Following the Great Recession of 2007 to 2009, which plunged financial institutions into traded and mortgage debt and ultimately into bankruptcy, most banks chose to adopt using which they could guard any of the loans that they in turn, would have extended to their customers against losses (Cohen and Edwards, 2017). The losses experienced during that period taught banks to provide funds that would cater for losses incurred from credit loans instead of awaiting trigger events that would be an indication for large losses on their part. A new accounting model was thus established in place of the previous model, commonly referred to as the “incurred loss” accounting model, which would employ the use of granular data and entail proper analysis to measure credit losses for debts and loans securities to help avoid any systemic costs as a result of delaying recognition of credit losses since results can be obtained at the highest point of granularity (Cohen and Edwards, 2017; Chinchalkar, 2016).
The Current Expected Credit Loss (CECL) model was drafted by FASB to be used to measure credit losses for debt securities and loans as a way for various financial institutions to approach credit impairment (Henkel, 2016). The model is in line with the set accounting standards and includes various methods that could be used to estimate expected losses such as loss rates, probability of default, discounted cash flow and division matrix using the loss factor. It focuses on future credit losses that could arise, in spite of the presence of any events that may trigger the losses (Cohen and Edwards, 2017). This model ensures that all financial assets that are measured at amortized costs are included in the model. Such assets include debt securities, trade receivables and loans. This way all cash flows within a financial institution are considered when estimating the possible risks that the institutions will incur. This differs from the previous accounting model that required the institution to record on a balance sheet all losses that had already been incurred. The new model ensures that any risk, whether remote or pronounced, is reflected to enable the institution to address the accounting issue resulting from this loss. Possible events that may result in losses such as loss of employment of creditor, failure of creditor to pay on the due dates stipulated in the loans agreement and a reduction in the collateral values are considered in the model since these events may result in lower or no returns to the financial institution (Cohen and Edwards, 2017). Variables also have to be put into consideration, depending on the type of credit. Loan and borrower characteristics are considered at the loan level as explanatory variables, while defaults, losses, cash flows and prepayments are considered to be performance variables. Another condition to put be into consideration when making use of this model is the heterogeneity of the characteristics. An example is the mortgage loan which offers different products such as fixed-rate and adjustable rate mortgage. In order to understand how the model works, it is also important for its operator to have information about past events, present conditions and any possible future occurrences surrounding the loan in order for them to be able to assess the possibility of collecting the cash flows that have been placed under contracts (Henkel, 2016). This action ensures that the model is efficient in establishing the amount of money will lose as a result of giving out a loan. To further increase the efficiency of the model, financial assets can be grouped into pools based on their similarities in risk characteristics (Jaju, Mohanty and Lakhe, 2009). This way, historical experiences of any financial institutions can study its past experiences with various loans, and come up with future analysis of possible losses. In such a case, credit risk can be differentiated based on the credit risk of the borrower, and different ratings provided for each loan under review (Henkel, 2016). Also, the model required that losses be estimated over lifetime of the stipulated loans and the effects of any prepayments be included and accounted for when calculating possible losses (Chinchalkar, 2016).
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The CECL is useful in terms of allowing financial institutions to provide capital that in turn, may cover them in the event of unexpected losses, especially those that are of a high risk (Cohen and Edwards, 2017). This capital is gained from loan pricing from the moment a loan is given out and supplemented with market signals in order to fully reflect credit risk. Some more capital should also be set aside by the financial institution to ensure that it covers for any deficient capital within the firm at any given time. These provisions have clear rules that help to maintain the transparency of financial institutions and ensure that the bank has a constant inflow amount. This thus reduces the effects of loaning, with the focus mainly on recession. Thus from the model, a financial institution that loans money is able to keep track of its stock measure, flow measure and market measure, through keeping track of its loan records. Study shows that financial institutions that take provisions while planning for any losses from credits has a higher risk-taking discipline (Cohen and Edwards, 2017). This means that a bank using the CECL model to study credit losses and plan for them has a higher chance of reducing leverage in the event that asset volatility increases.
