Credit Risk Modeling
Harnessing the power of advanced machine learning, our credit risk models detect intricate patterns, and deliver accurate risk assessments enabling banks and lenders make well-informed credit decisions, and mitigate risks
Key Results from our use case deployment
Business Challenge
Inaccurate risk assessment: Traditional credit risk models were unable to accurately predict risk, leading to higher default rates and increased exposure to bad debt.
Inefficient decision-making: Manual evaluation of credit applications was time-consuming and prone to human error, hindering agile responsiveness to market changes.
Regulatory compliance: Evolving regulations required real time adaptation of credit risk management practices to ensure compliance, adding complexity to existing processes.
Our Use Cases for Credit Risk Modeling:
- Credit Scoring
- Credit Loss Reserving
- Stress Testing
- Risk-based Pricing
- Loan Underwriting
- Portfolio Management
- Regulatory Compliance
Outcome: Key Benefits of Credit Risk Modelling
Our AI-driven solutions are targeted at reducing loan default, increasing accuracy of PD (probability of default), ensure seamless integration with existing credit risk management system, with ongoing support to maintain optimal performance of the solution
| MORTGAGE LOAN | WHOLESALE LOAN |
| 84% increase in model quality in PD model | 32% improvement in PD model |
| 47% increase in model quality of LGD model | 30% increase in accuracy of risk assessment |
| 70% Reduction in IT Team Effort due to DW built | Loan approval timelines reduced by 40% |
- Enhanced decision-making: Our AI-driven models provided better insights into borrowers' creditworthiness, resulting in the accuracy of risk assessments. The client could make better-informed decisions regarding loan approvals and credit terms, minimizing potential losses by up to 25%
- Reduced exposure to bad debt and lowering default rates
- Adoption of AI in the credit risk management process accelerated decision-making
- Ensured credit risk management practices were compliant with regulatory requirements, mitigating potential penalties and reputational damage
- Improved ability to attract and retain customers while maintaining financial stability
Our Credit Risk Modeling Techniques Using Advanced AI & Data Science
- Exposure at Default: We use custom neural nets to model UACF (Unadjusted Consumer Finance Charge) and derive CCF (Credit Conversion Factor), CEQ (Common Equity) and LCF (Loan Conversion Factor)
- Asset Correlation and Valuation: We value counterparties using jump-diffusion models on credit spreads of corporate bonds. Asset correlation in portfolio is modelled through a gaussian copula, tail copula estimation and a GARCH (Generalized Autoregressive Conditional Heteroskedasticity) based approach to handle expected and extreme events
- CCAR/DFAST: We use custom DSGE (Dynamic Stochastic General Equilibrium) models based on Smets-Wouters model to shock/stress the model across preference, investment, price, federal spending, interest, inflation and exchange rate
- Decision Level Probability of Default (PD) & Performance Level PD: Deep learning-based sequential classification models analyze current credit worthiness and stressed PD across the business cycle or duration of loan
- Time to Default: Ensemble Deep learning-based time series models to understand the likely time of default for a counterparty
- Loss Given Default (LGD) & Charge Off: Deep learning-based models analyze the cure rate and recovery rate under both normal and stressed conditions. Models to analyze when the customer should be moved to charge off, estimate loss and start recovery process