CASE STUDY

Soft-collection Remastering in a Bank

32% Faster Bad Debts Processing in 6 Months

Project Essence

As a result of the Soft-collection stage renewal, the following tasks were completed by our team:

Implementation of new processes and tools for Soft-collection.
Development of a mobile application for field collectors.
Development and deployment of new strategies for FICO® Blaze Advisor.
Building and implementation of debtor notification system via Interactive Voice Responder (IVR).

In 6 months our client has started processing bad debts 32% faster.

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About the client

Our client – further referred to as the Bank – specializes in retail lending. The Bank is a significant player in the commercial retail banking sector in Central Europe. Its main operational field includes retail lending, credit card issuance, and deposits.

Our client cooperates with many retail chains, has about 150 branches, and is represented in over 100,000 points of sales.

Project background

The Bank has already had debt collection management software implemented. However, when the Bank’s management got in contact with our team, the necessity to upgrade was vital. The existing debt collection management software has become a bottleneck both in accuracy and performance.

The Bank already had software solution from FICO onboard, and its functions satisfied the Bank’s requirements. That’s why it was decided to use a new analogous FICO product as a basis for the new debt-collection automation technology.

As RNDpoint is a partner of FICO (since 2014), the vendor recommended our team and gave excellent references.

Challenge

After a series of meetings with the Bank’s representatives, RNDpoint’s specialists discovered other flaws in the existing debt management system in the Bank:

Outdated, unsupported technological platform Capstone Decision Accelerator.
Out-of-date and unstable front-end technology.
Low performance of the existing debt management platform and its incapability for scaling.
Manual processing of information from Data Warehouse (DWH).
A number of non-automated business processes in the collection department.

The requirements of the Bank were coupled with a strict deadline. Development and implementation processes were to be done in 6 months.

Solution

In the negotiations, the Bank and RNDpoint agreed that it is necessary to realize the functions from the existing system in the new one. In addition, the new solution should be more customizable and productive.

After 6 months of hard and thorough work from both sides, the final result was reached. It included:

Implementation of new processes and tools for Soft-collection.
Development and execution of a mobile application for field collectors.
Designing and deployment of new necessary strategies for FICO Blaze Advisor.
Building and implementation of debtor notification system via Interactive Voice Response (IVR) system.
Deployment of appreciations for:
- planning meetings with clients at the Bank’s offices;
- working with document scans archives;
- validation of inputting data.

Technologies and Tools

BRMS, System orchestration, Backend development, DWH, FICO Blaze Advisor, FICO Application Studio, Java, Oracle.

Result

After launching the new system, the Bank recorded a significant improvement in debt collection effectiveness.

The Bank started to use the system immediately due to its stability and reliability.
After 6 months of the new system exploitation, an automated debt management platform allowed the processing of toxic assets 32% faster.
The Bank decided to use RNDpoint’s services for the renovation of the Hard-collection stage.

With every implemented iteration, RNDpoint’s cooperation with the Bank was taken to the next level of trust. The Bank has become one of our largest clients.

Speed

32%

problematic debts processed 32% faster

Time to market

2 Months

the 1st stable product delivered in 2 months

Experience

+1 Challenge

the customer returned to us with new challenges

Hosting behavioural scorecards in BRMS without IT department

Decision support system based on Big Data