Analytics
Analytics provides organizations with better visibility into the factors that drive revenues, costs, and shareholder value. Today’s business challenges demand timely financial information that enables executives, managers, and front-line employees to make better decisions, take action, and correct problems before they affect the company’s financial performance.
Analytics provide insight to the people who can impact business performance.
Collections + Analytics = Improved Recovery.
Collection Analytic is nothing but an intelligent analysis, using combination of mathematical and statistical tools, to arrive at a “behavioral scoring”, depicting accurate picture of the customer’s propensity and ability to pay, which is used to segment defaulters, prioritize collections activities to maximize recoveries and reduce collections costs.
Effective collection analytics empower the collection staff to focus on the right debtors, maximizing the payments collected by a combination of segmentation, scorecards, and strategies to help manage delinquent accounts better.
Collections Analytics segments and identifies accounts representing those that self-cure, those that cure with engagement, and those that will improve, remain stable, or grow more delinquent. Using Collections Analytics you can assign treatments and protocols to each of your segments depending on the account’s recovery score, outstanding balance, and balance age. This ensures collection teams are distinguishing well-intended, “accidental” debtors from those under economic hardship or potential cases of actual fraud. Strategies can be interfaces with client interaction further enhancing productivity by leveraging and optimizing investments in existing systems and providing a seamless integration to tools already being used to help collection teams.
Why collection analytic?
Because it increases amount of collection and more importantly reduces the losses by 1-2%. This translates into almost the existing profit of many well-run companies; will company not like to double its profit?
Predictive models
Predictive models analyze past performance to assess how likely a customer is to exhibit a specific behavior in the future in order to improve collection effectiveness. Many types of models are available and uniformly, all of them deduce the analysis to a scoring. A good scoring model requires varied data accurately and on timely manner.
Data is at the heart of everything. With expertise in the interpretation and use of credit bureau, clients’ customer data and customer contact history, Analytics turns this data into information, which enables organizations to predict how defaulters will behave in the debt collections process.
Do Indian companies require them? Yes, banks and big finance companies certainly require. But, are they ready? One is not sure.
Very few banks and lenders have applications data, credit bureau data, credit scores, and collateral details electronically. Some do not have a dedicated debt management system. Migration or pulling data from one to another system creates its own set of problems.
This apart, hardly any bank or lender has captured and recorded customer contact/ interaction history systematically and digitally. Without this crucial information, no predictive model implemented will churn out any meaningful results. It would take a few years more, provided companies realize the value and importance of recording customer contact/ interaction history.
Predictive models are of no use to Indian banks and lenders, in near term.
Descriptive models
This may be appropriate for India
I High Delinquent - High Risk
II High Delinquent - Low Risk
III Low Delinquent - High Risk
IV Low Delinquent - Low Risk
Additionally, we may use six sigma concept to further identify high risk accounts within the groups based on payment pattern falling outside six sigma (standard deviation). This would help rank-order the cases within the groups or in the collection lists.
We will discuss the level of effort, type of effort, timing of effort and the right strategy for each the above groups in future blogs.
