How to predict the expected value of a recovered debt thanks to Artificial Intelligence

Companies that deal with debt collection can have different types of objectives, some related to the quantity of recoveries, others related to the quality, that is the value of the recovered amount. The first, as we have seen in this article, refers to how many positions you are able to recover in a given period of time, while the second, which we will cover today, refers to the overall value recovered from the debts treated.


Each debt differs from others according to its economic value and the profile of the debtor. Setting up a campaign focused on the total economic value of the recovered debts therefore requires a very complex analysis, because it presupposes a double level of study which, in addition to each debtor’s propensity to pay off their debt, also contemplates the expected value of each debt.


In a Phone Collection campaign, for example, one could find oneself faced with a group of debtors who, despite having a similar propensity to pay off, have different values ​​to be recovered. In order to be able to recover the highest possible value, the company carrying out the campaign should decide to focus on those debtors who not only have a high propensity to repay their debt, but also whose expected recovery value is higher.


The alternative, as well as the processing method often used by companies that currently deal with Debt Collection, is to proceed with randomly contacting of one’s list of debtors, hoping that in the end it will be possible to reach the economic objective. In this way, however, time and resources are used without correct criteria and at the risk of being wasted.


To be able to make the most of company resources, it is necessary to rely on new technologies, such as Artificial Intelligence.


Thanks to it, it becomes possible to carry out an in-depth analysis of the debtor’s propensity to pay and the expected value of each recovery in a very short time, thus allowing a company to set the best strategy to achieve their objective.




But how is it possible to implement Artificial Intelligence in the analysis of the debtor list?


With BigProfiles, the first AI platform designed for Debt Management, it becomes possible to analyse both debts and debtors in depth and decide to focus strategies on those whose recovery will actually lead the company to reach its target. All in just a few clicks and without any coding knowledge.


Would you like to know more? Request a demo from our site.