Financial news

The second half of 2023 looks set to be challenging, with economists anticipating that interest rates will climb to six per cent by the end of the year. Amid this difficult environment, the importance of focused and purposeful asset and liability management is clear. 

Ashley Crawford, risk specialist at analytics, AI and data management software and service provider, SAS. Here, Crawford explores the importance of being able to proactively plan and manage balance sheets – and how this can enable a better understanding of a range of business, economic and market assumptions.

With economic volatility showing no sign of easing and interest rates likely to increase yet again, banks need to gain a clear understanding of their balance sheets. This became a priority following the 2008 financial crash and subsequent global economic downturn.

In fact, one PWC report that reviewed the scope of balance sheet management in 2009, found that many banks had undertaken an extensive review of liquidity risk management following the crash, with 88 per cent introducing a formal risk appetite for liquidity risk.

Most banks had also aligned their asset liability management (ALM) framework with the Basel Committee on Banking Supervision (BCBS) guidance, ‘Principles for the Management and Supervision of Interest Rate Risk’, and half of the respondents had conducted an independent third-party review of the ALM framework.

At the time, the report found that banks tended to operate several legacy systems to manage different aspects of the balance sheet, including liquidity risk and interest rate risk. Many had plans for change, with experts predicting that banks would rapidly upgrade to a more integrated approach, so planning and stress scenarios could be conducted across all aspects of the balance sheet.

But where are we 15 years on and why does accurate balance sheet management remain so important?

Understanding liquidity risk

Liquidity risk refers to how a bank’s inability to meet its obligations (whether real or perceived) threatens its financial position or existence.

As already mentioned, institutions manage their liquidity risk through effective asset liability management (ALM). Since the 2008 financial crisis, ALM as a discipline has matured and banks are needing to assign greater strategic importance to broader ALM programmes.

Prior to the collapse of the investment bank, Lehman Brothers – one of the events that triggered the financial crisis, it would be fair to say that many institutions took liquidity and balance sheet management for granted.

This made it impossible to maintain adequate liquidity and appropriate balance sheet structures during times of economic uncertainty and as banks started to fail in the United States, the central bank had to step in and inject liquidity into the national financial system in an attempt to keep the economy afloat.

The consequences of this poor asset-liability management reached far beyond just one financial institution and was one of the many factors that sent shockwaves across the economy and led to a global financial crisis.

Fast forward 15 years and recent events in the industry have shown that inadequate ALM is still a factor behind banking failures and cause for distrust amongst depositors and investors.

There is, therefore, an increased focus on analytics and data management and there is a need for a broader range of mathematical tools to tackle both short and long-term ALM and balance sheet challenges.

The role of balance sheet management

Without a centralised view of balance sheets, firms will struggle to understand their balance sheet position. Similarly, if departments work in silo, efforts to assess the impact of illiquid assets and asset classes across different geographies and business units also become far more challenging.

The problems that this can create cannot be overlooked.

By implementing a suite of tools for transaction capture, forecasting, interest rate risk measurement, stress testing, liquidity modelling and behavioural analytics, accurate balance sheet management can become far easier, with banks able to manage and optimise assets, liabilities and cash flows to meet their obligations.

By deploying analytics, banks can accurately project cash flows and net interest margins for underlying transactions, particularly when those transactions number in the millions.

Scenario-based analytics

To embed an effective liquidity risk management and ALM system, there are three core steps to follow.

• Firstly, firms must establish an analytic framework for calculating risk, optimising capital and measuring market events and liquidity.
• To minimise the impact of market shocks, and look for better arbitrage opportunities, analysing the effects of changes in cost and liquidity in near-real time will allow firms to act with precision.
• Using on-demand scenario analysis based on the most complex portfolios, firms can quickly find optimised solutions tailored to specific liquidity and capital needs. This is especially important during times of economic instability.

It’s not only market uncertainty that is placing pressure on financial institutions, the financial services business is growing and becoming more competitive. With new entrants and challenger banks offering innovative products and customer-centric services, as well as rising costs creating a need for higher capital requirements and higher quality liquidity reserve requirements, implementing the tools to better manage asset allocation needs to be a top priority.

Through the use of modern analytical tools, designed to help determine their optimal asset allocations, balance sheet compositions and liquidation strategies, financial institutions can achieve incremental operational and financial improvements.

At SAS, we understand that banks want a cloud-based risk management platform offering flexibility and transparency across an enterprise-wide risk system. Our acquisition of Kamakura further strengthened our expertise in ALM and by combining our leading analytical platform with the Kamakura Risk Manager (KRM) tool, customers benefit from more comprehensive and open risk modelling capabilities for ALM, liquidity and credit risk.

Given today’s challenging environment, taking a flexible approach to asset liability management will prove critical in maintaining competitiveness and preventing the recent bank collapses that we have seen within the industry.