RA Business Solutions Blog Post

Allowance for Loan and Lease Losses

The Allowance for Loan and Lease Losses (ALLL) is commonly the largest estimate on a financial institution’s statement of financial condition. The incurred loss model is currently used by institutions to estimate the required ALLL necessary to cover anticipated losses. The incurred loss model utilizes several different components including: 1. Reserves for homogenous loan pools based on historical loss data. 2. Reserves for non-homogenous impaired loans. 3. Reserves for troubled debt restructurings (TDRs) based on discounted cash flow calculations or a fair value assessment of the collateral less the costs to sell the asset. 4. Reserves for qualitative and environmental factors relevant to the particular institution. The incurred loss model generally delays recognition of credit losses until the loss is considered “probable.” The model also considers losses that have been incurred and will most likely be reflected as charge-offs during the next operating cycle (typically 12-months after the reporting date). The Financial Accounting Standards Board (FASB) believes that financial statement users desire transparency with regard to management’s full estimate of all expected credit losses, and the new proposed model would provide users with a consistent balance sheet objective. CECL is an estimate of the present value of future cash flows not expected to be collected based on quantitative and qualitative information such as: • past events • historical loss experience • current economic conditions • borrower credit worthiness • forecasts of expected credit losses • current point and forecast direction of the economic cycle. FASB expects that an institution’s estimate of expected credit losses will be largely formed by historical loss information for financial assets of similar type and credit risk. The expected credit loss estimate represents the life of a loan and considers prepayment, collateral value, current and expected economic conditions. CECL will reflect the time value of money either explicitly through the discounted cash flow model calculating present value of future cash flows discounted at the instrument’s effective interest rate, or implicitly through loss statistics based on a ratio of amortized cost written off due to credit losses to the amortized cost of the asset at the reporting date. Financial institutions would adopt CECL by posting a cumulative-effect adjustment to their statement of financial condition (undivided earnings) as of the beginning of the first reporting period in which the guidance is effective. However, it’s important to note that the CECL guidance has not been issued and financial institutions are not permitted to increase their ALLL in anticipation of the CECL model guidance. Likewise, financial institutions should not underfund their ALLL estimate with the goal of increasing net income and recording the CECL adjustment through undivided earnings if the guidance is ultimately accepted. The industry expectation is that a final standard will be issued by the end of 2015 with an expected effective date of 2019. In the meantime, subscribe to our communications at www.claconnect. com to stay current on CECL including implementation guidance. (Source: A.J. Eschle, CPA, Manager, Financial Institutions. andrew.eschle@claconnect.com or 703-825-2109) The Top Five Predictors of Subprime Risk Most of the conversation around automotive finance is currently focused on the growth of originations to consumers with subprime credit scores, but there is more remarkable growth in orginations made to consumers who do not have a credit score at all. As seen in Chart 1, subprime originations (designated by credit scores between 550 and 619) increased 2.16 percent from 2013 to 2014. Growth was even higher in the deep subprime segment (designated by credit scores below 550), with originations increasing 2.9 percent from 2013 to 2014. Yet the group that grew the most were consumers with no score at all, with originations growing 7.89 percent from 2013 to 2014. Chart 1 These originations are not only growing in number, but also performing quite well. Chart 2 displays subprime auto delinquency rates from 2006 to 2015. In the last five years, both the number of delinquent subprime accounts and the amount of balances owed have decreased overall, with that trend looking to continue in 2015. Chart 2 So how can subprime originations be growing and performing well, especially among consumers with no credit scores? The answer is that lenders are starting to leverage nontraditional financial attributes that are often more predictive for the subprime segment as well as consumers without a traditional credit history. In the past, these attributes were used anecdotally and reliant on information that consumers shared willingly with lenders. Moreover, it took time for consumers to hunt for their latest pay stub to prove they currently had a job and stable income. This ultimately led to delayed or derailed sales opportunities, which are loselose situations for all parties involved. Now lenders have access to alternative risk scores and databases of comprehensive financial information. Many of these emerging databases are more than a simple pooling of data sourced from different companies and public records, with data providers and consumer reporting agencies going a step further to generate state-of-the-art risk models to analyze information about subprime borrowers. These models are the result of analyzing financial characteristics that have been prioritized by statistical algorithms. Using these databases and algorithms can reveal that different individuals who have the same subprime credit scores may actually have entirely different financial situations. A growing number of lenders are looking at these alternative attributes to find subprime borrowers similar to the second individual in the example above – individuals who are rebuilding their credit history after hard times to demonstrate they are more likely to remain current on an auto loan. These alternative databases can be a goldmine of information, and lenders may be surprised at which financial attributes are the most predictive at assessing the risk of a potential borrower. Some of the most important financial attributes identified by these databases and algorithms include: • Size of Delinquent Telco and Utility Balances: Individuals having larger telecommunications or utility balances tend to be a greater risk for auto lenders. This is particularly true for Thin File individuals or those with a bankruptcy on file • Presence of an Involuntary Disconnection: Individuals who have had their utilities, cell phones, cable service or other telco or utility service disconnected due to nonpayment represent greater risk for lenders. • Number of Address Changes: Individuals who have changed their physical address multiple times represent greater risk for lenders. These attributes are only the tip of the iceberg – there is a wealth of alternative data that can provide lenders with the insight they need to formulate a more comprehensive evaluation of consumers in the subprime market. With automotive sales remaining strong, lenders can use these resources to quickly and efficiently assess applicants, communicate with their partners and help close more deals. (Source: Equifax. A version of this article ran in the July-August edition of Non-Prime Times)

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