Leveraging Gen AI in Reimagining Credit Risk Management

10 June 2024
Knowledge Base

by Ajay Katara

Gen AI adoption is steadily increasing in many banking and financial organisations. Its applications are expanding into risk and compliance areas, with each risk discipline identifying use cases where Gen AI can enhance efficiency by providing better insights, cost benefits, and faster turnaround times. Within the enterprise risk function, credit risk management is a primary financial risk function that significantly impacts many banking organisations. Over the past 15 years, credit risk management has matured through numerous regulatory mandates and transformation initiatives. However, there are still many areas within this function that can benefit from the adoption of generative AI.

Across the Credit Risk Lifecycle there are many areas that can potentially benefit from Gen AI based interventions, some of the key use cases are highlighted below.

  • Credit Risk Assessments – Credit Risk assessment involves analyzing extensive information from the moment a customer becomes a prospect to the rollout of a credit line. AI capabilities can be leveraged to examine customers’ financial data and utilize AI models that generate new data points or simulate scenarios based on existing data. This approach provides innovative methods to predict creditworthiness and manage credit risk.
  • Early warning Indicators – Generative AI can enhance credit risk early warning systems by synthesizing Internal and external data to improve model training and recognizing complex patterns that signal emerging risks. It can simulate economic scenarios for stress testing and boost predictive modeling accuracy. By analyzing unstructured data through NLP, it detects early risk indicators from various sources. Additionally, generative AI continuously learns from new data, adapting to changes, and provides intuitive visualizations for risk managers to quickly identify potential issues.
  • Credit Risk Controls – In the area of Controls there a functions like Credit limit management and Collateral management which can benefit from the potential intervention of Gen AI. Generative AI enhances credit risk controls such as limit management by analyzing vast datasets to identify optimal credit limits for borrowers. It detects patterns and trends that indicate potential breaches. Through scenario simulations, AI predicts the impact of various credit limit changes under different economic conditions. It continuously learns from new data, adapting limit strategies to evolving risk profiles. This approach enhances the effectiveness and responsiveness of credit risk control measures.
  • Credit Monitoring – In terms of monitoring, banks can use Gen AI to collect and analyze massive volumes of Internal and External data to build a 360-degree perspective on a customer’s financial profile. As a result, such a degree of monitoring allows institutions and organizations to get the most out of credit monitoring strategies.
  • Credit Reporting – Gen AI in combination with machine learning models can automate the generation of credit risk reports by scanning and analyzing vast amounts of data to provide insights on portfolio risk exposure, capital adequacy, and regulatory compliance. This reduces the manual effort needed for reporting, enabling risk management teams to concentrate more on reviewing, strategic decision-making, and risk mitigation. Gen AI also helps in generating automated narrative for Management reports that need to be submitted to the senior management and for Regulatory reports that need to be submitted to the regulators. These report narrations though are generated through Gen AI reduce time and enable the credit risk team to spend more time of review of the reports and the commentary generated.

By integrating generative Al into risk management frameworks, organisations can achieve a more proactive, accurate, and efficient approach to mitigating risks. However, the adoption of Gen Al in risk and compliance functions should not be done is a siloed manner and without a manual review mechanism in place.An enterprise adoption framework with necessary Governance and controls should be in place to enable organisations leverage learnings, better understand cross function dependencies and create a reusable infrastructure which enables efficient adoption of Gen Al to support Business initiatives.

The author, Ajay Katara, serves as a consulting partner and leads the Reg Tech portfolio within the banking risk management domain at Tata Consultancy Services. With over 19 years of expertise in business consulting transformation and solution design, he navigates regulatory compliances in the areas of Regulatory Capital Management, Credit Risk, Climate Risk, Stress testing and Anti Money Laundering. Operating across diverse geographies, Ajay has collaborated with numerous financial institutions and enterprises. His substantial contributions to conceptualising strategic offerings in risk management and his impactful role in driving successful consulting engagements underscore his influence. He has also been awarded the Risk Management Professional of the Year award by CIRM Magazine UK in 2023. 

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