

Figure 3

Figure 3

The current credit policy is yielding a profit at 100% acceptance rate (see Figure 3) because the sample being analyzed corresponds to all the customers that were accepted (i.e. we have repayment data about them). So, the portfolio is profitable.

But the sweet spot seems to be shy of 60% acceptance rate. If this FI were to cut down its approval rate to that level, profits would increase by about a third, and its return on portfolio value would almost double. Of course, there are other considerations around market share and capital adequacy that may play a role in such a strategic decision, but the opportunity is clearly uncovered by the model.

 

In my experience, the sweet spot usually lies within 30%-70% acceptance rates, driven by marketing expenditures, interest rates, cost of capital, sales channels, and regulation.

What if the shape of the curve shows a continuous positive growth? The sweet spot is at a 100% acceptance rate! – have we reached risk karma? – Most likely, the answer is no (but almost!).

Figure 4

Figure 4

Most likely, we are leaving money on the table. Some business rule may be filtering people before they are scored. I have experienced this situation while working with lenders. For example, a traditional bank was filtering out all SMEs that had been operating for less than X years. This bias in the population was creating a great portfolio from a PD point of view, but there was clearly an opportunity to include younger businesses. As you can see in Figure 4, the maximum return on the portfolio was achieved at 60% approval rate, but they could increase profits by approving beyond the current acceptance rate. Depending on their cost of capital, it may be a good idea to expand the portfolio by approving more people.

In summary, think of your origination model as a business tool. Don’t stop at looking at Gini to assess a model’s merit. Understand how your profitability would be impacted by changes in your acceptance rate. If the PD curve is steep enough, you may capture quite a lot of value by applying the model to either reduce or increase your acceptance rate.

How banks can benefit from collaborating with FinTechs

By Satoko Omata | 10 July, 2018

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TODAY, customers expect more from their banks – who are slow to deliver new products, services, and experiences as a result of their legacy systems and archaic processes.

However, those that truly want to meet and exceed expectations (and snatch up a bigger share of the market), there are a few lessons they can learn from fintechs.

By partnering with fintechs, banks would have access to new services that help deliver better offerings to customers, at cheaper rates.

At the Wild Digital conference on Wednesday, panelists at a discussion observed that of all the industries, those dealing with money-based investments have been the least changed by technology.

The panel featured Richard Eldridge, Co-founder and CEO of Lenddo EFL; Ashley Koh, Senior Vice President and General Manager of Send, Matchmove; Michele Ferrario, Co-Founder and CEO of StashAway; and Gan Pooi Chan (PC), Country Director GoBear. 

Read full article on Tech Wire Asia.

Blog | Lessons from the field: How we created new group psychometrics to increase financial inclusion in Mexico

While Jonathan takes notes, Gerardo helps an applicant navigate our psychometric assessment on a mobile device. An essential component of our field work was to get direct usability feedback from applicants as they completed new psychometric content.

While Jonathan takes notes, Gerardo helps an applicant navigate our psychometric assessment on a mobile device. An essential component of our field work was to get direct usability feedback from applicants as they completed new psychometric content.

By Jonathan Winkle, Behavioral Sciences R&D Manager, LenddoEFL

An experimental psychologist by training, I am relatively new to the world of financial technology. Since joining LenddoEFL, I have embraced terms like information asymmetry, alternative data credit scoring, and financial inclusion. Yet it was only during a recent trip to the field that I was able to meet the people behind the FinTech jargon we use in our day-to-day, the small business owners whose lives we help improve in our mission to #include1billion.

In April of this year, I traveled with colleagues to Veracruz, Mexico to test new psychometric content for one of the top 3 microfinance institutions (MFI) in the country. Their group loan product extends a line of credit to a collection of business owners, but liability for payments is joint: if one person misses a payment, the group must still make that payment in full. Since many of those applying for these loans lack traditional credit histories, this MFI asked LenddoEFL to develop psychometric exercises that could quickly and reliably assess group traits that predict creditworthiness.  

