Banking
How Adopting AI can Limit Risk in Corporate Banking
Global Banking & Finance Review®
Company
By Stuart Tarmy, Global Director, Financial Services Industry Solutions, Aerospike
Corporate banking risk management, which aims to limit the risk exposure and asset losses for an institution, often looks at issues such as fraud, investments, payments, credit, debt, assets, and financial markets. Unfortunately, when corporate banking risk management falls short, the impact can be enormous and result in billions of dollars in losses. Risk can occur in many parts of the bank, making it difficult for auditors and risk management experts to detect problems early without proper due diligence and stress testing.
For example, Credit Suisse lost $5.5 billion when Archegos Capital Management investment fund collapsed after losing big on the collapse of ViacomCBS stock. In the face of shareholder anger, the bank has admitted it failed and was to blame, citing “fundamental failure of management and controls.” Another report chronicled an overworked and underqualified staff as part of the problem, with a system focused on increasing sales and pleasing big clients rather than monitoring risk.
In another example, a federal judge earlier this year ruled that Citigroup is not entitled to recoup $893 million that it accidentally wired to lenders on behalf of a Citigroup customer, Revlon. Citigroup says it meant to send only a $7.8 million interest loan payment and blamed the foul-up on human error. While Citigroup says that lenders should have known it was a mistake and returned the money, U.S. District Judge Jesse Furman notes that “to believe that Citibank, one of the most sophisticated financial institutions in the world, had made a mistake that had never happened before, to the tune of nearly $1 billion – would have been borderline irrational.”
It was another blow to Citigroup after being hit with a $400 million fine by federal regulators in 2020 for long-term deficiencies and “longstanding failure to establish effective risk management.”
Such losses for Credit Suisse and Citigroup emphasise the need for better corporate bank risk management, especially in the face of growing pressures such as:
The shape of risk management today
Risk managers currently consider Value at Risk (VaR), a statistic that quantifies the scope of potential financial losses within a firm over a specific time frame over different economic scenarios. Corporate banks commonly use VaR to determine the probability and extent of capital loss or drawdowns in an institutional portfolio. Banks look to do this across all their securities, which can range from highly liquid equities to less liquid bonds and derivatives, to highly illiquid real estate or private placement funds (e.g., hedge funds, venture capital investments).
For example, the bank might want to know how their portfolio would react if the same economic, political, stock market, or interest rate conditions exist today that were similar to what happened during:
AI to manage risk in corporate banking – the time is now
The next ten years in corporate banking risk management are expected to bring a greater emphasis on analytics, underscoring the move by institutions to use data and artificial intelligence (AI) to better manage current risks in real-time and make more intelligent predictions about the future.
It has been reported that 15% of a corporate bank’s risk management staff today are dedicated to analytics, and that percentage will jump to 40% by 2025. That’s a significant shift from today’s working model, where 50% of a risk function focuses on risk-related operational processes such as credit administration.
Many risk management challenges can be met by adopting artificial intelligence (AI) to identify high-risk areas and provide automation and controls to limit the risk. Using vast amounts of data, AI can help corporate banks strategise for the future, make better real-time decisions, improve risk modelling, provide better monitoring and prevent costly human errors.
Still, AI requires lots of data to learn and then improve and optimise information for an organisation. That data also needs to be fast so that decisions can be made in real-time.
For example, a leading multinational financial services company moved to a modern data platform to accurately manage in real time account authentication, trade authorisation, and compliance/risk controls. The data platform handles large amounts of data quickly, ensuring that the company provides best-in-class responsiveness to customers’ trading activities while remaining in compliance with securities regulations and internal controls. At the same time, it ensures consistent data and performance with scalability and low latency, even during peak trading periods.
The current unprecedented worldwide regulatory and market pressures are likely to be further compounded in the coming years. Corporate banks who adopt a proven real-time platform will be the ones who can handle their data quickly, reliably, and consistently will, in turn, be able rely on AI and data to help prevent costly human errors and provide greater insight when determining risk.
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