Leveraging Machine Learning for Micro Finance Collections
A loan passes through various stages or events from the moment it is given till the time it is repaid. Collection strategy of a loan for any financial institution is as important as its lending strategy and delays in repayments not only impacts the financer’s books, it also impacts the borrower as it is also reflected in their credit history SHG / JLG Collections A SHG (self help group) is a community based group with 5-20 members. Micro Finance Institutions typically offer group loans and individual loans that have standardized repayment structure. The repayment cycle could be weekly, monthly or fortnightly depending on the scheme and institutions. In a typical collection process either an MFI agent visits the borrower to collect the repayment in cash or the borrower walks to a physical branch to make the payments. How Data Science Helps Predictive analytics plays a key role in coming up with behavioral patterns to determine whether a customer is likely to default. A right collection model can be a driving factor behind a product’s collection efficiency. Simple classification model or a scorecard can be trained on the past data to help the collection team to identify the chunk of customers in the current portfolio who display a similar pattern to the ones who defaulted in the same product in the past. It can help the collection team to put more focus on these customers and align their efforts accordingly. This model will run at the start of every collection cycle and its frequency will be similar to the repayment frequency of the customer(weekly, monthly or fortnightly). The two major category of variables that can be used to identify this pattern are: Credit Bureau Data Looking at customer’s credit data tells us the customer’s current market activity and his/her past credit history. Major variables that can help us identify our customer’s potential credit default include: Customer’s Internal Performance Data You also have repayment history of the customer with you. It can also be broken down into three types of variables Machine learning algorithms are fed all of this data from which they learn and create predictions. These algorithms can extract linear and nonlinear patterns in the data which will be difficult for a human(Collection team) to see. A multivariate machine learning model with hundreds of features can easily outperform a univariate rule based collection strategy. Use of applied machine learning can not only give you better results but also a clear interpretability and deeper insights for business to make better decisions. With the help of predictive analytics in collections MFIs can maintain good clean books and can aim to achieve higher profitability.
