Preeti
Generative AI (gen AI) has rapidly gained popularity since its launch in late 2022, with Open AI’s ChatGPT service attracting over 100 million users within two months. Big technology companies are now integrating gen AI capabilities into their products, particularly in industries like credit risk. McKinsey surveyed senior credit risk executives and found that 20% have already implemented gen AI use cases, with 60% planning to do so within a year. Use cases in credit risk include client engagement, credit decision processes, portfolio monitoring, and customer assistance.
While gen AI has started to make an impact in credit risk, its current use cases are narrow and non-customer facing, focusing on specific operational pain points. Challenges in scaling up gen AI in credit risk include risk and governance issues, data quality concerns, and model risk issues. Financial institutions need to develop a common set of practices to prioritize, develop, deploy, maintain, and reuse gen AI applications to fully realize its potential in credit risk management.
Implementing practices such as an AI roadmap, aligned processes for building gen AI tools, a secure technology stack, and a governance and talent model can accelerate successful gen AI deployments by 30 to 50%. Major credit risk players are quickly embracing generative AI, which has the potential to improve efficiency, accuracy, and personalized services across the credit life cycle. Overcoming challenges related to risk governance, talent acquisition, and ecosystem development will be crucial for financial institutions to unlock the full potential of gen AI in credit risk management.
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Source link: https://www.mckinsey.com/capabilities/risk-and-resilience/our-insights/embracing-generative-ai-in-credit-risk
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