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    Beyond compliance: How to improve loan origination and risk capital management with CECL modeling

    Beyond compliance: How to improve loan origination and risk capital management with CECL modeling

    Meeting the requirements of CECL can also support better overall risk and capital decision-making for smaller banks—potentially providing benefits to the business that more than offset the costs of a solution. The advent of more demanding standards for credit reserves presents challenges to smaller banks. Bank executives will be looking for solutions that not only meet the new requirements but also improve business processes throughout their credit operations.


    In mid-2016, the Financial Accounting Standards Board (FASB) issued the final standard for the Current Expected Credit Loss (CECL) model, which requires banks to set aside capital based on forward-looking estimates of expected credit losses. Scheduled to take effect between 2018 and 2020, CECL will transform loan accounting for financial institutions of all sizes. Yet the burden will fall hardest on smaller institutions.

    While larger banks already have processes in place to calculate loss estimates to comply with regulations such as Basel III, smaller U.S.-based financial institutions have long relied on simpler historical models to calculate expected losses. Typically, smaller banks calculate an overall default rate using historical losses on aggregate loan pools. Bank executives then fine-tune the numbers based on their experience and judgment. Accurate expected credit loss calculations are valuable to risk managers, shareholders, and investors but improving the overall credit process along the way is a strategic opportunity.

    CECL allows an opportunity for a more granular, level of modeling. These methodologies can include discounted cash flow analysis and regression analysis, with calculations applied to individual loans to assess the probability of default and expected losses given
    default as part of loan origination. Financial institutions will have some leeway to decide how to implement CECL based on their own profiles and loan portfolios, and, as the regulations state, may do so “without undue cost or effort.” In practice, regulators will
    compare compliance costs against what a bank and its shareholders may lose in a year. That means many smaller banks may be able to choose between continuing with segment-level loss forecasts as appropriate, or be prompted to move to a more detailed analytic method.

    Executives at smaller banks have an understandable reluctance to spend money on models beyond what’s necessary. The incremental costs can be significant, especially for those that try to create their own predictive models. Banks building their own custom models are likely to incur significant costs for consultants and analysts, plus ongoing maintenance and staffing costs. While other banks cannot contemplate an in-house solution at all, so a vended solutions could provide cost effective access to the desired analytics rather than building.


    An important factor to consider is that the more detailed approach provides an opportunity to improve business processes far beyond CECL compliance. If you’re spending money on a solution that can estimate the risk associated for any loan, why use that information only for compliance?

    Loan-level risk data is valuable information. With it, loan officers can better decide whether to extend a loan and at what terms. Relationship bankers can suggest appropriate products to customers based on their risk profiles. Chief Risk Officers can see comprehensive reports on allocated and available reserve funds, helping CFOs calibrate lending policies and CEOs establish profitability goals. These risk calculations can also generate significant value during M&A activities by reducing uncertainty associated with loan portfolios. The starting point is to gain the capability to perform CECL relevant analysis based on distinct loan and borrower characteristics.

    This analysis should be applied not only to existing portfolios to serve the purposes of compliance, but also at the point of origination. Typically, banks issue new loans based on broad underwriting criteria, and only later figure out how much risk capital to set aside. Instead, banks should forecast probability of default using a CECL compliant method before issuance, so that the bank is aware in advance how much risk capital would have to be set aside to approve the loan. That way, banks can be more judicious about preserving their risk capital.

    By incorporating loan-level risk calculations into the loan origination process, financial institutions can improve overall portfolio quality, support better decision-making, and implement smarter lending practices.

    Moreover, this combination of loan-level data and economic analysis leads to ongoing profitability through more effective capital management. By taking a data-centric approach, banks can set appropriate levels of reserve capital based not just on instinct, but also to provide an auditable process that can be provided to internal and external examiners and auditors.

    Related Solutions

    Moody’s Analytics provides tools for the most crucial aspects of the expected loss impairment model, with robust solutions to aggregate data, calculate expected credit losses, and derive and report provisions.