Game changer: Gen AI can transform how banks tackle regulation and risk

Banks are advised to prepare a catalog of prod ready gen AI services and solutions.

Generative AI may just change the way financial institutions (FI) tackle regulatory compliance, risk assessments, and financial crime, says McKinsey & Co.

However, FIs should remember to put guardrails around gen AI’s use in an organisation, the global management consulting firm said.

“Gen AI has the potential to revolutionize the way that banks manage risks over the next three to five years,” McKinsey & Co. said in their latest report, “How generative AI can help banks manage risk and compliance,” written by associate partner Rahul Agarwal; partner Andreas Kremer; senior partner Ida Kristensen; and partner Angela Luget.

One major change it could bring about is it could free up FIs’ risk professionals to pursue a “shift left” approach: It could allow functions to move away from task-oriented activities toward partnering with business lines on strategic risk prevention and having controls at the outset in new customer journeys.

That, in turn, would free up risk professionals to advise businesses on new product development and strategic business decisions, explore emerging risk trends and scenarios, strengthen resilience, and improve risk and control processes proactively, McKinsey & Co. said.

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Emerging applications
One use for gen AI could be to create risk intelligence centers that serves all lines of defense: business and operations, the compliance and risk functions, and audits. Such a center would provide automated reporting, improved risk transparency, higher efficiency in risk-related decision making, and partial automation in drafting and updating policies and procedures to reflect changing regulatory requirements.

Overall, McKinsey can see applications of gen AI across risk and compliance functions through three use case archetypes: first is through a virtual expert, where a user can ask a question and receive a generated summary answer, with the information and data coming from long-form documents and unstructured data with the bank.

Gen Ai can also be used for manual process automation, where it will perform “time consuming tasks.” Gen AI can also be tasked with updating and translating old code or writing new code.

For instance, McKinsey has developed a gen AI virtual expert that can provide tailored answers based on the firm’s proprietary information and assets. 

“Banks’ risk functions and their stakeholders can develop similar tools that scan transactions with other banks, potential red flags, market news, asset prices, and more to influence risk decisions. These virtual experts can also collect data and evaluate climate risk assessments to answer counterparty questions,” McKinsey noted.

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Regulatory compliance, credit risk
Two major use cases for gen AI are for better regulatory compliance and credit risk.

“Enterprises are using gen AI as a virtual regulatory and policy expert by training it to answer questions about regulations, company policies, and guidelines. The tech can also compare policies, regulations, and operating procedures,” the report said. “It can automate checking of regulatory compliance and provide alerts for potential breaches.”

Gen AI also has the potential to generate suspicious-activity reports based on customers’ and transaction information; or create and update customers’ credit risk ratings based on their data.

“Financial institutions are using the tech to generate credit risk reports and extract customer insights from credit memos. Gen AI can generate code to source and analyze credit data to gain a view into customers’ risk profiles and generate default and loss probability estimates through models,” McKinsey wrote.

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Winning strategies
McKinsey & Co. suggests developing a gen AI ecosystem with a prepared catalog of production-ready and reusable gen AI services and solutions. The catalog should be easily pluggable into a range of business scenarios and applications across the banking chain.

Banks’ gen AI services should also support hybrid-cloud deployments, enabling support for unstructured data, vector embedding, machine learning training, execution, and pre- and post-launch processing, it added.

Banks are also advised to prepare a road map detailing the timeline for when various capabilities and solutions will be launched and scaled that aligns with the organization’s broader business strategy.

“For gen AI adoption by risk and compliance groups to be effective and responsible, it is critical that these groups understand the need for new risk management and controls, the importance of data and tech demands, and the new talent and operating-model requirements,” McKinsey & Co. said.