Business shutdowns, PPP loans, historic unemployment levels, CECL regulations — characterizing the banking industry as stressed out is an understatement. Global disruption has negatively affected financial services companies’ – and their customers’ – revenue and exposed their portfolios to credit risk.
To cope with these risks, financial services companies need to deploy proactive, data-driven strategies and align their people, policies and procedures, and technology to execute their plans.
Your bank’s data is a powerful tool that your credit managers and loan review teams need in order to mitigate credit risk. Banks collect and analyze an increasingly large amount of data. In fact, your bank likely has expanded its data collection in response to the CECL standard.
Effectively using data can help improve your bank’s credit risk management. This data – which can include location codes, NAICS codes, peer and industry data, borrower credit quality indicators, updated appraisal values, and loan income characteristics – equips your bank with valuable insights.
If there is no comparable history – a paused economy and a momentous, almost overnight, unemployment jump – modeling a like situation is tough. In the absence of history, current insights are key. Your bank should stay vigilant and pay special attention to loans with risk rating updates on hold because of a deferral. You also should conduct regular sensitivity analysis and stress testing to understand your models’ response to recent changes in macroeconomic variables.
Credit risk managers and loan review teams can benefit from loan-level analysis. Loan-level valuations allow the bank to apply assumptions dynamically based on the characteristics of each loan, such as the probability of default by product type, sector, current payment status, and prepayments linked to interest rates.
Begin to purposefully sample loans with modifications and produce targeted, loan-level valuations. Flag loans that soon could see a grade change. Your assumptions about these loans can reflect historical trends, but place extra focus on pockets of your portfolio that currently are in deferral or fall in problem sectors, like retail, hospitality, and oil and gas.
Credit marks from loan-level valuations (which represent life-of-loan losses) also can be compared to life-of-loan loss estimates calculated under the new CECL standard. This analysis can help your loan review teams identify problem loans and discuss with the credit department proactive strategies to get them back on track.
Successful credit risk management requires banks to efficiently capture, manage, and use data. Improved data management and automation can help your bank better use its data to address growing and changing credit risk management needs. Plus, better data usage in one area can lead to improvements in another. For example, loan review results can feed into CECL and loan valuation models, and forecasted cash flows from loan-level valuations can feed bottom-up stress-testing models.
Alternatively, relying on manual processes reduces the impact your loan review department has on overall credit risk management. You don’t want your loan review department spending its time entering data, tracking progress, and producing reports. You need that team to prioritize potential credit issues, control breakdowns, and risk mitigation strategies.
But while automation offers considerable benefits, technology alone might not fully address your bank’s needs. You need a scalable, dynamic solution that offers automation and analytics capabilities based on a foundation of trusted data to help your bank confidently respond to rapidly changing credit risks and regulations.
Banks should retain as much data as possible to help with credit risk management. They also need to implement and follow a robust data governance program that supports accurate, complete, timely, and easily accessible data.
Incomplete and ungoverned data ultimately can produce flawed outputs and decisions. Similarly, missing data, coding errors, and other data issues can produce a distorted view of your bank’s risk and lead you in the wrong direction. If your data is going to help and not harm your efforts, it needs to be right.
A mature data governance program, based on institutionalized processes, standards, organizational roles, and technology makes your data more reliable. It also supports strong financial reporting systems.
