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On the use (and misuse) of Gini Coefficients in Credit Scoring

Over years of blogging, one of our most popular ever blog posts was about the Gini coefficient. In this series of posts, we revisit the Gini and dig further into its uses and the ways we see it misused in credit scoring.

What is a GINI?

For lenders around the world, the “Gini Coefficient” is an often heard, sometimes feared, and frequently misunderstood statistical measure. Commonly used to assess things like wealth inequality, Gini Coefficients are also used to evaluate the predictive power of credit scoring models. In other words, a Gini Coefficient can help measure how good a credit score is at predicting who will repay and who will default on a loan: the better a credit score, the better it should be at giving lower scores to riskier applicants, and higher scores to safer applicants.

Though calculating a Gini Coefficient is complex, understanding it is fairly simple:

A Gini Coefficient is merely a scale of predictive power from 0 to 1, and a higher Gini means more predictive power.

Continue reading…


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

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

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.

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On the use (and misuse) of Gini Coefficients in Credit Scoring: Gini and Acceptance Rate

The relationship between Gini Coefficients and Acceptance Rate

One of the most frequent uses of Credit Scores is to decide whether to admit or reject an applicant applying for loan. This is usually called an “Admission score” or “Origination score”. A key decision around this use case is the selection of a score cut-off that will determine a threshold for admission. This cut-off value determines the acceptance rate of the population.

If the score is working well and predictive power is good, the relationship between acceptance rate and default rate will be positive. The higher the acceptance rate, the higher the default rate of the accepted population and vice versa. The direction of this relationship also has two implications: when acceptance rate is higher, the absolute number of bad loans (i.e. non-performing loans) or “bads” will also be higher, and the proportion of these “bads” in respect to the total loans in the accepted population will be higher too.

What does this mean in practical terms?

It means that the predictive power as measured by a Gini coefficient for the exact same score at different levels of acceptance rate for the exact same population will be different. The higher the acceptance rate, the higher the Gini coefficient and vice versa.

Continue reading…


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On the use (and misuse) of Gini Coefficients in Credit Scoring: the Economics of Credit Scoring

Gini Coefficients and the Economics of Credit Scoring

On a global scale, billions of dollars in debt are granted every year using decisions derived from credit scoring systems. Financial institutions critically depend on these quantitative decision to enable accurate risk assessments for their lending business. In this sense, as with any tool that serves a business purpose, the application of credit scoring is not ultimately measured by its statistical properties, but by its impact in business results: how much can Credit Scoring help to increase the benefit and/or to decrease the cost of the lending business.

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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? Continue reading…


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Using psychometrics to quantify personality traits

The LenddoEFL assessment, part 1

At LenddoEFL, we collect various forms of alternative data to help lenders verify identities, analyze credit risk, and better understand an individual. One of our most important tools for financial inclusion is our psychometric assessment. While some people still lack a robust digital footprint, everyone has a psychological profile that can be characterized and used for alternative credit scoring.

In this series of posts, we shed light on the science behind the LenddoEFL psychometric assessment and how we’ve pioneered an approach to measure anyone’s creditworthiness.

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Measuring how people answer questions with metadata

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The LenddoEFL Assessment Part 2

The last post showed how our psychometric content reveals people’s personality traits, but our assessment also captures an abundance of metadata. Metadata is information about how people process the questions and exercises they complete. Here are some examples.

  • How long did an applicant take to answer a question compared to their average response time?

  • How many times did an applicant change their mind and switch their response before submitting their answer?

  • Is the applicant’s information consistent with their written request to the financial institution? (e.g., requested loan amount)

By measuring metadata, LenddoEFL’s approach goes beyond what is possible in traditional credit applications to reveal more information about applicants. Continue reading…