An overwhelming majority of organizations are still basing collection strategies on age and value of invoices. According to an FIS study, 41 percent of credit and collections professionals are using age and 35 percent are using value of invoices. Even more astonishing is the number of collectors that spend time contacting customers that would have paid even without any contact at all. The primary reason for this is that collection teams are mostly incented on a percent current goal. Human nature is to develop habits to ensure you hit targets. Whether the target is every month, quarter or year, one target can change behavior. Teams spend precious collection time attempting to get a $500,000 payment one day early, just to bump the percent current metric. The expense of spending time on this one payment are the hundreds of $1,000 payments that collectors can never seem to get to.
The irony is that the $500,000 payment is typically with a very well-established company that bears little risk of not paying. The smaller invoices are generally with customers who are borderline credit worthy and have a greater potential of not paying.
Let’s put this into practical terms. If I were to offer you two options: option one is a choice of guaranteed payment of $10,000 today plus $500,000 tomorrow or option two, which is receiving a payment of just $500,000 today, which would you take? Evidently, 99.9 percent of you would take option one. So, why are collection teams designed to take option two and potentially forego the additional $10,000?
I’m not suggesting that percent current is not a valid measurement of collection team success. On the contrary, it is a very important health measurement of any collection team. However, if risk is incorporated into the strategies employed, write-offs and concessions will be minimized while driving percent current past the plateau that inevitably happens with focusing on age and value.
Artificial intelligence (AI) can be leveraged to inject risk measurements into collection strategies. AI uses machine learning to monitor customer payment patterns. Sudden increases in disputed items or short payments can be an indication of increasing customer risk. Many other factors are included in the complex algorithms that an AI engine calculates over many thousands of scenarios to determine the most effective cumulative approach for any portfolio, at any given time.
As risk factors are monitored, any triggering events or combination of events will automatically adjust collection procedures. For example, the standard collection stage for a specific customer may be to email a reminder of invoices coming due, five days before the due date. However, by monitoring multiple risk factors, AI methods have the ability to automatically move this customer up in the collector’s queue to receive a phone call today.
Collectors do not have the time or expertise to effectively prioritize their collection queue based on constantly changing risk factors. Introducing an AI review that scores the risk of every customer within a portfolio, eliminates the needless contact to customers that pay like clockwork. It also frees collectors from the administrative burden of prioritizing and allows them to contact the right customer, at the right time, via the right method.
True AI is the next step in improving credit and collection results. Your teams will be freed up to execute the most effective strategies without the burden of prioritizing or the administrative research that is required via other processes. Deviations in conditions are handled automatically and without delay to ensure continued success. Help your teams reach greater results by leveraging AI in your collections process. What is learned through the AI engine today, will improve the results of tomorrow.