There are traits that define a strong social group which are nonexistent for individual borrowers. A successful group has strong internal relationships that ensure they will help each other in times of need. A tenacious group can generate creative ideas to solve problems that arise when life presents hardships, as it is wont to do. And a cohesive group exhibits decision making abilities that allow it to act deliberately and with confidence. We designed new psychometric exercises to measure these core traits, and tested them in the field with groups of small business owners applying for loans.

Hiding from the Veracruz heat underneath a family’s palapa, Gerardo leads a collection of applicants through our group psychometric exercises while Jonathan makes observations about their behavior.

Hiding from the Veracruz heat underneath a family’s palapa, Gerardo leads a collection of applicants through our group psychometric exercises while Jonathan makes observations about their behavior.

Measuring interpersonal relationships through social pressure
To measure the strength of a group’s interpersonal relationships, we examined the social pressure that exists among group members. Do individuals feel that they can answer sensitive questions honestly? Or do they feel pressure to conform to the opinions of the group majority? While the group was sitting together in one room, we asked them to raise their hands if they agreed with statements about the trustworthiness, fairness, and helpfulness of their local communities. We then asked individuals to answer these questions privately. The discrepancy between how the questions were answered in each setting could reveal how much social pressure exists, and thus how comfortable group members are being honest with each other. We expect that less social conformity means the group’s interpersonal relationships are stronger, an important factor for predicting whether the group will cover individuals who may miss payments throughout the loan cycle.

Measuring creativity through brainstorming
To measure a group’s creativity, we created a set of generative exercises. For both an easy and a hard problem, we had groups brainstorm as many solutions as they could in 60 seconds. The number of solutions generated was recorded as a creativity metric, and, as predicted, groups generated many fewer ideas for the harder exercise. We were also interested in the group’s dynamic as they performed these tasks. Were they apathetic or engaged? Was there a dominant member of the group? Ultimately, when a loan payment is due and some individuals are short on money, can the group come up with ideas for how to get the extra money? We hope that these generative exercises will shed light on this critical group trait.

Gerardo snags a picture with one of the applicants we met and her business, a stand selling eggs, candy, and other sundries. The small scale of some businesses we encountered, such as the one pictured above, reinforces their need for access to financial products. This woman’s entrepreneurial endeavors are only limited by the capital she can acquire.

Gerardo snags a picture with one of the applicants we met and her business, a stand selling eggs, candy, and other sundries. The small scale of some businesses we encountered, such as the one pictured above, reinforces their need for access to financial products. This woman’s entrepreneurial endeavors are only limited by the capital she can acquire.

Measuring decision making abilities through consensus
To measure a group’s decision making abilities, we created a time-to-consensus task. This exercise asks the group to solve a problem where all members must agree on the answer they provide. While we asked the groups to estimate the population of the state they live in, we actually don’t care how accurate their answer is! What’s more important in this exercise is how the group reaches consensus. Are they indifferent and accept the first estimate suggested? Or do they take their time and argue intensely while deliberating over possible solutions? What kind of strategies did they use to reach their estimate? Importantly, this task provides loan officers with a window into the group dynamic that might not otherwise be seen if the assessment merely collected static information such as sociodemographics and business revenues.

Financial inclusion is the mission of LenddoEFL, but working directly with the people we want to include allowed me to better understand how our assessments must be tailored to their cultures and experiences. The better we can measure group dynamics that predict creditworthiness, the more successfully we can extend financial services to those in need. As we continue to expand our credit scoring offerings across the world, looking past the business jargon we use and maintaining empathy for the humans we touch is essential on our path to #include1billion.

 

Blog | On the use (and misuse) of Gini Coefficients in Credit Scoring: Comparing Ginis

By: Carlos Del Carpio, Director of Risk and Analytics, LenddoEFL

This is part 2 of a series of blog posts about Ginis in Credit Scoring. To see the part 1, follow this link.

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What is an AUC?

AUC stands for “Area Under the (ROC) Curve”. From a statistical perspective, it measures the probability that a good client chosen randomly has a score higher than a bad client chosen randomly. In that sense, AUC is a statistical measure widely used in many industries and fields across academia to compare the predictive power of two or more different statistical classification models over the exact same data sample [1].

