Every business sector is looking at infusing business analytics into operations to enable faster, smarter decisions, and financial services is no exception. For decades, banks have used "smart" software that watches for aberrations in credit card usage patterns to detect potential fraud, terrorist "watch list" applications that look for individuals who withdraw and deposit large sums, and loan-decisioning software that takes a look at the entire debt, asset, and income pictures of loan applicants, and then determines whether they should be given a loan, for how much, and on what terms. Like ATM machines, these tools have stood the test of time, and they continue to prove their worth every day.
These tools carry with them important lessons that IT managers in financial services are wise to heed as they move forward with new business analytics.
Fit for purpose solutions. Applications monitor activity and issue an alert for unusual card usage patterns. Credit card issuer employees know that, when such an alert occurs, they investigate the situation and immediately notify the card owner. These applications carry significant benefits for early fraud detection: collectively, billions of dollars in losses can be avoided and claims against the fraud insurance policies banks carry reduced. These applications aren't tasked with multiple tasks: They monitor patterns and report, do it quickly and well, and are left alone while other applications are created for additional functions.
Policy assistance. Loan officers and managers are evaluated on how well they manage the loan portfolios they create and on the return on these portfolios. The major revenue source for banks is the income gained from what they pay to borrow money from the Federal Reserve or other sources, and the interest rate that they can charge in the form of consumer and business loans. The higher the risk of default on the loan, the higher the interest rate that they demand for the loan. Like grocery stores that anticipate a certain amount of food spoilage, banks also realize that part of the cost of doing business is loss from bad loans. They balance this against the profit opportunities they obtain from the majority of their loans.
Accordingly, banks grade loans into A, B, C, and D paper. The lending department determines the mix of the loan portfolio, and in these economic times, there are many institutions that are choosing to only grant loans to individuals or businesses in the "A" category. The loan-decisioning software is what executes the grading process for loans. It does this by comparing the consumer's or business's financial profile against the institution's established lending guidelines for A, B, C, and D paper. As with credit card monitor applications, this app is not called on for more than its original design. It may feed information to other applications, but its analytics are left alone to do their one job well.
Anxiety removal. The entire loan-decisioning process comes together on the loan officer's desk. Based on what the analytics tell him, the loan officer either recommends approval or denial of the loan, and sets the terms of the note. This is reviewed and approved by the loan manager, and it's of course open to a manual override if lending chooses to do that. The key here is that lending standards are being met -- and properly-designed software removes the anxiety of making the decisions from the loan officer.
Easy to use. Card usage pattern analysis and decisioning for lending are integral to the processes they support and are easy to use. This is because everyone using them understands their role and purpose in the business processes they support. In general, the team that builds the application understands the business and the process that must be supported.
The financial services industry will continue to move more business analytics into operations for purposes of loan portfolio risk analysis, customer demographics, and tailored product offerings, sales prompts for tellers, and even overall financial education for staff. As they continue with analytics, it will serve them well to take a look at some of the great analytics successes in the industry that have been returning value to the business for years -- and why they work so well.