Banks commit considerable effort to the task of determining who gets what loan and under what conditions. The goals are to ensure that the process of loan determination is uniform and that it complies with both regulations and bank lending policy. This is why most banks have consolidated their lending departments, and why they use analytics software to assist loan officers in the loan determination process.
In fact, loan "decisioning" software was one of the first "smart analytics" solutions to be deployed in banks and credit unions. For over 20 years, it has delivered consistent results and value. The software, of course, isn’t any smarter than the lending guidelines that banks and credit unions program into it, but what it does guarantee is a uniform administration of lending, no matter which loan officer is using it.
Loan decision-making software operates on business rules defined by lending managers. Will loans be limited to Class A borrowers with great credit histories and low risk? Or will loans also be extended to lower-quality borrowers -- at higher interest rates to compensate the financial institution for the higher risk?
Lately, these lending guidelines have been tight, and lending officers have been operating at low risk. They don't use their own personal judgment when assessing loan requests; they simply run the analytics and come back with a "yes" or "no." Financial institutions also use this software to run pre-approvals on loan and credit card offerings -- and there are some cases where online lending companies actually use the automated decisioning software to approve loans on the spot.
Yet how does this well established analytics system fit within the the framework of long-term customer service? Automated decision-making will consistently give great service to "A" borrowers with all kinds of financial assets and a stellar borrowing record that goes on for years. However the software may give inconsistent, less than stellar service to customers who are just starting out on their first job and are struggling to establish credit.
Banks and credit unions used to aggressively address this younger segment of their customer and member bases by reaching out to the "un-banked" -- lower-income people who still paid all of their bills -- and to the younger people who were just starting out. However in the wake of an economic bust that was largely caused by loose lending, these outreach efforts have stopped. This has brought on conservative lending practices and left behind many worthwhile borrowers and small businesses.
One key way to rebuild the economy may well be to augment recent policy so that people early in their financial and credit careers can obtain loans. Borrowers could start building great credit records, and lending institutions could get renewed opportunities to build their customer and member bases for the future. (The average credit union member today is already in his mid-forties, so new customers are essential.)
So perhaps it is time for financial institutions to alter the policies that their decision-making software is using and decrease their reliance upon the software. Certainly this software will continue to mitigate the risk of lending to unproven groups of borrowers, but it is not a complete substitute for policy, strategy, and customer service.