How is AUC used in Credit Scoring?

In the particular case of Credit Scoring, AUCs are useful for example in the model development process, when there are several candidate models built over the same training data and they need to be compared. Another typical use is at the time of introducing a new credit score, to compare a challenger against an incumbent score over the same sample of data under a champion challenger framework.

How does AUC relate to Gini Coefficient?

The Gini Coefficient is a direct conversion from AUC through a simple formula: Gini = (AUC x 2) -1. They measure exactly the same. And it is possible to go directly from one measure to the other, back and forth. The only reason to use Gini over AUC is the improvement in the scale’s interpretability: while the scale of a good predicting model AUC goes from 0.5 to 1, the scale in the case of Gini goes from 0 to 1. However, all the properties and restrictions of AUC still translate into Gini Coefficient, and this includes the need to compare two different AUC values over the exact same data sample to make any conclusion about their relative predictive power.

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What does this mean in practical terms?

Any direct comparison of the Gini Coefficients (or AUCs) of two different models over two different data samples will be misleading. For example: If a Bank A has a Credit Score with a Gini Coefficient of 30%, and Bank B has a Credit Score with a Gini Coefficient of 28%, it is not possible to make any conclusion about which is better or which is more predictive because they have been calculated over different data samples without accounting for the difference in absolute number of observations and the difference in proportion of good cases against bad cases. The only direct comparison possible is the one made about two scores side by side, over the exact same data sample.

Bottom-line: To affirm that a certain absolute level of AUC or Gini Coefficient is “good” or “bad” is meaningless. Such affirmation is only possible in relative terms, when comparing two or more different scores over the exact same data sample. Unfortunately this is often not well understood, which leads to the most frequent misuse of AUC and Gini Coefficients, such as direct, un-weighted comparisons of Gini values across different samples, different time periods, different products, different segments and even different financial institutions.

 

[1] Hanley JA, McNeil BJ. The meaning and use of the area under a Receiver Operating Characteristic (ROC) curve. Radiology, 1982, 143, 29-36.

Blog | Raising the Stakes on Psychometric Credit Scoring

An updated and expanded 2nd edition (first edition)

Why read this post?

Learn why high-stakes data is essential for building accurate credit-scoring models.

 

Introduction

Billions of people lack traditional credit histories, but every single person on the planet has attitudes, beliefs, and behaviors that can be used to predict creditworthiness. Quantifying these human traits is the focus of psychometrics, and the alternative data provided by this technique allows LenddoEFL to greatly expand financial inclusion in its mission to #include1billion.

But there is a catch: in order to build models that accurately predict default, applicants need to complete psychometric assessments in pursuit of actual financial products, a so-called “high-stakes” environment. This is because people answer psychometric questions differently when they have a chance to receive a loan (the high stakes) than they would in a hypothetical situation with no incentive (the low stakes).

Despite this fact, psychometric tools are frequently built using low-stakes data. For example, many companies develop psychometric credit scoring tools using volunteers. And many lenders want to validate psychometric credit scoring tools on their clients through back-testing: giving the application to existing clients and comparing scores to their repayment history, again a low-stakes setting.


These approaches are only valid if low-stakes data can be applied to the real world of high-stakes implementation, where access to finance is on the line for applicants. But it turns out that this is not the case. A recent study published by our co-founder Bailey Klinger and academic researchers proved that low-stakes testing has no predictive validity for building and validating psychometric credit scoring models in a real-world, high-stakes situation. The data below shows exactly how applicant responses shift as they move from one environment to another.

 

The Experiment

To test for differences between low- and high-stakes situations, LenddoEFL gathered psychometric data from two sets of micro-enterprise owners in the same east-African country. One group already had their loans (low-stakes) and another group completed a psychometric assessment as a part of the loan application process (high-stakes).

First, the low-stakes data. The figure below shows the frequency distribution for two of the most important ‘Big 5’ personality dimensions for entrepreneurs, Extraversion and Conscientiousness, as well as a leading integrity assessment[i].
 

