Largest Scale : ICICI Bank : Pre Delinquency Management

Best Green IT Project : State Bank of India : Green IT@SBI

Maximum Social Impact : Eko India Financial Services : SimpliBank

Runner-up : e-Gov Projects : Govt of India : Automation of Central Excise & Service Tax [ACES]

Best Automation Project : Punjab National Bank : CBS@RRB

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.

Largest Scale

Company Scenario
Before Deployment
  • Due to economic slowdown bank's customers were facing pressure in paying off their EMIs. .
  • A delinquent customer reduces bank's profitability and increases NPAs.
What was deployed


Pre-Delinquency Management system deployed that monitors over 30 Million customers' behavior to predict probability of defaulting customer.

After Deployment  
Roll forward rate of customers reduced from 14% to 6 %, resulting in significant credit loss savings. o The entire process from identification of a 'likely to be delinquent' customer and taking remedial measures has been automated.  

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.

Ashish Singhal, DGM - ICICI Bank

What has been the overall impact of this project?
Major benefits are in form of loss reduction where roll forward rate of customers reduced from 14% to 6 % resulting in credit loss savings. The productivity gain has been from cost reduction by man power savings (80 man hours saved) as a result of process automation. TAT reduction of 3 days due to smooth and timely flow of data to end users. The contact details of the customer are enhanced using the data stewardship covering details from various direct-indirect channels like IVR system, Internet banking, POD from courier etc, and across various relationships that the customer has with the bank.

What according to you sets this project apart from any other in its class?
There are two main USPs of this project, namely establishing synergies within various complex technologies and key resources. There are many different technologies being used like Sybase IQ as an enterprise data warehouse solution,  SAS suite as an enterprise reporting tool, SAS e-miner and Text Miner for analytics, Model Builder, which is an advanced analytics & predictive modeling tool; UNICA as a Campaign Management & Tracking Tool, response modeling, Clementine Data clean for Data Quality and Consolidation and Application Business Objects for enhanced reporting. The team that worked on this project is from diverse backgrounds and has brought immense knowledge and expertise.

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.

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