The Essentials of AI-based
Credit Scoring 

1.5 billion people around the world do not have access to the services of a bank or similar financial institution. These individuals are referred to as ‘unbanked.’ For the rest of us, less than one-half of the banked population are eligible for lending. To extend the ability of banks to issue loans, smarter credit scoring solutions are evident. 

AI-based credit scoring is the most promising and relevant solution. Credit scoring evaluates how well a bank's customer can pay and is willing to pay off debt. 

AI-based credit scoring decisions are formed from data such as:

Scoring represents a mathematical model based on statistical methods and calculations for a large amount of information. 

As a result, AI scoring provides more sensitive, individualized credit score assessments based on an array of additional real-time factors. This allows more people with income potential to access financial services.

How AI-based Credit Scoring with Datrics is Different from Traditional Models

In most financial institutions, credit scoring models still operate on the scorecard approach, i.e., the dynamics characteristic for the time of their inception. A potential borrower has to possess sufficient historical data on previous borrowing behavior to be rated as "scorable." In cases where this type of historical information isn’t available (which is a typical situation for new customers of the banking sector), even creditworthy borrowers are denied access to credit.

Moreover, the traditional scorecards have a limited lifetime because the key attributes commonly vary throughout time, so-called population drift. This might be explained by changing economic conditions or new credit strategies (new target audience, new credit products, etc.) In this case, the financial institution risks losing the precision of the credit default estimation resulting in financial loss.

Unlike traditional credit scoring methods (e.g., the scorecard method), by focusing on the past performance of a borrower, AI credit scoring models built with Datrics are more sensitive to real-time indicators of the potential borrower's creditworthiness, such as:

Borrowers with high potential (for instance A/B credit score) are invited to participate in the credit programs, while those who formally pass the conventional credit scoring assessment (e.g., credit card churners) are excluded from them. 

In other words, AI-based credit scoring allows for more precise profit predictions based on the smart AI models.

AI/ML advantages for credit scoring by Datrics are: 

Datrics also solves the issue with the disadvantages of the ML approach.

The main criticism of credit scoring based on machine learning is the unclear results and biased decisions. In the first case, it is usually pointed out that it is impossible to understand exactly how decisions are made since the model is a black box. Datrics tackles this problem by encapsulating the mechanism of the model explainability in both general and case-by-case instances — the analyst can see what exactly impacted the decision, what was the most critical factor, or the combination of which factors had the most significant influence on the result.

Credit default risk prediction interpretation. The first figure depicts the cumulative features' impact on the model's output. The explanation of each observation's model response from the feature perspective is described as a single dot, which represents the correspondence of the feature value to the SHAP value of that feature. The feature's value (from low to high) is reflected via the dot's color (from blue to red)The second figure is the visualization of the features' attributes that force to increase or decrease the prediction. The prediction starts from the base value - the average of all model responses. Each Shapley value is depicted as an arrow that makes the model increase or decrease the prediction.

Credit default risk prediction interpretation. The first figure depicts the cumulative features' impact on the model's output. The explanation of each observation's model response from the feature perspective is described as a single dot, which represents the correspondence of the feature value to the SHAP value of that feature. The feature's value (from low to high) is reflected via the dot's color (from blue to red).

The second figure is the visualization of the features' attributes that force to increase or decrease the prediction. The prediction starts from the base value - the average of all model responses. Each Shapley value is depicted as an arrow that makes the model increase or decrease the prediction.

Moreover, Datrics provides the possibility to use conservative statistical models with their transformation into traditional scoring cards. Thus, on the one hand, we get all the advantages of using machine learning for credit scoring, and on the other, we negate the criticism of this approach.

Model Score Distribution plot. It depicts the distribution of the output scores per target classes, including probability density function, and range- and quantile-based discretization plots, which reflect the share of the class items that took the specific score range

Model Score Distribution plot. It depicts the distribution of the output scores per target classes, including probability density function, and range- and quantile-based discretization plots, which reflect the share of the class items that took the specific score range

The transformation of the credit scoring model to the traditional scorecards. Each attribute gets a partial score, which reflects its impact on the final decision and leads to the expected Scores range.As the model predicts the credit default, the partial scores, which contribute to the credit-worthy score, have the opposite sign to the model coefficients. High partial scores characterize the low-risk groups (A and B) with the risk default probabilities range probabilities less than 15% and 25%, correspondingly. In comparison, the high-risk group (E) relates to a default probability higher than 75%.

The transformation of the credit scoring model to the traditional scorecards. Each attribute gets a partial score, which reflects its impact on the final decision and leads to the expected Scores range.

As the model predicts the credit default, the partial scores, which contribute to the credit-worthy score, have the opposite sign to the model coefficients. High partial scores characterize the low-risk groups (A and B) with the risk default probabilities range probabilities less than 15% and 25%, correspondingly. In comparison, the high-risk group (E) relates to a default probability higher than 75%.

As for the second case, decision bias — i.e., the model bias towards a certain country or cohort, Datrics platform provides the possibility of a stratified approach to modeling and/or manual weighting, which significantly reduces the risk of model bias.

Credit Scoring Process

The Datrics Credit Scoring process consists of the following steps:

Add custom code bricks to your pipeline, set up the arguments, and run pipeline as usual

Besides developing individual credit score models, Datrics can provide business expertise for clients and support them with options for deployment and maintenance.

Note: Datrics solution is usually run isolated inside the client's infrastructure, removing the need to share the client’s data directly with us.

Benefits of AI Credit Scoring with Datrics

Speed

Credit scoring with Datrics. Speed up lending

Using credit scoring models speed up the process of making lending decisions. Traditionally, banks used decision trees, regression, and complicated arithmetical analyses to generate a client's credit score. With Datrics, you can organize superfluous, unstructured, and partially structured data in convenient and clear models to make smarter credit-related decisions in real-time.

Democratized
Crediting

Credit scoring with Datrics. Democratized crediting

With Datrics’ credit scoring models, more borrowers get access to credit, stimulating their businesses and helping them jumpstart their ideas. Getting one's first-ever credit report has become simpler as it is based on the AI financial projections regarding the client's income potential and employment opportunities.

Streamlined
lending processes

Streamline lending process with credit scoring built on Datrics

The use of AI tools for credit scoring and lending decisions helps lenders make data-driven decisions, focus on margin maximization instead of risk minimization, and estimate a smoother risk vs. profit curve instead of using pre-calculated scoring card brackets. 

With Datrics, lenders can increase their number of customers and their profits while streamlining the overall process.

AI accessible
to everyone

Datrics makes AI accessible

The prime focus of Datrics’ model is to expand access to the AI where it is needed most. This way, more people in different industries can use the power of artificial intelligence to solve financial and social issues. 

Credit scoring systems by Datrics have a user-friendly interface, clear structure, and no-code base, making it possible for everyone to understand and use the models.

With Datrics, you can build credit scoring to perform precise credit risk assessments and credit scoring based on masses of data, enabling accurate eligibility forecasting and smart borrower ratings. Besides substantial human resource savings, the AI credit scoring systems by Datrics help lenders address "bad" loans via intelligent customer segmentation and forecasting algorithms.

Book a demo with us, and find whether Datrics is the right tool for you.

Do you want to discover more about Datrics?

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