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You can see that when the stakes are high, people are answering the same questions very differently. The distribution of scores on these three personality measures shifts significantly to the right. When something important is at stake, like being accepted or rejected for a loan, people answer differently.

How do these differences in low- vs. high-stakes data matter for credit scoring?

To see how these differences impact the predictive value of psychometric credit scoring, we can make two models[ii] to predict default: one uses responses from applicants that took the application in low stakes settings, and the other uses responses from applicants that were in high stakes settings. Then we can use a Gini Coefficient—which measures the ability of a model to successfully rank-order applicants’ riskiness and for which a higher coefficient is a metric of success in this—to compare each model’s ability to predict default for the opposing population as well as its own.[iii]

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These results clearly show that there is a significant change in the rank ordering when models built on low-stakes data are applied in high-stakes settings and vice versa.[iv] Importantly, we can see that a psychometric credit-scoring model can indeed achieve reasonable predictive power in a real-world, high-stakes setting. But, that is only when the model was built with high-stakes data.

Think about it like this: when the stakes are high, both less and more risky applicants change their answers. But, less risky applicants change their answers in a different way than riskier applicants. This difference is what is used to predict risk in psychometric credit scoring models: the difference between how low- and high-risk people answer in a high-stakes setting.

This also illustrates why we see that a model built on low-stakes data is ineffective in a real-world high-stakes implementation. In the low-stakes setting, the low- and high-risk people aren’t trying to change their answers, because they aren’t concerned with the outcome of the test. Once the stakes are high, however, this pattern changes.

 

Conclusions

Testing existing loan clients or volunteers has an obvious attraction: speed. That way you don’t have to bother new loan applicants with additional questions, and then wait for them to either repay or default on their loans before you have the data to make or validate a score, an approach that takes years.

Unfortunately, these results clearly show that this shortcut does not work. People change their answers when the stakes are high, so a model built on low-stakes data falls apart when used in the real-world. People answer optional surveys with less attention and less strategy than they do a high-stakes application, and therefore the only strong foundation to a predictive credit-scoring model is real high-stakes application data and subsequent loan repayment.

Consider an analogy: you can’t predict who is a good driver based on how they play a driving video game, where the outcome is not important. Conversely, someone who does well on a real-world driving test may not perform that well on a video game.  Whether it is driving skills or creditworthiness, you must predict the high-stakes context with high-stakes data.

 

TAKEAWAYS:

- Psychometric model accuracy is only guaranteed when you collect data in a high-stakes situation (i.e., a real loan application).

- Despite its speed, back-testing a model on existing clients in a low-stakes setting is risky because it might not tell you anything about how the model will work in a real implementation.

- If you want to buy a model from a provider, the first thing you should verify is what kind of data they used to make their model. Was it from a real-world high-stakes implementation similar to your own?

 


[i] These are indices from widely available commercial psychometrics providers. It is important to note that LenddoEFL no longer uses any of these assessments or dimensions in our assessment, nor any index measures of personality.

[ii] Stepwise logistic regression built on a random 80% of data, and tested on the remaining 20% hold-out sample. An equivalently-sized random sample was used from the other set (high-stakes data for the low-stakes model, and low-stake data for the high-stakes model) to remove any effects of sample size on gini.

[iii] Note that this exercise was restricted to those questions that were present in both the low- and high-stakes testing. It does not represent LenddoEFL’s full set of content and level of predictive power, it is only for purposes of comparing relative predictive power.

[iv] The results also show that using standard personality items, the absolute predictive power is lower in a high-stakes setting compared to a low-stakes setting. This is likely because of the ability to manipulate some items in a high-stakes setting makes them not useful within a high-stakes setting. This lesson has lead LenddoEFL to develop a large set of application content that is more resistant to manipulation and which has much higher predictive power in high-stakes models. This content forms the backbone of the current LenddoEFL psychometric assessment, all of which is built and tested exclusively with high-stakes data and subsequent loan repayment-default rather than back-testing.

