In less than two years, the financial world witnessed the collapse of some of the most trusted industry icons, mergers and acquisitions of many of the largest banks and government bailouts - namely TARP.
Combined with rising unemployment and the housing market's troubles, and accounts going to collections soared. Banks have been pushed to focus more resources in the area. Many customers in default were viewed at the time of origination as prime borrowers.
Because of the shift in the volume of collections and the uncertainty behind identifying customers at risk, there is more at stake than just money. Banks face a choice between closing accounts and decreasing credit lines - options that carry the risk of alienating the best customers. Or, they can do nothing and continue to take on losses.
A better option could be to starting viewing customers across the entire credit lifecycle, considering all of their relationships with the bank, and then use that data to drive more intelligent analytics and segmentation. If banks can do this, customers can be given the most appropriate treatment. Top customers might receive the best offers and service and those that pose a default risk can be dealt with quickly.
The Complete Cycle
Credit risk needs to be handled across the entire lifecycle so banks have a better view of the customer as a whole and all areas that can benefit from 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 can exacerbate 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 critical 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 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 but also about having a common set of data attributes across all lines of business - from origination to collections - so that everyone is talking the same language. If the collection team has a 30-day past due attribute that is different than the one used in origination, they can’t be used together to analyze behavior and trends and improve the bank’s ability to identify troubled accounts early and take appropriate action.
In collections, the reality is the sooner the problem is identified, the better. The last creditor to find out a customer is in trouble is going to lose out. It’s not just about how many dollars are brought in, but about how much money and time is spent in the collection process to garner those funds.
Collections shouldn’t be about damage control. Through a more analytical and automated approach companies can manage a portfolio more efficiently and cost-effectively.
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 use 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. The data points can include total revolving debt, income estimators, geographic areas with high unemployment, and consumer use of payday loan services.
All of this data can be used to rank and adjust collection efforts. An auto worker who is laid off in Detroit is going to have a harder time finding new employment and more likely to go into bankruptcy, for example. Conversely, an unemployed nurse from the same area is likely to find employment because of a nationwide shortage in that profession - and thus becomes a better target for assigning resources.
Companies also can employ performance simulation to fully understand what drove certain individuals to collections and how they behaved once they got into trouble. The testing will help determine what will be the most effective collection methodologies to apply.
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 a prime customer with an excellent payment history and credit score gets a payday loan, it's a clear sign of trouble. Employing automation and analytics will be far less expensive than the collection losses faced by not taking immediate action.
Tom Johnson is SVP of Product Development at Zoot Enterprises. He will be speaking at the 14th Annual National Collections & Credit Risk Conference next month in MIami.