The banking industry has stared hard at its customer data "dots" to analyze customer behavior, and it has learned valuable lessons for other industries that use data mining. Although banks have employed statistical analysis tools with some success for several years, previously unseen patterns of customer behavior are now coming into clear focus with the aid of new data mining tools.
Data mining is the automated analysis of large data sets to find patterns and trends that might otherwise go undiscovered. By studying these patterns and trends, banking executives can predict with increasing precision how customers will react to interest rate adjustments, which customers will be most receptive to new product offers, which customers present the highest risk for defaulting on a loan and how to make each customer relationship more profitable.

Many financial institutions use data mining to study the needs and habits of customer groups in the interest of "customer relationship management." The objective is to increase the amount of business with each customer. Banks regularly take aim at that goal with targeted promotional mailings and in the normal course of customer interactions; however, data mining helps them market more precisely, saving money on mailings and increasing the effectiveness of cross-selling efforts.

Fleet recently used data mining to identify the best prospects for its mutual fund offerings. Grossman's team mined customer demographics and account data including transaction activity and account balances along several product lines. From that analysis, they found customers who were likely to invest in mutual funds, and they used that information to help Fleet's Investment Services division target prospective clients.

Some 13 million customers call Bank of America's West Coast customer service call center each month. It's an unbeatable marketing opportunity. So when a customer calls, a rep has a much better chance of cross-selling if he knows what accounts the customer holds and whether the customer is part of a group with a propensity to buy a particular product.

At Bank of America, customer service representatives equipped with customer profiles gleaned from data mining pitch new products and services that are the most relevant to callers. For example, a customer in a certain age group who has children and a home equity loan with the bank is a good candidate for taking out a student loan. Data mining helps the bank identify such customers.

Bank of Montreal mines its 1-terabyte "customer knowledge database" to develop profitability profiles of customers based on multiple factors such as the amount of money in particular accounts, demographic information, the number of monthly transactions and their choice of banking channels (teller, ATM or phone). The Toronto-based bank calculates current profitability profiles of households and produces models to predict the profitability of customers over a lifetime.

In addition to helping increase the value of customer relationships, mining customer information databases aids banks in managing risk. Bank of Montreal, for example, analyzes mortgage customers' transactions in checking, savings and other accounts for insight into who is at risk of defaulting. The bank was surprised to find that some customers who consistently made their mortgage payments late were not necessarily at a high risk of defaulting. The bank found that a certain type of customer is in the habit of paying bills late but has the wherewithal to fulfill his or her obligations.

By analyzing the transactional behavior of customers across all their accounts, the bank can see which customers experience periodic cash flow crunches and which may truly be in danger of defaulting.

Some banks are currently testing data mining tools to manage their credit portfolios more efficiently. Data mining holds great promise in assessing the risk of a bank's entire portfolio of loans.By analyzing customer behaviors such as payment habits, data mining can provide answers to vital questions such as What percentage of loans will be refinanced next quarter? What percentage will go to foreclosure? and What percentage will be in serious delinquent status? Accurate answers to these questions allow credit risk managers to allocate optimal loan loss reserves--funds set aside to cover bad loans--which is important to profitability.

Credit risk assessment is a new area for data mining. For example, Bank of America built a data mining model to predict attrition of small business customers. One of the key factors was the length of time small businesses held accounts with the bank. That indicator proved to be misleading, however, because about 60 percent of small businesses go bankrupt within three years. Bank of America's model was a better indicator of companies headed for bankruptcy rather than those headed to a rival bank. The bank subsequently revised the model to eliminate that factor.