 

CardRates.com | How LenddoEFL Uses Data and Personality Analyses to Increase Access to Financial Services in Emerging Economies

Credit is hugely important to people around the globe. You need it to obtain housing and higher education. You need it to start a business. You need it in case of emergencies and other unexpected expenses.

But in emerging economies, credit may not be accessible to many people. According to the World Bank’s 2017 Global Findex, 31% of the world’s population doesn’t have an account with a financial institution or a mobile money provider.

“We still have 1.7 billion people on the planet who don’t even have a basic bank account,” said Amie Vaccaro, Director of Marketing at LenddoEFL. “Only 11% of people around the world borrowed from a formal financial institution in the last year.”

Read full article

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Blog | Score Confidence: Boosting Predictive Power

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Note: This is a new and improved version of a popular post from last year.

Our unique platform has a big reason to live: we provide fast, affordable and convenient financial products for more than 1 billion people worldwide. And there is only one way to accomplish that: by facilitating more actionable, predictive, robust and transparent information to our clients to enable them to make the best possible lending decisions. However, data quality pose the most challenging problem we have faced along this journey as it threatens the predictive power we are delivering to our clients. Therefore, through the years we have developed and perfected a one-of-its-kind way to assess the quality of the data applicants are supplying: Score Confidence.

What exactly is Score Confidence?

Score Confidence is a tailored algorithm that scans and analyzes psychometric information gathered through LenddoEFL's Credit Assessment to generate a Green or Red flag which reflects how confident we are on our score’s ability to represent an applicant’s risk profile:

  • The result will be Green if LenddoEFL is confident in the data quality such that we will generate and share a score based on it.
  • Conversely, the outcome will be Red when LenddoEFL’s confidence in the gathered information has been undermined.

What does Score Confidence measure?

Once the applicant has taken our psychometric assessment, we put the data through our Score Confidence algorithm to find out whether we can be confident in a score generated using this data or not. We will return a Green Score Confidence flag if we believe the score accurately predicts risk, and also be transparent about the reasons behind a Red Score Confidence flag to empower our partners with increased visibility and actionable information.

LenddoEFL's Score Confidence system is comprised of five Confidence Indicators of key behaviors, each generated from a combination of different data sources. If we identify evidence of any of the following behaviours, the assessment will be rated as Red and no risk score will be returned in order to protect our partners:

  • Independence – the assessment has not been completed independently, and LenddoEFL detects attempts to improve one’s responses with either the help of a third party or other supporting resources.
  • Effort – the applicant has not put forth adequate effort and attention in completing the assessment.
  • Completion – the applicant has not responded to a sufficient portion of the timed elements of the assessment.
  • Scoring error – a connection issue or system error occurred and LenddoEFL is unable to generate a score.

What information feeds Score Confidence?

Our data quality indicators are constantly reviewed and updated and, over the years, we have added new and different data sources to our Score Confidence algorithms:

  • Browser and device metadata surrounding the completion of the application
  • User interaction information with LenddoEFL’s behavioural modules
  • Self-reported demographic data

Our Score Confidence system flexibly combines all the available data in order to return a Red or Green status for each application.

How does Score Confidence help our partners make the best possible lending decisions?

To boost the predictive power we can deliver for our clients, LenddoEFL does not share a LenddoEFL score for applicants with a Red Score Confidence flag as we have learned that Red applications tend to have very limited predictive power whereas data coming from Green flagged assessments can effectively sort risk amongst applicants. Therefore, not lending against a score for Red flagged applications boosts the predictive benefit for our clients.

Sina News Taiwan | How to break the credit assessment problem? (如何破解信貸評估難題?)

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Bangladeshi banker and Nobel laureate Muhammad Yunus (Muhammad Yunus) the promotion of microfinance , is the poor through microcredit loans , so there is money to do a small business to support themselves, and thus get rid of poverty. However, due to the time-consuming and laborious credit evaluation of lenders, the large-scale application of microfinance is difficult to achieve once.

