AI Credit Scoring: The Future of Credit Risk Assessment

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:

  • Total income
  • Credit history
  • Transaction analysis
  • Work experience
  • User behavior analytics

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. To gain a deeper understanding of how machine learning is revolutionizing this field, read our detailed exploration in credit scoring using machine learning. This article provides insights into the data-driven nature of AI credit scoring, a cornerstone of modern financial analytics.

Understanding AI Credit Score and Its Meaning

AI credit score, or artificial intelligence credit scoring, is a modern approach to assessing a borrower's creditworthiness. Unlike traditional credit scoring, which relies on static variables and historical data, AI credit scoring uses machine learning algorithms to analyze a wide range of data points, including non-traditional data, to predict a borrower's likelihood of repaying a loan. The AI score meaning, therefore, represents a more comprehensive and dynamic assessment of credit risk, providing lenders with a more accurate and nuanced understanding of a borrower's financial behavior. For further insights into the practical applications and real-world scenarios of AI in credit assessment, our article on AI for credit modelling use cases offers a comprehensive overview.

The Role of Artificial Intelligence in Credit Scoring

Artificial intelligence plays a transformative role in credit scoring. Traditional credit scoring models often fail to account for the complexity and variability of individual financial behaviors. AI, on the other hand, can process vast amounts of data, identify patterns, and make predictions with a high degree of accuracy. This allows for a more personalized and fair assessment of creditworthiness. AI credit scoring also has the potential to extend credit opportunities to underserved populations, such as those with thin credit files or those who are new to credit, by considering alternative data in the scoring process.

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:

  • Current level of income
  • Employment opportunities
  • Potential ability to earn

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.

How Datrics Solves the Challenges of AI Credit Scoring

AI credit scoring, while revolutionary, is not without its challenges. The main criticisms revolve around the opacity of machine learning models (often referred to as the "black box" problem) and the potential for biased decisions. Datrics has developed solutions to address these issues.

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.

Tackling the Black Box Problem with Datrics

One of the primary criticisms of machine learning-based credit scoring is its opaque decision-making process. The intricate nature of these models often leaves analysts in the dark about the exact factors influencing decisions. Datrics addresses this "black box" issue by introducing mechanisms for model explainability, both in a general sense and on a case-by-case basis. This transparency ensures analysts can pinpoint the most influential factors in any given decision.

Credit Default Risk Prediction Interpretation

Datrics enhances understanding with visual tools designed for interpreting credit default risk predictions. A notable illustration shows the cumulative impact of various features on the model's output. Here, each model response is symbolized by a dot, its color indicating the value of the feature in question. Such visual aids empower analysts to discern the relationship between features and their respective SHAP values.

Visualization of Features' Impact on Predictions

To further demystify the prediction process, Datrics offers a tool visualizing the influence of individual features on predictions. Beginning from a base value—representing the average of all model responses—each Shapley value is portrayed as an arrow. These arrows, either augmenting or diminishing the prediction, offer a vivid depiction of each feature's contribution to the final outcome

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%.

Integrating ML with Traditional Scoring for Comprehensive Analysis

Datrics introduces a novel approach that seamlessly integrates the power of machine learning with the transparency of traditional scoring methods. By offering the capability to transform conservative statistical models into traditional scoring cards, Datrics effectively addresses prevalent criticisms of AI-driven credit scoring. This hybrid method not only leverages the benefits of machine learning but also ensures clarity and familiarity, akin to conventional scoring techniques.

To further enhance understanding, Datrics presents the Model Score Distribution plot. This visualization showcases the distribution of output scores across target classes, incorporating elements like the probability density function and both range- and quantile-based discretization plots. Such detailed representations allow analysts to see the proportion of class items within specific score ranges.

The transformation process is meticulously detailed. Each attribute in the credit scoring model is assigned a partial score, signifying its influence on the final decision. As the model forecasts credit default, these partial scores, which contribute to determining credit-worthiness, bear an inverse relationship to the model coefficients. This intricate system facilitates the categorization of risk groups based on their default probabilities, ensuring a comprehensive and transparent credit analysis.

Addressing Decision Bias

Finally, to address the issue of decision bias, the Datrics platform provides the possibility of a stratified approach to modeling and/or manual weighting. This significantly reduces the risk of model bias, ensuring that the model does not unfairly favor or disadvantage certain countries or cohorts.

Credit Scoring Process

The Datrics Credit Scoring process consists of the following steps:

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


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.


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.

lending processes

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

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.

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