How GenAI Is Transforming Risk and Compliance in Banking

AI Banking Insights | March 13, 2024

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Generative AI: Transforming Risk and Compliance in Banking

As outlined by McKinsey, Generative AI (GENAI) is destined to completely transform risk management and compliance in the banking sector.  The technology will streamline operations and enhance the strategic capabilities of financial institutions by automating tasks, improving decision-making, and fostering innovation.

Chief Risk Officers (CROs) can use GenAI to identify high-priority areas where generative AI can deliver the greatest impact, aligning with the bank’s strategic goals and compliance requirements.  Companies are advised to start with a focused set of use cases for generative AI implementation, allowing for quick wins and the establishment of best practices for wider application.

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Be sure to implement guardrails around the use of generative AI to manage novel risks, including data privacy, model bias, and security vulnerabilities. Encourage cross-functional collaboration between risk management, IT, and business units to ensure the successful integration and utilization of generative AI technologies.

• Generative AI acts as a virtual regulatory and policy expert, automating regulatory compliance checks and providing alerts for potential breaches. This reduces the manual workload and increases the accuracy of compliance processes.
• The technology automates the generation of suspicious activity reports and updates customer risk ratings, enhancing the efficiency and effectiveness of financial crime and anti-money laundering (AML) detection.
• Generative AI accelerates the end-to-end credit process, from summarizing customer information to drafting credit memos, thereby improving the speed and quality of credit risk assessments.
• The automation of model monitoring and the generation of alerts for performance outliers streamline the management of data analytics, making the process more efficient.

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• By generating code for detection rules and accelerating secure code development, generative AI strengthens the cybersecurity framework, mitigating risks associated with digital threats.
• The technology aids in the automation of data collection for counterparty transition risk assessments and generates early-warning signals, facilitating a proactive approach to managing environmental and climate risks.

Building Winning Strategies

To effectively harness the potential of generative AI, CROs should consider the following strategies:

• Evaluate potential use cases based on their impact, risk, and feasibility, prioritizing those that align with the bank’s vision and regulatory landscape.
• Build a catalog of reusable gen AI services and solutions, supported by a secure, AI-ready tech stack and integrated with enterprise-grade models and tools.
• Cultivate a talent pool skilled in gen AI and establish governance models that promote innovation while ensuring compliance and ethical use of AI technologies.

Conclusion

By strategically planning and responsibly implementing generative AI, banks and financial institutions can mitigate risks more effectively but also unlock new avenues for growth and innovation.

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Integrating AI will become increasingly critical for maintaining competitive advantage and achieving operational success.

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