Effective Monitoring and proactive Delinquency Management Platform
Banking services are looking for the ways to improve the efficiency of their operations. One of the areas is the management of Pre-delinquency and Delinquency management. More Specifically the use of aggressive and well planned collection of debts and loss mitigations campaign.

After the recession the lending market is characterized by rapid consolidation and large economic scale for making an ideal market for automated scoring tools.

In the area of automated scoring and analytics Nucleus Software has come up with a solution "FinnOne^TM Forecaster PDM” for our existing as well as new clients.

Delinquency Tracking or Default Tracking or Bad Loan Monitoring are different names for the same purpose i.e. tracking the banks/other lending institution’s assets under doubt. FinnOne^TM Forecaster can be used as a powerful tool to predict the “Probability of Default for a loan borrower” and also the “Probable Movement of the Default Bucket for a Loan Borrower”. FinnOne Forecaster can use the historical demographical and repayment behavioral data to predict the two most important information required by a financial institution to devise an effective default tracking mechanism and thereby reducing the financial institution’s asserts risk of becoming delinquent.

There are primarily two areas where FinnOne^TM Forecaster enables you to effectively devise a strategy with a set of action plans for handling each type of default customers:

• The first objective is to identify the set of customers in pre-delinquency stage, which are likely to get delinquent. For this the attributes chosen for building up the model include more of the behavior in nature and some repayment attributes as well. FinnOne Forecaster will be able to predict which customer set (which is still not moved to delinquency but are most likely to move to the delinquency bucket) in the coming months. With this information the financial institution can define a preventive action plan like early calling, additional camps for receipt of payments on holidays, proactive calling for construction period installment in case of mortgage loans and so on.
• The second objective is to identify the bucket movement among the customers, which are already delinquent. For this the behavior attribute is given almost equal importance than the repayment attributes. The risk class/delinquency bucket for the customer in the coming months is predicted so that an effective tracking and recovery strategy can be made for each customer segment.