Our CECL Resource Center includes information on implementing the new standard, including the advantages and disadvantages of the modeling techniques that can be used and the data you should be collecting now.
Implementing CECL
CECL requires a financial institution to recognize an allowance for expected life of loan credit losses on day one. CECL represents a significant change in the way financial institutions currently estimate credit losses because it eliminates the probability threshold. As a result, financial institutions will have to revise their current loss estimate models and, in many cases, implement new models. Whatever methodology is used, the standard requires that the loss estimate be based on current and forecasted economic conditions. This white paper describes how financial institutions can benefit by utilizing modeling and financial concepts developed by other industries as they implement CECL. Examples include using discounted cash flow modeling techniques long used by the asset-backed securities industry and static pool analyses used in the insurance field. We conclude by showing how few financial institutions will have sufficient losses to be statistically valid and the importance of supplementing their performance with industry-wide data. This white paper is available below:
Data Collection for CECL
CECL allows financial institution to calculate the allowance in a variety of ways including discounted cash flow, loss rates, roll-rates, and probability of default analyses. Wilary Winn believes that the information financial institutions should collect as they work to implement CECL is primarily dependent on:
- The financial institution’s lending strategies and the type of loans being assessed
- The credit risk model the financial institution plans to use
We believe that financial institutions will utilize more than one methodology based on loan type and that the type of information required to develop assumption inputs depends on the model(s) it plans to select. We detail the advantages and disadvantages of the various models that can be utilized and why we selected discounted cash flow models for residential real estate and consumer loans. We describe how to form predictive data pools and the pluses and minuses of increasing granularity. In addition to models and pool formation, we will discuss the macroeconomic data to collect including where to find it and the advantages and disadvantages of various sources including cost and data limitations. This white paper is available below:
The Business Case for CECL – Capital Stress Testing and ALM
While it is imperative to understand lifetime credit losses in conjunction with the accounting requirements of CECL, this paper makes the business case for an organization to forecast lifetime credit losses by demonstrating the numerous advantages of adopting this approach within the asset liability management framework. While financial institutions must begin to prepare now in order to become compliant with the required CECL provisions by 2021, we show an effective implementation of CECL can lead to better integration of credit risk analysis across the organization and why we believe the best managed institutions will actively include credit risk management and lending personnel in their calculations. We detail how measuring credit, interest rate risk and return on an integrated basis under multiple potential macroeconomic environments can lead to better decision making, increased profitability, and more effective allocation of capital. This white paper is available below:
Making the Business Case for the CECL Approach – Part I
The Business Case for CECL – Concentration Risk
This paper demonstrates how financial institutions can appreciably enhance their concentration risk management by prospectively considering how credit risk changes as economic conditions change and quantifying the potential effects on its capital. By better understanding the main drivers of lifetime credit losses a financial institution can develop robust quantitative concentration limits and sub-limits leading to improved Concentration Risk Policies and credit risk management. The white papers are available below: