Combine non-traditional data sources with machine learning to create long-lasting customer relationships

The availability of new data sources creates enormous potential for developing predictive credit risk management models which go far beyond 'traditional' internal and credit bureau data.

We have already invested in this area, developing extensive knowledge, experience and established a track-record in delivering big data, data science, advanced analytics and smart credit solutions, in numerous countries across the globe.

Projects integrated with the traditional tools have quickly delivered increased benefits into the different areas of the credit lifecycle - from marketing, to credit risk, collection, customer retention and fraud.

We've briefly outlined a suite of advanced analytics solutions that use non-traditional data sources, including contact centre voice calls, transactional credit card information and unstructured web data, combined with machine learning techniques, to successfully deliver origination, customer insights, collections, regulatory and fraud prevention solutions.

We help you to:

  • create predictive credit risk management models which go far beyond ‘traditional’ internal and credit bureau data
  • extract value from non-traditional data sources (e.g. contact centre voice calls, transactional credit card information and unstructured web data)

How we do it:

Voice of Customer Analytics - drives value from new data sources (dedicated to company that uses multiple customer communication channels)

Transactional Data Insights - extracts value from credit and debit card transactions

Web Data Analytics assess customer behaviour through on-line presence measurement

Automatic System for Fraud Insights is a complete solution boosting fraud detection with machine learning techniques.

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