The financial services industry has witnessed mergers and acquisitions of the country’s largest banks, the collapse of some of the most trusted financial icons, the mortgage meltdown and government bailouts - namely TARP. All of this has taken place in the past 18 month.
Rising unemployment and the collapse of the housing industry have increased the number of accounts going to collections and pushed banks to focus more resources in this area. Many of these customers were viewed at origination as prime borrowers, but are now headed toward default and possible bankruptcy.
Because of this shift in the volume of collections and the uncertainty of being able to identify customers at risk, there is more at stake than just money. Banks are facing a choice to close accounts and decrease credit lines on a very broad range of consumers, which comes with the risk of alienating the bank’s best customers that are now in that same group, or do nothing and continue to have unacceptable losses.
A better option is to view the customer across the entire credit lifecycle, taking into consideration all of their relationships with the bank, and use that data to drive more intelligent analytics and segmentation. If banks can accomplish this, customers can be given the most appropriate treatment—the top customers receive the best offers and service and those that pose a default risk can be dealt with quickly and effectively.
Credit risk needs to be handled at an enterprise level across the entire lifecycle so banks have a better view of the customer as a whole and all areas can benefit from the enhanced data and intelligence. In doing so, banks can minimize the risk of default and loss while improving service and retaining their best customers.
Historically banks have been organized in silos by lines of business. This further exacerbates the banks’ ability to deal with their customers from a holistic point of view. Breaking down silos has not only been a problem politically, but system limitations and legacy technology reinforce boundaries between departments. Because a bank’s structure is a multi-dimensional matrix, this adds even more complexity. There are different channels for interacting with customers (i.e., mobile, branch, online, call center, etc.), different lines of business and different phases of consumer life cycles. It is imperative to get a consistent picture of a person across all of these dimensions and take advantage of every piece of data and every interaction to mitigate problems.
Managing collections is just one more reason to move toward an enterprise view of a bank’s relationships with its customers. For example, the bank’s credit card division may attempt to cross-sell an additional product to an existing customer and realize through that process that the consumer is no longer a good credit risk. With a siloed approach, the bank’s mortgage department may continue to extend credit on a home equity line, missing signs of early default and increasing the bank’s risk of loss.
If there was an enterprise view of the customer across these lines of business, the bank would have known at the time of the failed credit card prescreen that the consumer was in trouble and action should be taken.
It isn’t simply about increasing communication across all divisions and functions within the bank. You must also have a common set of data attributes across all lines of business and divisions, from origination to collections, so everyone is talking the same language. If the collections team has a 30- day past due attribute that is different than the one used in origination you can’t use them together to analyze behavior and trends and improve the bank’s ability to identify troubled accounts early and take appropriate action.
The reality in collections is the sooner you can identify the problem the more likely you are to get paid. If you are the last creditor to find out a customer is in trouble somebody else is going to get their money before you. It’s not just about how many dollars you can bring in, but it is about how much you are spending through your collections process to garner those funds.
Collections shouldn’t be about damage control. Through a more analytical and automated approach you can manage your collections portfolio more efficiently and cost-effectively. While you can’t eliminate the problem you can minimize its impact by taking the right actions at the right time.
Banks already use intelligent automation in the account opening process to determine what types of offers should be made to each customer and the likelihood those offers will be accepted. Collections and portfolio management groups can utilize the same type of propensity modeling and scoring to take a more analytical approach to their functions.
There is a lot of information available at the bank, demographically and in the credit file to start mapping behavior and assist with segmenting borrowers, such as total revolving debt, income estimators, geographic areas with high unemployment, and consumer use of pay day loan services. All of this data can be used to prioritize and adjust your collections efforts to maximize their effectiveness.
Segmentation helps you decide where to focus your energy and where your return is most likely going to come from. For example, an auto worker who gets laid off in Detroit is going to have a harder time finding new employment and more likely to go into bankruptcy, no matter what you do to help him and how easy he is to work with.
On the other hand, an out of work nurse from the same area is likely to find employment due to a nationwide shortage in this profession and thus a better target for assigning resources. You can also employ performance simulation to better understand what drove certain individuals to collections and how they behaved once they got into trouble. This testing will help determine what will be the most effective collection methodologies for these individuals, who will be most likely to pay and who you need to foreclose on or repossess immediately.
Over the years, bank portfolios have remained relatively static and they did not have a need for methodologies to manage unpredictable and rapidly changing markets. Now there is a much broader spectrum of people whose credit is shifting rapidly and banks don’t have the systems to handle this.
While cost is always a factor, when you see a prime customer with an excellent payment history and credit score getting a pay day loan, and recognize this as an indicator they are in trouble, you’ll be glad you made the investment. Employing automation and analytics is going to be far less expensive than the collections losses banks will face by not taking immediate action.
Tom Johnson is SVP of Product Development at Zoot Enterprises. He is a featured speaker at the 14th Annual National Collections & Credit Risk Conference to be held March 21-23 in MIami.
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