Debt Management or Collection Analytics helps the debt collector or debt recovery processes to understand the behavior of customers, predicting their behavior after defaulting and prioritizes their collection activities to maximize their recoveries and reduce cost.
With the increasing competition in the lending business and services sector, collection functions of all companies across the financial business spectrum are feeling pressure to cut cost and become agile in adapting the changes. Even governments across the geographies are tightening the legislations to monitor those collection agencies from going overboard in collection efforts. The onus is on the collection functions of these companies to adapt to the changing market and legislative environment to increase efficiency and cut the cost. The application of analytics especially predictive analytics helps the companies to understand the causes of default and best way to maximize the collection at optimum cost.
It is always better to understand the type and reason of delinquency from historic data and act proactively on the accounts showing similar type of characteristics. Based on the typical behavior, we at ScoreData divide customers into different segment and recommend differential treatment to each segment.
Traditionally collection process starts when the customers default in their regular payment. Usually different companies have different criteria to move customer from regular process to collection. But the customers start showing the sign of risk of slipping into arrears much before actually turning delinquent. Identification and treatment of such account is very sensitive as the customer has done nothing contractually wrong. This stage in collection is referred as Pre Delinquency Period. The focus in pre delinquency is the prevention of default. Secondly when a customer is actually delinquent and in early to medium stages (it varies from company to company), the focus of collection process is to find methods to make customer repay his outstanding arrears. This is referred as early vintage collection. When customer starts to ignore the request of repayment and becomes increasingly delinquent then the third and final stage is reached. This is referred as Late Vintage Collection portfolio. After this the company books the account as loss or charge-off/write-off. The business objective of each stage of collection is different.
Broadly analytical techniques that are used in predictive analytics can be divided into regression techniques and machine learning and regression techniques. Regression techniques includes linear regression, logistics regression , Discrete choice models, probit regression, time series models , survival or duration analysis etc. Machine learning includes neural network, MLP(multi layer perceptrons, Radial based functions, Naïve bayes etc.
The analytics solution of ScoreData lays emphasis on treating the customers at different delinquency level differently. Our predictive solutions segment the customers using their demographics, collection data based on their risk profile but also do analysis to find the most suitable treatment for each segment. We use predictive analytics to make accurate estimates of a customer's propensity to repay, as well as the likely amount that the customer will repay. Our collections models help distinguish between self-cures and potential long term delinquent accounts only to maximize the collection from the delinquent accounts while preserving valuable customer relationship.