Since reports have to be made from the results concluded in the CECL model, financial instruments of any firm can easily be accounted for, and updated frequently for the purposes of studying any changes that may appear in the credit risk of financial assets. The model thus allows for future credit loss events to be looked into.
Calculation of results obtained from the model occurs in three stages to establish the deterioration in credit quality. Stage 1 requires that a firm reports expected credit losses for 12 months, which are considered to be expenses, and a firm thus records its loss allowance. In cases where financial assets are involved, interest revenue is calculated on the gross amount, and this implies that no adjustment is made for the loss allowance. This is done for every subsequent year until the loan matures. Other financial institutions calculate ECL based on the understanding of probable credit loss of an asset divided by its lifetime, multiplied by probability of default occurring in the coming 12 months to obtain the results of default events that may affect a financial instrument within a period of 12 months (Cohen and Edwards, 2017). Stages 2 and 3 entail calculation of the total lifetime ECL, where assets considered to have a substantial default risk in over a year are considered. However, credits that are not considered as being low risk are moved to Stage 2, where interest earned is calculated on the gross amount lent initially to obtain the full lifetime expected credit loss. The full lifetime thus indicates the expected current value of the losses in the event that debtor defaults payment for a given period of time. In the event that credit risk has a large, notable increase, the lifetime of the credit loss is monitored with each increase. This is considered in cases where credit risk is overdue by 30 days and the risk can thus be considered either in Stage 2 or Stage 3. In Stage 3, lifetime ECL is calculated if credit quality lowers to a point where actual credit losses are obtained. Interest revenue is no longer calculated on the gross amount, but on the adjusted gross amount for loss allowance (Cohen and Edwards, 2017).
Looking at allowance for doubtful accounts for account receivables, credit risk should be established since, when a company undertakes to give goods on credit to many customers, there is the possibility that a few of the customers may not be able to pay back the full amount. This unpaid amount is regarded as doubtful account for account receivables. In such a case, establishing a credit risk allowance allows for a company to be able to realize and record the bad debt losses on the company’s financial statements. Coming up with an allowance on the credit risk from the loans ensures that a company is able to cater for risk of failure of complete payment of the credit. This is done immediately a sale is performed on credit, so as to be able to determine the amount of allowance to set for that particular credit to identify the risk related to it. However, it is clear that at this point, the actual amount for doubtful accounts for account receivables cannot be clearly estimated, and it is not easy to determine which credits will be defaulted. Therefore, estimates are used based on previous credits and on the accounting periods that the credit is required to accumulate. This estimates can be obtained by estimating the percentage that is expected not to be collected and applied to the resulting total of the sales. Another estimate can be obtained using the aging method, whereby all debts are grouped and specific periods assigned to the groups. The aggregate of all the groups gives the estimated doubtful accounts for account receivables. This way, the allowance is obtained and used to estimate credit risk for the firm.
When comparing CECL to allowance for doubtful accounts for account receivables, it is evident that both models allow for a firm to plan for credit risks so as to avoid insolvency due to lack of money and assets. These plans also help a firm to plan for any default credits without incurring huge losses. When calculating credit risk in both models, the lifetime of the credit is considered to establish its risk. In CECL, lifetime of the credit is considered in order to decide on the method to use to obtain the risk, that is, whether it should be calculated as a Stage 1 risk or as a Stage 2 or 3 risk. In allowance for doubtful accounts, the debts are grouped by age in order to obtain the aggregate that will be used to determine the risk allowance. Both consider default payments in setting their risks.
