Improving the asset liability management modeling process
Each bank is unique in that it possesses a distinct strategy, asset mix, product selection, customer base and risk profile that differentiates it from others. As such, the profitability and survival of each one of these institutions are largely determined by how well their boards and senior managers can balance current and potential earnings while maintaining adequate liquidity amidst intense market competition, growing regulatory complexity, new technologies and ever-present interest rate risks.
As the Office of the Comptroller of the Currency (OCC) clarified in October 2012, analyzing the potential impact of adverse economic events on banks’ performance is a critical and essential process to help institutions set adequate concentration limits, adjust their risk management strategies and maintain adequate capital levels. National banks and federal savings associations with $10 billion or less in assets are expected to stress test their loan portfolio at least once a year. Larger banks with assets above $10 billion generally address the stringent stress testing and the Fed’s Comprehensive Capital Analysis and Review (CCAR) reporting requirements by either establishing internal technical teams, or outsourcing these responsibilities to third party risk management service providers.
Community banks, on the other hand, face an entirely different reality when deploying processes to measure and assess the potential impact of adverse economic scenarios on their risk profile, balance sheet performance and profitability. For example, it can be incredibly costly for these institutions to acquire the specialized software and systems required to perform credit loss estimation, budgeting and planning and support strategic planning activities. Additionally, it can be extremely difficult to get access to centralized, consolidated accounting and financial information, have the required internal analytical skills and have reporting system capabilities in place.
Modeling ‘What If?’
Despite data, systems and other resource constraints, community banks can leverage the “what if” functionality of their asset liability management (ALM) modeling tools to assess the potential impact of adverse economic scenarios on balance sheet performance. This should be a two-step process. First, define the external macro-economic conditions that could either directly or indirectly impact local economic performance, such as a global economic slowdown, geopolitical risks, health of the U.S. economy, fiscal and monetary policies. Second, describe the linkages and process of how changes in the macro-economic environment could impact the regional or local economy where the community bank operates.
Modeling a bank’s balance sheet under adverse economic scenarios is a tried and established practice that recognizes the interdependence and impact of external macro-events on the bank’s performance. This also helps a bank’s Asset and Liability Management Committee (ALCO) and management understand the assumptions and ensure the institution is taking a realistic and comprehensive view of its potential risks.
For example, consider the impact of the recent and precipitous drop in oil prices, a factor that has contributed to the slowdown of job growth in Texas and North Dakota. Previously, these regions experienced fast job growth fueled by the energy boom of recent years. More recently, however, this drop in oil prices has had an adverse impact on their local economies with ripple effects that have contributed to increases in banks’ levels of nonperforming assets, requiring higher Allowance for Loan and Lease Losses (ALLL) reserve requirements – actions that strain bank liquidity and impair a bank’s ability to grow and expand.
Along similar lines, the recent strength of the U.S. dollar is making exports more expensive, curtailing external demand and contributing to economic uncertainty in those regions with a strong export market orientation. This decline in foreign demand for manufacturing, technology or agricultural products can adversely impact balance sheet performance due to reductions in net interest margins through the indirect impact of these external, macro events.
By defining relevant macro-economic shocks (decline in GDP growth, rise in unemployment, shift in the yield curve, inflation, drop in home prices, non-maturity deposit runoff) and how they impact bank assets (loans, commercial counterparties, consumer counterparties, general collateral, fair value), the current capabilities of a bank’s ALM modeling tools can be tailored to define stress test scenarios to aid in the understanding of a bank’s balance sheet risks, risk sensitivity, bank profitability and survival.
Once the relevant macro-economic shocks and scenarios are defined, to overcome data, system and other resource constraints, community banks should consider partnering with a firm that provides ‘what if’ ALM modeling tools and risk management advisory services. These complementary capabilities can help these institutions assess the potential impact of economic shocks, for example, interest rate movements and their subsequent impacts to liquidity, core deposits and other key drivers of bank performance, such as capital. Capital optimization is a critical risk management process that helps a community bank determine the level of capital it truly needs to remain compliant with examiners (as smaller institutions generally tend to hold more capital than is necessary), which will free up working capital for alternative investments.
Additionally, by analyzing and quantifying the recoverability of their non-performing assets, community banks would be better prepared to set competitive prices for their discharged debt, providing them with an additional source of capital from an often overlooked source of funds.
Community banks may also need help measuring and modeling the behavior of their non-maturity deposits (NMDs), which play a critical role in the profitability of depository institutions. These deposits account for nearly 58% of the industry total funding, directly impacting a bank’s ability to lend, invest and drive earnings. Given the projected interest rate increase, many NMDs are in jeopardy of leaving the balance sheet, posing a serious threat to a bank’s profitability. Identifying and quantifying the rate of decay of NMDs will help measure and identify the dependability of this source of income and its impact on liquidity, interest rate risk and NIM based on a bank’s specific data.
ALM tools and balance sheet modeling capabilities such as capital optimization, NMD decay studies and distressed debt management are just a few examples of how developing a more strategic ALM modeling framework strengthens internal controls and audit processes to ensure that a bank’s input assumptions and results accurately and appropriately capture and quantify its risks – both on and off-balance sheet – mitigating potential scrutiny from examiners’ concerning the validity and relevance of stress scenario assumptions.
This level of insight also enables banks to improve communications relative to its strategic goals, balance sheet performance and risk exposures with members of its ALCO and senior managers. Taking a more customized approach to ALM modeling is necessary to help determine the effectiveness of a bank’s risk management practices and the performance of trade-offs between earnings and interest rate and liquidity risk exposures under various economic situations.
Recognizing the interdependencies between macro and regional economic factors leads to a more robust, “what if’ scenario modeling process that is better aligned to measure a bank’s balance sheet performance, earnings, loan and deposit strategies, non-maturity deposit trends, pricing, interest rate risk exposures, liquidity and funding needs.