How to Improve Collection Performance Using Predictive Analysis? - Blog
Debt collection has been prevalent since the early days of banking. It requires pursuing payments of debts that have been owed by individuals or businesses. The traditional debt collection process is governed by standardized scripts and involves multiple layers of case handling. In cases where a delinquent debt is not collectible by the lending organization, they have to hire a third party collector to take over.
Working of the Traditional Collection Process
In traditional collections processes, customers are segregated into a few simple risk categories based on delinquency buckets or simple analytics and are assigned teams accordingly. Low-risk customers are usually allotted to newer collection agents based on their availability. These agents follow standardized scripts without being asked to evaluate customer behavior. Agents with moderate experience, training, and skills are assigned to medium-risk customers. They are trained to assess customer behavior based on ability and willingness to pay. High-risk customers are assigned to the most skilled agents, who own their accounts and use less-standardized approaches to assess customer behavior.
As debt complexity rise, however, institutions are beginning to feel the need for more efficient and effective collections operations. With so many different types of loans, amounts, and individuals, it is impractical to measure each case with the same cup. Hence, to avoid losses and gain better control, lending institutions are increasingly adopting the power of advanced decision analytics and machine learning to transform the way collections work. These powerful digital innovations are remodeling collections operations, helping to improve performance at a lower cost. Using advanced analytics, banks can move to a deeper, more nuanced understanding of their customers. Here are 3 key factors to consider using predictive analytics in banking:
Bring Collection Cost into Control:
Predictive models can help forecast which cases are more likely to pay. These cases can then be allotted to the ‘in house’ collectors instead of wasting their time on difficult cases. This will also increase their productivity. While those cases which have a less probability of paying back on time can be forwarded to the collection agencies or they can be considered for alternative treatments. Segregation of cases helps you significantly lower your collection cost and save time.
Develop a Scorecard
A collection scorecard statistically determines the debtor’s propensity to pay and thus helps to define what actions should be done to increase collections, thereby facilitating debt management decisions. For example, using a collection scorecard, lenders can identify those customers who require less interaction, or contact, to prompt payment. This gives you the space to focus on the individuals who actually need further contact or support to bring their accounts up-to-date. CRIF, one of the leading credit information companies in India, has developed a collections scorecard algorithm that observes past behavior to identify risky customers. This is followed by taking appropriate action to protect the business and avoid undue expenses. Collection scorecards help minimize credit losses and improve profitability.
Bring a Steady Improvement
Analytics uses a feedback technique called “test and learn” where you can assign different strategies for different cases and run a test. For example, you can run 70% of your cases under existing collection strategies, 20% cases under X alternate strategy and 10% of cases under Y alternate strategy. With all other factors kept constant, the analytics can then measure the impact of the changes from each strategy. This accurate comparison will allow you to continually improve your operations. You can also be able to give different treatment to different debtors giving you a more customized approach.
Predictive Analytics opens greater avenues for lending agencies to make objective decisions and achieve a higher level of organizational performance.