Sigmoidal Case Study

Credit Scoring With AI

Learn more about the AI-based credit risk management system, which we developed for a bank in Birmingham, Alabama. Over the course of 6 months after the integration, the default rate decreased by almost 15%.

Background

Tackling The Risk

Many potential borrowers look good on paper and are classified as creditworthy, but are in fact risky. This is because traditional methods can give credit to consumers who, e.g., have a large number of reward credit cards but sign for them to get bonuses (the so-called “card churners”), and are not, in fact, profitable for the issuer.

On the other hand, many of those who could turn out to be creditworthy get rejected by the traditional credit model. This can be caused by factors like payment history, types of credit used, and recent credit inquiries. Therefore, it will deny credits to consumers without a good credit history, no matter what is their actual current situation.

challenge

Improve the credit scoring process to minimize the risk and decrease the default rate.

SOLUTION

A ML-based system for assessing each individuals’ credit scoring.

Value Delivered

15% Reduction In Default Rate

Over the course of 6 months after the integration, the default rate decreased by almost 15%.

45% Faster Process

The credit scoring process has been sped up by more than 45% with less cases going to manual verification.

Risk Mitigation

The accuracy improvement can help better assess those risky borrowers, who look good at first sight.

Solution

As lending is a big data problem, machine learning is a great tool to leverage. The more data you have about an individual borrower and similar borrowers (and their history of loan repayment), the better you can assess if someone is genuinely creditworthy.

Our tool offers a granular and individualized approach. Among the others, it can consider factors like current income, employment opportunities, and one’s ability to earn. The amount of this data is so vast that it is almost impossible for humans to go through it. AI, on the other hand, can analyze all of these data sources together to create a coherent decision, uncovering hidden patterns.

The solution can also complete regulatory demands to provide grounds for credit decisions automatically.

Implementation

Part 1: Data Preparation

The bank already held the most valuable data: loan repayment track records. This is crucial to understand the market dynamics. To build an effective model, the bank needed to share this data (anonymized) with us (an AI company).

To make the scoring more accurate, we used alternative data. Currently, there are no regulatory challenges in using diverse sets of data for credit scoring. However, some raise ethical and privacy concerns in this matter. Nevertheless, such criteria as the applicant’s income, career, education, or utility and rental payments, although it took time to gather, were valuable for effective credit scoring.

This first step was, therefore, collecting all the available data and integrating it into our model.

Part 2: Feature Engineering

In this case, one of the most challenging parts of the project was Feature Engineering. It is the process of using domain knowledge of the data to extract features from raw data and to create new ones based on data insights. If feature engineering is done correctly, it increases the predictive power of machine learning algorithms.

The process was quite a time-consuming one and took most of our engineering team’s time when building the initial ML pipeline.

Part 3: Validation

The problem with traditional credit scoring is that it reinforces historical biases. If the system is to be free of discrimination the data that is used needs to be debiased. The data required to identify and remove discrimination include gender, age, race, disability, national origin, religion, sexual orientation, marital status, and more.

After each iteration, the client got a detailed performance report, so that he could regularly track insights and statistics.

Conclusion

Our AI solution made the process of credit scoring much more accurate due to quicker and broader data analysis. The credit scoring process has been sped up by more than 45% with less cases going to manual verification. The system allowed our client to detect high-risk borrowers more effectively, reducing the default rate by almost 15%.

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