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Credit Model
Enhanced credit scoring models use AI and alternative data to assess risk, boosting accuracy while expanding access to the underbanked.
Enhanced credit scoring models use AI and alternative data to assess risk, boosting accuracy while expanding access to the underbanked.
A credit scoring model is a statistical tool that helps assess the financial ability of potential borrowers and predict the likelihood of default, meaning failure to meet the obligations under a credit agreement.
In the lending industry, credit models are used during the credit decisioning process.
Until recently, all banks and other credit institutions used only financial data about applicants provided by credit bureaus in their credit risk models.
Examples of such models include FICO Scores, which take into account payment history, debt amount, length of credit history, new credit applications, and the list of credit products:
Recently, due to its limitations, this approach has ceased to satisfy lenders.
It is impossible to serve a significant portion of the global population with it — namely, 1.4 billion people without a credit history.
Credit organizations are increasingly using enhanced scoring models to drive growth and increase the quality of their loan portfolio.
Enhanced models are models that use a much broader range of data compared to the traditional approach to assessing creditworthiness.
They allow for the creation of a comprehensive profile of the applicant, which includes their financial situation, consumer habits, and even some personality traits.
The main characteristics of enhanced credit score models include:
This includes information about various payments (in addition to loan payments), data from mobile operators and internet providers, as well as digital footprints of users.
The latter is considered the most valuable non-traditional data. This includes any information about a person that can be found online as a result of their online activity.
Alternative credit scoring platforms analyze the digital footprints of a potential borrower and create a digital credit score.
Based on this, the lender can make an informed decision about granting a loan.
This gives enhanced models another advantage — they can continuously learn. Therefore, AI credit scoring relies solely on current data.
In contrast, traditional credit scoring models rely on historical data. Unfortunately, by the time they are checked, this data may be outdated.
The superiority of the effectiveness of enriched models over traditional risk assessment methods is confirmed by the results of a study.
In it, analysts used the AUC metric to assess credit models.
AUC – Area Under the Curve – is a metric that allows assessing credit scoring models, or more precisely, their discriminatory power. It shows how accurately the model can distinguish between reliable borrowers and potential defaulters.
The aforementioned study demonstrates the following results. With a perfectly accurate model score of 1.0 (100%), the accuracy of traditional scoring models was 68%. That is, AUC = 0.68.
The use of alternative data increases the model's accuracy to 70% – AUC = 0.7.
If the traditional scoring model is enriched with alternative data, it demonstrates the highest AUC score of 0.73. This means its accuracy reaches 73%.
It can be concluded that digital credit scoring is most effective when a credit risk modeler uses a combination of traditional and alternative data to analyze creditworthiness.
Modern lenders are increasingly choosing to boost predictive power with digital footprints.
Alternative data that shows maximum effectiveness includes:
Email data. By the address provided in the application, it is possible to establish a list of online accounts registered to it, determine the age of the email account, and identify data breaches or the inclusion of the address in high-risk subscriber databases.
Phone number data. The type of phone number and SIM card, operator code to determine location, spam distribution, and more. All of this can be determined with access to the applicant's mobile number.
IP address data. By knowing the applicant's IP address, the lender can establish their actual geolocation and determine the connection type and use of anonymizers.
Enriching a credit risk assessment model with this data brings several advantages for the lender. Among them are higher loan approval rates, reduced default risk, and effective fraud prevention.
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