Credit Scoring Model
Increased competition and growing pressures for revenue generation have led credit-granting
and other financial institutions to search for more effective ways to attract new
credit worthy customers, and at the same time, keep risk at tolerable levels and
control losses. Aggressive marketing efforts have resulted in a deeper penetration
of the risk pool of potential customers, and the need to process them rapidly and
effectively has led to growing automation of the credit, insurance application and
adjudication process. At the customer management level, companies are striving ever
harder to keep their existing clients by offering them additional products and enhanced
services. Risk managers are called on to help in selecting the low risk customers
for such treatments. On the other hand, for the customers who have negative behavior
patterns (criminal record, fraud, non-payment), strategies need to be devised not
only to identify them but also to deal with them in an effective manner to minimize
further loss and recoup any monies owed, as quickly as possible.
This has led to the increased demand for automated Credit Scoring to assist organizations
in such day-to-day decision-making and also ensure wise decisions from a strategic
perspective. Risk scorecards offer a powerful, empirically derived solution to business
needs for Credit Scoring. Risk scorecards have been used by a variety of industries
for predicting delinquency. It also curtails fraud activities via increased transparency
and accountability, and helps in recovering claims. Scoring methodology offers an
objective way to assess risk, and also a consistent approach, provided that system
overrides are kept to a minimum.
Our specialized Credit Scoring consultants come with a wealth of experience in understanding
the nature of each business and selecting the right set of variables to ensure effective
credit scoring models to facilitate the organization in its operations. This is
accompanied by experienced statisticians and modelers specialized in ensuring a
statistically sound and theoretically correct model is produced. Key deliverables
include:
