Success story / Competitor loans consolidation

Competitor loans consolidation for Raiffeisenbank

Detect customers’ competitor loans

2x loans detected

Generate leads on a daily basis

Project Brief

Applying advanced data analytics
and machine learning to
detect outside loan payments

As a part of the solution, Profinit implemented a tool for automatic detection of external loans. This instalment detection tool uncovers customers’ obligations to other banking and non-banking institutions, which brings valuable information for possible offers of loan consolidation, better-targeted marketing and more accurate credit risk scoring.

Client’s comments

Project background

Our client, Raiffeisenbank CZ, offers an outstanding online service and is continuously pushing out new products to meet customer needs and expectations.

The bank was keen to maintain its position in the industry, so we looked at how we could use state-of-the-art technology to help them.

Working alongside our client, we asked the question, “How can we use this collected data fully, in a way that enables us to offer bank customers an even better service?” Our answer came via applying advanced data analytics and machine-learning methods to the problem – enabling us to make competitive loan consolidation offers to the right customers.

Business needs

The solution needed to meet the following specifications:

  • Identify clients with competitor loans for targeted marketing campaigns focused on consolidation
  • More accurate assessment of customers’ credit risk scoring
  • Adding data science tools to bank infrastructure and setting up a big data processing pipeline
  • High-performance technology to promptly process clients’ transactions without delays


Detecting loan instalments paid to other lenders, within customer transactional data, involves executing complex computations over hundreds of millions of records on a daily basis. A robust big data pipeline for high parallel data processing is needed, as well as the inclusion of suitable data science tools and methodology.

It is cutting-edge work. In fact, this project was the very first implementation of this kind into the bank environment, without any existing technological or architecture blueprint.


We designed a complex processing pipeline, implemented on a local Hadoop cluster, including data science tools such as Apache Spark, Hive and Jupyter. In order to identify customers with loans elsewhere, we applied our instalment detection tool.

Using an instalment detection tool

The tool processes customers’ banking transactions and related data. It’s calibrated specifically to automatically detect loan instalments for each customer. The model is based on advanced statistical and machine-learning methods such as Multi-layer Bayesian Networks. Implementation into the big data pipeline means it can handle processing huge volumes of transactional data – even billions of records on a daily basis.


Tech stack

Apache Spark

Project Summary

The solution we designed and implemented has achieved these results for the bank:

  • The new solution can detect twice as many competitor loans as the former one.
  • A new big data pipeline now processes billions of transactions daily.
  • Daily leads for loan consolidation offers and better campaign targeting.
  • Information about new loans elsewhere improves credit risk management of debtors.

Would your bank benefit from accessing this cutting-edge technology?

Let us show you how Profinit can improve the way you use and access data within your organisation…

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