Explainable AI in Credit Risk Assessment
Credit Risk Management involves assessing, controlling, and tracking the potential risk that arises when borrowers are unable to fulfill their financial obligations. This is a core function of financial institutions, ensuring stability, profitability, and trust. In the past, various credit risk assessment methods, such as the ‘5C’ analysis and CAMPARI, were used to evaluate credit risk. However, these decisions were often biased and inaccurate, as they relied on human judgment and limited data.
Currently, leveraging artificial intelligence, specifically machine learning and deep learning, has become a more effective and viable solution for the financial sector. This shift is due to the increasing availability of vast amounts of data and growing computational power. Deploying machine learning and deep learning models to manage credit risk has improved accuracy, speed, and reliability while reducing human resource requirements in the finance sector.
As finance companies place greater emphasis on predictive accuracy and efficiency, it has become necessary to ensure that predictions are interpretable and explainable to comply with regulations and build customer trust. Machine learning and deep learning models often function as black boxes. Therefore, it is important to utilize explainable AI (XAI), as it provides a framework for regulatory compliance and for explaining decisions without human intervention.
Interpretability involves producing descriptions or decisions that use simple vocabulary and are understandable to humans, while explainability refers to the understanding of the algorithms underlying the model. Explainable AI techniques, such as LIME and SHAP, have emerged as practical and effective solutions for providing interpretability into a model’s decisions.
SHAP uses game theory to show how each feature contributes to a prediction, giving both global and local explanations and showing how features push predictions up or down. LIME explains individual predictions by approximating the model locally with a simple, interpretable model, highlighting the key features for that specific prediction, and works with any machine learning model.
In this study, Explainable AI techniques, specifically SHAP and LIME, were selected to interpret and provide insights into model predictions for a publicly available loan data set. Initially, SHAP was deployed globally to understand the dataset’s interpretation. This helped the author understand the influence of features across the dataset. Subsequently, both SHAP and LIME were applied to selected instances, including both positive and negative decisions. To enhance interpretability, a variety of visualization methods and plots were generated.
SHAP and LIME, explainable AI techniques, demonstrated that they could produce interpretable outcomes for customers in their loan decisions, making the credit risk assessment meaningful and compliant with regulations. It indicated how each feature contributed to the decision, either positively or negatively, with its weightage.
Subsequently, to gain deeper insights and deliver clearer explanations to the customer, LIME was used to explore What-If scenarios. The rejected loan applications closest to the 0.5 threshold and focused on two adjustable features: annual income and loan amount. The aim was to determine whether increasing annual income or reducing the loan amount could have shifted the prediction to an approval. The purpose was to provide the customer with a clearer understanding of how certain amendments to the loan application could impact the loan approval and lead to a more favorable decision. However, the results were limited, with only one instance changing its outcome. This indicates that the model’s decisions depend on more complex feature interactions, which need to be better understood before LIME can be effectively used for customer-facing What-If explanations.
Despite these positive outcomes, certain challenges must be addressed when employing explainable AI. It is indeed important to understand the feature relationships before changing certain features in LIME to gain a deeper understanding or turn a negative decision into a positive one. Furthermore, these models can be highly sensitive to certain features; therefore, a simple change in a feature due to human error could mislead the customer, eroding confidence in the institution. Additionally, a mechanism is needed to convey the decision in a more human-readable format.
By addressing these challenges, the interpretability and explainability of credit risk assessment models can be enhanced, leading to more transparent evaluations with both regulatory and societal benefits. Adopting Explainable AI is much needed in the present context, and its implementation can help build trust among stakeholders, including the customers and the regulators. When further analysis of explainable AI tools is conducted, it helps identify potential biases in the model, leading to fairer decisions and equity across all demographic groups.
To strengthen accountability and trust in automated credit decisions, it is essential to effectively adopt explainable AI tools while focusing on the accuracy of the selected models. By adopting this strategy, it will create more responsible, ethical, and fair lending, build customer trust, and align with regulatory expectations.
Reference
Peiris, T.M.R. 2025. Towards Transparent and Fair Credit Risk Assessment. Turku University of Applied Sciences thesis.
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