Customer Managment (TAG)

Portfolio Risk

Marketing Campaigns &
Upselling strategies

Early Warnings

Limit Change


The practice of knowing your portfolio better to take strategic actions towards risk control, portfolio growth, gaining competitive advantage, and moving towards operational efficiency. Of the 50 largest global banks, three out of four now pledge themselves to some form of customer-experience transformation.

Success Stories

Discover how FinTech rediscovered opportunities within their existing data
Discover how some of the world's most important companies improved their performance and defined a brand-new digital path, both for them and for their clients.

Bank in the GCC

New Tool to Increase your Conversion Rate and Optimize Cost by Targeting Right Clients

The bank wanted to consider credit risk to propensity score to narrow down on the right pool of customers to target for cross sell. The business challenge was to targer the right customers before the competition does.

Solution Adopted

  • Prediction of likelihood of a customer taking a Personal Loan in short to medium term
  • Helped segregating portfolio into propensity categories which can help your marketing for more focused targeting
  • PPS is developed using machine learning algorithm on CRIF’s bureau database, the largest credit bureau database in India
  • Delivery with the possibility of an additional score when a portfolio scrub is done with CRIF



Were initially excluded by Bureau exclusion rules

34% propensity rate for PL

was observed on avg. out of the remaining 98% after initial exclusion

52 -1%

portfolios across very high – to – low propensity likelihood were segregated with CRIF PPS score


were scoring very high in terms of propensity score as of 52%


reduction in past due amounts


reduction in marketing costs


reduction in standard turn around time

Limit Change
Churn & Retention
Customer Value / Lifetime Value

Client level behavioral data
Third party data Account transactional data
CRM / collection workflow data
Predictive models with different targets
Customer Lifetime Value estimation
Segmentation into customer clusters
KPIs to feed BI and reporting suites
Storage of historical data snapshots
Decision engine to compute business rules and scorecards
Full integration with origination systems
Action-Reaction mechanism (champion-challenger)
KPIs tracking and comparison
Data driven decisioning

Technology is so powerful right now and offers many solutions to support financial systems undergoing radical change.

That’s why we want financial institutions to discover our idea of a future packed with opportunities that we already provide.