Are You Ready to Reserve for Loan Loss?
Are You Ready to Reserve for Loan Loss?
Now that we’ve settled into a new decade, the industry will continue looking into how financial institutions will continue to place an emphasis on being more competitive. Another key trend this year will be a focus on managing risk and reserving for loan loss, especially in the wake of the new CECL standard.
It is critical that banks and credit unions leverage CECL as a competitive advantage, but the big-ticket question is, do they have the right data to proactively approach CECL to drive growth?
At this point in the game, everyone knows that CECL may affect banks and credit unions across many different areas and departments, not limiting credit modeling, regulatory capital impact, operational implications, financial reporting, technology investments and so on.
One of these changes will also be calculating the expected loss over the life of each loan and setting aside reserves to cover losses. Banks and credit unions are concerned about a significant increase in the loss allowance on their loans and the resulting impact on their net income. In fact, research from S&P Global projects that the industry may need to increase its reserve levels up to $246 billion – 1.5 times the reserve amount under the current model.
But CECL could, in fact, help banks and credit unions gain more control over their losses and minimize earnings volatility. It starts with data.
First and foremost, financial institutions must ensure they have the right data. Today, financial institutions manage their loss reserves according to a portfolio’s past performance. For example, if a bank had an auto loan portfolio with $500 million in assets and the default rate for last year’s portfolio was two percent, the loss allowance would be enough to cover a default rate of two percent, just like last year.
Under CECL, the bank must determine the potential credit loss over the life of the loan once that loan is originated. This is challenging – there are various economic factors that can impact the credit quality of a portfolio, which will impact the loss allowance and ultimately, influence the institution’s income.
Data elements, like the contractual terms and the value of a loan or credit product (variable or fixed rate) are needed – but beyond the obvious data, risk attributes and collateral information, like collateral type, amount, guarantees and risk rating, are also needed. Without it, it is impossible to truly understand the risk and how it affects their portfolio.
Second, banks and credit unions must ensure this data is accessible. Otherwise, what’s the point in having it at all? While the institution is locating the right data, it should also determine how to access it moving forward. Because this process will likely cross departments, it is crucial that banks’ and credit unions’ staffs are aware of its data governance policies – who within the institution owns it and who is responsible for ensuring it is of quality. This must be clear.
So now you have the data and know where to access it, but if the data isn’t accurate, there really isn’t any point to having it at all, right? Banks and credit unions must assess their data and establish processes to identify and correct any inaccuracies or inconsistencies.
Missing amortization schedules, paper or Excel spreadsheets, or incorrect collateral codes can all prove problematic when predicting the performance of a portfolio. Even minor inconsistencies, such as ZIP codes, which can be captured as five digits or nine, or address abbreviations like Ave. versus Avenue or Pl. versus Place, can hinder efforts. How the data is entered must be standardized, otherwise institutions will find it harder to segment portfolios.
Finally, banks and credit unions must evaluate their loan origination process to identify ways to increase efficiencies and consistencies within the process. This will ensure institutions have an accurate view of how changes in risk exposure will influence a loan’s performance.
The data points captured at the time of origination must also be consistent. To determine when there is a change in credit quality, banks and credit unions must know the quality of the credit at origination. With consistent data points, it is much easier to track when and why a portfolio’s credit quality has changed.
Similarly, underwriters must have access to relevant data to comprehensively evaluate loan applicants. With a solid understanding of how each type of loan is being decisioned and priced, there is a better understanding of which loans are driving profitability versus which loans are not.
For institutions looking to minimize income volatility and confidently drive profitability in 2020, there is a solution – data. To calculate loss estimates, banks and credit unions must have the right data, have access to it, and ensure it is of quality. Not only will this help to better manage risk, but institutions have an opportunity to improve processes, including loan origination and underwriting, all the way to marketing efforts.
Are you ready for a new decade of lending?
Posted on February 21st, 2020 at 8:43 am
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