3 Benefits of automating loan decisions 

A bank or credit union in South Florida obviously has different real estate market considerations than one in Seattle. Similarly, every financial institution has a unique business plan and its own appetite for risk.

That’s why many financial institutions aren’t interested in an off-the-shelf solution for automating their lending processes, particularly for automating loan decisions.

LeCorgne

However, automating loan decisions – or at a minimum, using technology to assist with loan decisioning – can create some major benefits, according to Neill LeCorgne, Sageworks vice president of banking and a former bank president.

He notes that technology has evolved to allow financial institutions to customize automated loan scoring and decisions so that they can be tailored to an institution’s appetite for risk, business plan and market situation. In other words, a bank or credit union doesn’t have to use a one-size-fits-all solution.

“Loan decisioning is an art with human intervention, and it relies on the skills of approving officers,” says LeCorgne. “With an appropriate balance of technology and human oversight of both the decisioning process and final approval, automated technology-based scoring and decisioning tools can provide important benefits to an organization.”

Traditionally, community banks use a manual process for scoring and approving or declining loans. An analyst scores each loan using a matrix based on risk factors or financial metrics considered relevant by that institution for the type of loan and the institution’s credit culture and policy. The total score is converted to a loan grade and made part of a credit memo distributed to approving officer(s) for either approval or rejection. “This process can be very inefficient and slow for the borrower,” LeCorgne says.

An automated scoring system can calculate and score quantitative and qualitative risk factors, weighting each factor as needed and then aggregating all of the scores into a final score. All of the information needed for scoring is already uploaded as an automatic data feed and incorporates the institution’s own credit culture and policies. “Unique matrices can even be created for different types of loans, with weighting to reflect the importance of each risk factor in a particular matrix,” LeCorgne says. The loan decision recommendations can then be automatically generated, if desired, with documentation generated instantly as well.

LeCorgne outlines the following three benefits to automating loan decisions:

1. Automating loan decisions allows banks to provide answers to loan applicants quickly, and it improves efficiency of the entire loan decision process. Technology can score and decision all loans automatically. Or, in a more practical scenario, a financial institution might initially use automated scoring for all loans, but only use automated decisioning for loans of a certain type (smaller or simple loans, for example) so analysts can focus on larger or more complex credits. Another option would be for scoring results to be used to create different pathways for loans. For example, one pathway would accelerate the final approval of very high quality loans, another would accelerate the rejection of very weak loan requests, and a third would flag “in the middle” loans for more analysis. For performing loan renewals, such a system could enable rapid approval and processing. For larger and more complex loans, loan officers and approving officers could use scoring results as a starting point for their decisions. “In all of these situations, the result is that the applicant hears back from the financial institution more quickly,” LeCorgne says. “The financial institution staff spends less time keying in data and can spend more time on the most complex or significant credits.”
2. Automating loan decisions helps banks increase consistency in underwriting, approval and documentation that is customized to the bank’s policy. Bankers know that staff can interpret loan policy and even lending culture differently. In addition, it’s rare that a credit policy would spell out specific loan risk factors that should be included in a loan decision. As a result, consistent underwriting decisions are a challenge, particularly when loan approval officers are spread across loan and market segments. When a bank or credit union uses a customized loan-decision module for a designated loan type or size, score-based thresholds are consistently applied, sending the loan automatically down a set path toward approval, rejection or further underwriting. These practices facilitate loan review and portfolio risk management oversight.
3. Automating loan decisions provides flexibility to ramp up or pull back on loan decisions as warranted by business strategy and the business environment. As mentioned earlier, one option for a lender using automate d decisioning technology is to designate certain types of loans or scenarios for yes/no pathways to accelerate decisions, or to outline specific loan scenarios for automatic approval or declination. As the economy changes, or as a financial institution’s strategy or risk appetite changes, it can dial up or dial down its loan risk easily by revising the selection of loan risk factors, adjusting the weighting of risk factors and changing the final score thresholds that feed into the decisioning process. “If an institution anticipates or experiences a decline in the local market economy or real estate values decline, the scoring can be adjusted for each potential path of consideration – yes, no or maybe – and that can automatically impact the approval processes,” LeCorgne says. “This may help an institution dial down risk in anticipation of a negative trend or event.”

Fully automated loan decisions will not be appropriate for every loan at every financial institution. Banks and credit unions have too many factors affecting their business plans and risk appetites to use a one-size-fits-all approach.

However, LeCorgne says, “Every financial institution can benefit from automating more of the loan scoring and decisioning process, especially since they can embed their own credit culture and policy into an automated module in much the same way these are embedded into the manual processes currently in use.”