How to put your Data into Action within your Debt Management Strategy

How to put your Data into Action within your Debt Management Strategy

Financial institutions find themselves in an incredibly challenging market as consumer debt has been steadily rising since 2013.

Temenos – Company

Financial institutions find themselves in an incredibly challenging market as consumer debt has been steadily rising since 2013. According to the latest Household Debt and Credit Report, released by the Federal Reserve Bank of New York, the total debt balance as of Q4 of 2017 stood at just above $13 trillion. This marks the fifth consecutive year of annual growth. If you couple the overall consumer debt statistics with delinquency rates that are continuing to rise we have an uptick in collections activity and a potential threat to your portfolio health.

In response, many financial institutions are looking to shore up a comprehensive collection strategy which can greatly improve collections efforts, keep down costs and increase repayment returns/higher collections rates directly improving the financial institutions bottom line and profitability.

Data analytics is widely popular across areas of the financial services, but largely ignored when it comes to debt collection strategies. Small and big data in the collections world can help to refocus efforts and improve efficiency. But what are the best practices for a practical application of data analytics and is there value in a flexible technology solution to support data driven collection efforts? First, let’s take a look at the numbers…

In December, Urban Institute released their survey findings on Debt in America. As you can see, there is a good amount of the darker blue all over the map.

Breaking down the numbers, Urban Institute’s research found that 33% of Americans hold debt that is currently in collections. Higher rates can be found in specific demographic and geographic areas with some states like Louisiana and Oklahoma in the low to mid 40% range. Also derived from this study, the median amount of that collectable debt sits at an average of $1,450.

As the numbers show a trend toward higher delinquency rates, this is forcing many in leadership to put the pressure on departments that are responsible for mitigating loss and leading collection efforts, challenging you to rethink and innovate your collection strategy.

What can data analytics do for you?

One strategy to consider is incorporating data analytics as an additional layer into your debt management strategy. Data analytics comes in many forms. Applying this data to collections efforts is a huge and largely uncharted area with great potential for return. A robust collections analytics program can look different to each financial institution, but ideally the focus is to repoint efforts to the right account, at the right time, to the right individual. In my experience, the three most impactful benefits many financial institutions see after implementing data analytics in their strategy are:

  • Single out self-cures – Singling out self-cures can filter your focus to the accounts that really need that human touch.
  • Manage roll rates – Reducing the roll rates and stopping the delinquencies before moving to late stage, mitigating that risk early on is another benefit.
  • Identify early placement – The third point will also help narrow your list to spearhead accounts most likely to pay and remove the least likely for early placement so you’re not wasting energy. For example, you may have a goal to remove non-collectable accounts from your plate. Analytics can help to identify what these accounts are and export them to a third party agency.

Key considerations:

The process to include data analytics, predictive models and scorecards includes several steps and thorough evaluation, review, change and implementation. Here are some key considerations when tackling this initiative:

  1. Start small. You don’t have to go all out with huge operational and technical goals that put a burden on IT and take a long time to implement. Basic data structuring and scorecards can provide more than you are working with today. I like to term this “small data”. Its impact can be underestimated, but it has proven to be a stepping stone to big data dreams. Small data can be accessed from many sources but reports are the simplest avenue to obtaining this data in a readable, exportable format. One recommendation is to analyze accounts delinquent in the past 24 months then look at their historical payment patterns. I have seen financial institutions discover extreme value in this subset of data. For example, one financial institution found a pattern across a specific demographic showing that they might not pay on their scheduled due date but they almost always paid within a five-day window. This information (as simple as it was) helped them to refocus and repoint collections efforts so they were not wasting their time on accounts that were likely to pay based on historical data.
  2. Pick and choose. Do not skimp on details and scenarios, get specific. You have a potentially large demographic of account holders and they all will not fit into the same scenario. Create a visual map that records all scenarios and goals. Then pick and choose the highest yielding and most impactful and start there. This means white-boarding sessions, inclusion of employees with a fresh view of the data and lots of coffee.
  3. Have a plan to track, report and run quality assurance as risk based assessments. This ensures that you are measuring how these strategies are influencing your operational results and meeting (or not meeting) objectives and goals. You may find that something is not currently working that was performing great six months ago. You may need to tweak the dataset or move towards a more sophisticated program with algorithms. None of this can be discovered unless you are tracking and measuring results.

Internal and cultural perception may be your biggest challenge when moving to incorporate data analytics into your collection strategy. Most times within an organization there is a need to prove that pertinent, correct data can be built into the best strategies. Collections operations can sometimes be viewed as a “cost of doing business” area, but with a combination of creative approaches, innovative data analytics and technology capabilities, opportunity can be found where it was once nonexistent. And, you can contribute to the effort to be a sustainable financial services organization.