The two differ in several ways. To start with, while CECL requires that a firm looks into the past, present and future information of a credit so as to establish risk of such a credit. On the other hand, allowance for doubtful accounts focuses on recent estimates to come up with credit risks for the given credit. This makes CECL to be more accurate in its prediction of probable credit risks. CECL model ensures that borrowers satisfy the credit risk portfolio set by the company, while allowance for doubtful accounts is established based on customer frequency. The purchase of Lehman Mortgage was done at a time when the world economy was shrinking, back in 2008. The economic situation globally was shrinking and as a result, banks were experiencing slowdowns, and this resulted in reduced consumer spending (Schea, 2008). Looking at the type of asset that ABC corporation was dealing in, it can be inference that balances on mortgage loans are comparatively higher than other assets, due to the number of assorted products it offers such as loans with different terms and conditions, adjustable-rate mortgage and fixed rate loans, making it less homogenous in comparison with other assets such as credit cards and auto loans (Chinchalkar, 2016).
Had the corporation adopted the CECL model at the time of the financial crisis, then it would have been able to set aside enough capital to cover for any losses that would have been anticipated, such as in the case of the borrower not being able to pay back the money due to loss of employment. This way, the corporation would have loan reserves to help cushion it through the period of financial crisis and thus avoid huge monetary losses during the period. The reserved capital would have prevented the corporation from being pushed into insolvency as most of its mortgage assets would still be valuable at a time when other financial institutions could not value their assets as per the standards set by FASB.
The model would have allowed the corporation to keep track of every detail concerning the loans. This means that historical, current and future information regarding the loans would have been reviewed for purposes of accounting for the loans and analyzing debts. This way, any income on loans would have been recognized, and thus the company would have been able to measure their assets and thus remain competent as a financial institution at a time when ther financial institutions faced insolvency for being unable to rate their securities. Lack of details regarding the mortgage loans that they had given out, may have resulted in the corporation underwriting certain loans, even though they has lower chances of defaulted payment. This means that such loans were not well secured and as a result, there was a high likelihood for the corporation to lose money on the loans that were underwritten since such loans were not highly monitored. In reality, several underwritten loans can result in huge monetary losses. Loan defaults could also have resulted from poor monitoring of loans and this may have resulted in huge losses over a long time. At the time of the financial crisis, most banks and financial institutions that gave loans declared an end to credit risk since most borrowers ended up defaulting on payments. However, adaptation of the counterparty risk resulted in accumulation of liabilities, which resulted in uniform financial losses at the time. Adoption of the CECL model would have ensured that these defaults were considered early enough as credit losses and the corporation would have planned far ahead to avoid huge losses.
Looking at the mortgage market during the period, numerous homeowners were interested in making higher interests by increasing their gains and mortgage lenders were in a hurry to maximize loan volumes. Being a mortgage originator, it is possible that the corporation gave out mortgage loans with the hope of getting higher interests without considering some of the factors stated in the CECL model such as historical information regarding the loan and future predictions. This way toxic mortgages were highly secured without further consideration while money was lent to clients who were not worthy of the loans, resulting in huge losses. However, if the company took time to study the risks of giving out loans and took time to calculate the probable losses that would have been incurred before giving out random loans with expectations of higher interest returns, then the CECL model would have been useful in saving the corporation huge amounts of money during the period of financial recession.
References
Chinchalkar, S. (2016). Mortgage Models for CECL: A Bottom-Up Approach. Moody’s Analytics. Vol III. Retrieved from http://www.moodysanalytics.com/risk-perspectives-magazine/convergence-risk-finance-accounting-cecl/spotlight-cecl/mortgage-models-for-cecl-bottom-up-approach
Cohen, B. H., and Edwards, G. A. (2017). The New Era of Credit Loss Provisioning. Bank for International Settlements. Retrieved from http://www.bis.org/publ/qtrpdf/r_qt1703f.htm
Henkel, C. (2016). Current Expected Credit Loss: A New Impairment Model is born. Garp. Retrieved from https://www.garp.org/#!/risk-intelligence/all/all/aIZ4000000Bv07EAC
Jaju, S. B., Mohanty, R. P. and Lakhe, R. R. (2009). Towards Managing Quality Cost: A Case Study: Total Quality Management and Business Excellence, 20 (10)., 1075-1094. Retrieved from http://dx.doi.org/10.1080/14783360903247122
Schea, F.E. (2008). Tough Times, Tougher People. Strategic Finance, 90(6), 6-61.
