Success story / Propensity-to-buy scoring

Propensity-to-buy scoring of clients for consumer loans offers

Identifies customers with high-propensity scores

Added business value for 70% of client base

Effective for customers with no loan history

Project Brief

Creating a prediction model aimed at identifying clients likely to take out consumer loan products and optimising campaign targeting.

Profinit’s propensity model delivered added business value for almost 70% of our client base, including challenging segments like new and inactive customers.

Peter Baláži
Head of Credit Risk, Equa bank, a.s.

The Project background

Equa bank – a fast-growing Czech challenger bank and long-term Profinit partner – asked our team to create a propensity scoring model that would help identify clients likely to apply for future consumer loan products. The project involved compiling data from the 2-year transactional history of 400,000 Equa clients as well as analysing the socio-demographic and product information available for this specific group.

The Business needs

The solution needed to meet the following business targets:

  • Improve the conversion rate of consumer-loan product offerings
  • Generate accurate prediction scores for Equa’s entire client base
  • Evaluate added business value for different client segments

The Challenge

To create a sufficiently accurate propensity model, we needed to execute a number of highly complex computations based on a huge volume of unstructured data. The task would involve processing tens of millions of transactional records and hundreds of millions of links across the client network.

To that end, we knew that employing relational databases or conventional statistical methods like regression and segmentation just wouldn’t do. The challenge required a sophisticated technical solution.

The Solution

Our unique propensity-to-buy solution was based on modelling client behaviour and social similarity networks. With these insights, we were able to identify client microsegments using advanced machine-learning methods developed in close consultation with our research partners at Charles University in Prague.


To handle the huge volume and complexity of input data, we built a big data computational pipeline using specifically designed data structures on Apache Spark and the Hadoop platform. The set of clients evaluated by the Profinit team was independently verified by data analysts at Equa bank. They confirmed the high precision of our prediction model (87% AUC). Not only that, they found that our propensity-to-buy scoring was effective across almost 70% of the client population including new customers, inactive clients and those with no previous loan history

VALUE INCREASE OF TARGETING PROFINIT MODEL TRADITIONAL MODEL 68% 1% 31% SAVERS AND LESS ACTIVE CUSTOMERS MAINSTREAM AND NEW CUSTOMERS REGULAR BORROWERS

The Tech Stack

  • Profinit propensity-to-buy lending solution
  • Hadoop
  • Apache Spark
  • Python

Project Summary

We rolled out an advanced propensity-to-buy model to enhance consumer loan uptake for our client Equa bank, delivering the following results:

  • Propensity scores computed for all bank clients
  • High-accuracy prediction model for future loan applications
  • Added business value for almost 70% of the client base as confirmed by Equa analysts
  • Applicable even for new and inactive clients without previous loan history

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

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