How to Embrace Analytics and Collect More Money

If “Moneyball” taught us anything, it’s that data and statistics can provide insights to help anyone in any industry. Collecting debt is no different. What was once an industry focused on trying to make as many calls as possible because more calls meant more collections is now an industry that uses analytics and data to identify the accounts that are most likely to pay. While nobody will argue there is still room for art in the process of collecting debts, science is now playing a more important role than ever before. Ask anyone in the industry if they were starting a collection operation from scratch how they would build their company, and analysts and technology experts would be hired before collectors every time. 

By identifying the importance of data analysts and making investments in technology like machine learning and artificial intelligence, companies can supercharge their efforts to identify the accounts most likely to pay. For example, predictive analytics in debt collection can take into account a wide array of variables, such as:

• • Payment history: How consistently has the debtor made payments in the past?
  • Credit score fluctuations: Have there been recent changes that might indicate financial instability or improvement?
  • Communication patterns: Are there trends in how and when the debtor responds to outreach efforts?

By examining these and other factors, machine learning algorithms can assign a probability score to each account, indicating the likelihood that the debtor will make a payment. This allows collection agencies to focus their efforts on the accounts most likely to result in successful recoveries, optimizing both time and resources.

If access to that type of data is not available, looking at more fundamental metrics, like the amount of the debt, whether the individual has had any accounts in collections before, and the age of the debt can also provide a foundation to predict which accounts are more likely to be responsive to your communications. 

The Moneyball Approach

The shift from experience-based decision-making to data-driven strategies can be compared to the transformation seen in professional sports, famously illustrated by the Moneyball approach in baseball. Just as the Oakland A's used analytics to identify undervalued players who could help them win games, collection agencies are now using machine learning for debt recovery to identify accounts that are "undervalued" by traditional metrics but hold significant potential for payment.

This shift represents a broader trend in many industries, where science is increasingly replacing art. In the past, a collector's "gut feeling" might have been the deciding factor in which accounts to prioritize. Today, sophisticated algorithms are making those decisions more precise and reliable.

Actionable Insights for Implementing Analytics in Collections:

• • Start with Clean Data: Ensure your data is accurate, up-to-date, and comprehensive. Quality data is the foundation of effective analytics.
  • Embrace Machine Learning: Invest in tools that can learn and adapt from outcomes, continuously improving prediction accuracy.
  • Integrate Multiple Data Sources: Combine internal data with external sources like credit bureaus, public records, and economic indicators for a more holistic view.
  • Focus on Actionable Insights: Don't just collect data; ensure your analytics provide clear, actionable recommendations for your collection teams.
  • Continuously Test and Refine: Regularly compare predictions against actual outcomes and refine your models accordingly.

The shift towards data-driven decision-making in debt collection is not just a trend or a fad; it's the evolution of collecting debts from consumers. By embracing predictive analytics in debt collection, companies can not only improve their recovery rates, but also enhance customer relationships and operational efficiency.

As we navigate this new landscape, it's crucial to remember that technology should complement, not replace, human judgment. The most successful collection strategies will be those that blend cutting-edge analytics with empathetic human interaction. The Oakland A’s didn’t get rid of their scouts when they implemented their new system; they just understood the role they are playing is different and a new approach was needed. The same is true for your organization. Are you ready?