Largest Scale : ICICI Bank : Pre Delinquency Management

Last year's economic slowdown had an adverse effect on everyone. Customers of

credit cards and loans from ICICI Bank were also impacted by the then prevailing

economic landscape. This resulted in a customer's inability to pay the dues to

the bank, which in turn directly influenced the performance of retail assets

business of the bank. Since the customer's income was stressed from various

payment commitments, the bank had to ensure that the dues towards the bank get

paid on priority. Especially, the unsecured product dues required a proactive

approach of contacting the customers. If a customer is not able to pay the dues

for a period, he ends up becoming delinquent and the bank's losses increase. The

economic scenario had led the Bank to focus towards two challenges: first, to

recover the maximum amount of outstanding from delinquent customers and second,

to minimize future delinquency. The bank already had a debt service management

function that takes care of the recovery of non-paid dues from delinquent

customers. To minimize delinquency it was required to identify the stressed

customers at an early stage. This in turn created the need of data driven

categorization of customers on basis of their probability to default. The bank

intended to initiate a comprehensive pre-delinquency management program (PDM).

The aim of this pre-delinquency program was to minimize losses arising out of

currently non-delinquent customers.

The process of identification of probable customers to default was done in

the data warehouse where customer's history was mapped against certain

parameters and behavioral patterns. The customers who are identified by the

system as probable to default are also evaluated on a risk scale score and this

whole process is automated. The score acted as an indicator of the customer's

propensity to default in near future. Once a customer has been identified, the

PDM system itself generates offers that can be presented to the customer and

also finds out the complete contact details of the customer from various

direct-indirect channels, IVR system, internet banking, POD from courier etc.

The system can map a customer across all relationships with the bank and produce

contact details. A credit card customer could also have a savings account with

the bank, the system can identify a customer and his relationships across

different services with the bank and then find out all contact points to get in

touch with him. Considering that ICICI Bank has over 3 million customers under

credit cards and loan products, and an overall customer base of 20 million for

their retail products, it can be envisaged how complex the system would be to

not only predict customers who would default based on their history but also to

map the customer across all relationships with the bank and know his complete

contact details.

The entire process of identification of the customer for PDM is automated.

The PDM generated offer is directly uploaded in the calling system database

which is used by the auto dialer to assign the calls. The technology platform

used for PDM includes Sybase IQ as Enterprise Data warehouse, SAS suite as

Enterprise Reporting Tool and SAS e-miner and Text Miner for analytics, Business

Objects for enhanced reporting, etc.

With PDM implementation the roll forward rate of customers reduced from 14%

to 6 % resulting in credit loss savings. For instance, if the roll rate for year

2008-09 was 100, then post deployment the roll rate for year 2009-10 came down

to 55. Due to process automation also the bank witnessed cost reduction by

manpower savings of more than 80 man hours per month. The Bank's customers also

benefited as they were given flexible offers of paying back in time of economic

stress.