By Brad Bruckschen and Somna Trehan, Oracle | March 23, 2023
The sharp rise in US Federal interest rates has created turbulence in the global banking sector and sheds light on the need for more confidence in bank asset liability management (ALM). ALM is one of the basic and core operational functions for any bank as it’s the backbone for their business model, and the basis for how to make a profit.
Time and again, banks are unable to assess the risk associated with certain investments and exposures, the resulting vulnerability spirals into severe crisis—as we’re seeing today. This can be ascribed to a gap in regulations, but a large share of responsibility remains with bank management to oversee any impending risk. There is a need for a clear view of risks across financial instruments and investments, along with the ability to forecast and analyze how potential risks will impact liquidity, profitability, and capital adequacy.
To respond quickly to adverse interest rate environments, bank ALM systems and processes should provide accurate and explainable outcomes, scale to meet transaction processing requirements, and provide flexibility to support interest rate risk reporting, everchanging scenario modeling requirements and what if analysis.
Most ALM systems and processes are unable to scale to properly handle a bank’s contract and account volume of deposits and loans. Many banks are forced to heavily aggregate their data thereby losing significant accuracy. In addition to having the required granularity of contracts and accounts, banks can’t operate on ALM systems that are not fully transparent in the underlying calculation logic, as this ensures results are explainable and can be independently verified as accurate.
As we speak ALM analysts across the globe are undoubtably facing new pressures from senior management to increase the number of scenarios being run. This is a primary focus to assess the impact of a variety of stress events in a much shorter amount of time than usually required. ALM reporting capabilities that can immediately access interest rate risk analytics can be the difference between identifying risks within a reasonable time to take the necessary mitigating action versus missing these risks all together.
So as an ALM Analyst within a bank, what if you had the ability to:
How can Oracle help?
Recent events are a stark demonstration of the importance that banks need to have strong governance and risk management processes that are underpinned by high quality data and focused insights. It’s no longer enough to solve for these required capabilities in siloes, they must be thought of in combination to ensure that firms are solving high priority business problems.
Asset Liability Management capabilities to help banks get an accurate view of their profitability and earnings stability as well as the overall risk exposure of their balance sheet. It provides timely and actionable insight for managing interest rate and liquidity risk and offers transparency into critical issues.
Funds Transfer Pricing to determine the spread earned on assets and liabilities, and the spread earned based on interest rate exposure for each customer account. It also provides an accurate assessment of profitability across products, business lines, channels, and customers as well as centralizing interest rate risk for effective management.
Liquidity Risk Management comprehensively addresses liquidity risk at a global enterprise level. The solution drives multi-jurisdictional compliance by addressing continuously changing regulatory guidelines. It lays out a flexible stress testing framework and builds scenarios based on multiple dimensions, magnitudes and at any required level of granularity. It provides capabilities to facilitate the most optimal usage of a bank’s reserves.
Profitability Management provides an enterprise-wide view of profitability drivers, risk-adjusted performance, and performance across multiple dimensions including product, business unit, legal entity, and channel. It uses a sustainable, repeatable, and integrated performance measurement process that generates fully reconcilable profitability and performance insight at multiple levels of granularity. It directly leverages cost and expense allocation outputs for computation of profitability trends and metrics for reporting.
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