See how KPMG helped a leading bank enhance its Probability of Default model with AI, achieving a 15% increase in accuracy (Gini Coeff)—unlocking unparalleled predictive insights.
Before KPMG
A major bank in Kazakhstan was grappling with significant challenges in developing and managing its Probability of Default (PD) models. These models lacked robustness and suffered from low predictive power, making it difficult to accurately differentiate between high-risk and low-risk borrowers. Additionally, the reliance on traditional statistical methods hindered the bank’s ability to address the complex, non-linear relationships within its data, resulting in inefficiencies and inaccuracies in credit risk assessment.
Manual approaches to variable selection further exacerbated the issues, leading to suboptimal model stability and reduced reliability. Long computation times for model development and validation delayed decision-making processes and increased operational costs. Moreover, the lack of transparency and interpretability in the models created compliance challenges, making it difficult for the bank to justify credit risk decisions to regulators. These inefficiencies heightened regulatory scrutiny, created operational bottlenecks, and eroded confidence in the bank's credit risk management systems.
Enhanced Probability of Default model accuracy, richer client insights, and faster time-to-production—all delivered in a single, AI-powered solution.
Key Pillars of KPMG’s Collaboration
KPMG’s Financial Risk Management (FRM) team leveraged advanced machine learning techniques to address these challenges and revolutionize the bank’s PD models. By applying sophisticated clustering algorithms, the team segmented loan takers more effectively, enabling granular risk profiling and improving the predictive power of the models. This approach delivered richer insights while significantly reducing analysis time.
The collaboration also focused on intelligent variable selection, identifying the most relevant features for credit risk prediction. Machine learning algorithms, including advanced techniques to analyze patterns over time, were employed to validate and recalibrate the models, ensuring they adapted to evolving market dynamics. As a result, the bank’s PD models achieved greater accuracy, robustness, and efficiency, providing precise credit risk assessments well into the future.
KPMG’s expertise extended beyond technical enhancements. The FRM team fostered a close partnership with the bank's financial risk professionals. By sharing advanced validation insights and contributing to a forward-thinking regulatory framework, KPMG helped establish best practices for financial risk management.
Client Outcomes
KPMG’s AI-driven solutions delivered immediate and transformative results to the client, achieving a 15% increase in its PD model’s distinction accuracy (Gini Coeff). This enhancement enabled more precise differentiation between high- and low-risk borrowers, empowering the bank to make better-informed lending decisions and mitigate credit losses.
Additionally, computation times for model development and validation were drastically reduced, allowing for quicker deployment of models into production. The integration of interpretable AI models ensured greater transparency and auditability, building trust among internal stakeholders and regulators. These advancements also streamlined compliance and regulatory reporting processes.
One standout achievement involved recalibrating the PD model to incorporate macroeconomic factors, enabling the model to account for external economic shifts. Advanced machine learning methods identified the most predictive factors, minimizing human bias and optimizing model performance. This recalibration resulted in a marked improvement in the model's reliability, providing the bank with a resilient tool to navigate dynamic economic environments.