Nowadays, mobile banking comes. It can collect data to help people who have little formal financial records in the traditional sense to broaden their services. Labor costs are also greatly reduced. For example, Kenyan mobile telecommunications operator Safaricom and African Commercial Bank jointly launched the M-Shwari business in 2012, which can determine customers’ credit scores based on Safaricom’s user information and the trading history of its M-PESA mobile money business. Loan amount.

In addition to payment data, mobile phones (especially smart phones) can also provide more types of information for credit evaluation by borrowers . For example, a person's geographic location data can reflect whether he has a stable job and fixed residence; shopping records can even reveal whether the borrower is pregnant ; and the richness of information obtained by social media is not Yu.

The fintech start-up company Lenddo EFL also uses the Internet to conduct psychological tests on potential borrowers. The question concerns the concept of money (for example, choosing to pay $10,000 at a time, or $20,000 for six months), where your money is spent. , Evaluation of living communities, etc., to determine the reliability of testers loan repayment. To date, the company has completed more than 7 million credit assessments, helping consumers with a lack of traditional credit records to borrow 2 billion U.S. dollars from 50 financial institutions of varying sizes.



詳全文 如何破解信貸評估難題?-財經新聞-新浪新聞中心 http://news.sina.com.tw/article/20180514/26854022.html

Spore Magazine | Réduire les risques : Des systèmes innovants d’évaluation du crédit pour aider les agriculteurs

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La difficulté d’emprunter, pour de nombreux petits agriculteurs ne disposant ni de garanties ni d’antécédents de crédit, a fait apparaître de nouveaux systèmes pilotes d’évaluation du crédit pour aider les banques à apprécier les risques que présentent réellement les emprunteurs et tirer parti de ce secteur potentiellement lucratif.

L’évaluation psychométrique

Pour augmenter les taux d’acceptation et réduire les délais de traitement des prêts aux agriculteurs, Juhudi Kilimo, prestataire de solutions financières pour les petits agriculteurs d’Afrique de l’Est, teste la méthode d’EFL Global, une entreprise privée qui utilise l’évaluation psychométrique pour créer les profils de risque d’emprunteurs africains, asiatiques, européens et latino-américains. Cette méthode pilote – financée par la Fondation Mastercard – mobilise les représentants de six agences kényanes de Juhudi qui visitent et incitent les demandeurs de prêts à passer des tests psychométriques sur tablette. Ces tests permettent, selon EFL, de définir leur personnalité, y compris leur self-control en matière de dépenses et budgétisation. Sur cette base, une cote de crédit à trois caractères est alors attribuée aux demandeurs. À partir de son évaluation initiale d’environ 6 000 clients réalisée à l’aide de l’outil d’EFL, Juhudi a constaté que 6 % des personnes classées dans le quintile le plus bas avaient au moins une fois des arriérés de remboursement de 60 jours pour un prêt type d’un an, contre 1,5 % dans le quintile le mieux noté.

Read full article.

Financial Express | Credit bureau veteran Darshan Shah joins LenddoEFL as Managing Director

“Having worked across geographies and being well-versed with the problem of credit coverage, I look forward to leveraging my experiences to work on the challenge of financial inclusion in India. The need is massive with less than 45% of Indian adults included in the credit bureau and less than 10% borrowing from a financial institution in the last year, as per the World Bank.” said Darshan Shah.

Read full article in Financial Express

Lodex Blog | The Future of Data-Driven Financial Inclusion Posted by Aisha Hillary-Morgan

"In Australia, millions of people find themselves in a chicken-or-egg-type dilemma when it comes to getting credit. Even though they have steady income, they still can’t access credit because of lack a formal credit history. Yet, most of these consumers carry a smartphone, are online and connected through social networks, leaving behind a digital footprint that can be analyzed to better understand who they are and their attitudes toward credit.

This is why we have teamed up with LenddoEFL, the leading technology platform powering data driven decisions for financial services, to help them create more of a credit story. Your Social Score will use, with your consent, your digital footprint to provide additional insights for borrowers and for lenders to more efficiently make a preliminary assessment." Read the full article

Originally posted by our partner Lodex