CRIF Categorization Engine, providing insights about your
customer base by leveraging account transaction data.CATCH is powered by a proprietary categorization algorithm based on machine learning and artificial intelligence that turns unstructured data into structured insights
- CATEGORIZATION ENGINE
CRIF has implemented a proprietary categorization algorithm, which allows the classification of current account transactions into different categories
- ADVANCED KPIs
CRIF has developed an analytics suite of structured insights from unstructured data. Insights and KPIs are then integrated to assess the customer portfolio in terms of risk monitoring as well as KYC and cross-/upselling strategies.
- SCORE
CRIF Score is entirely based on current account information and on the categorization of banking descriptions performed by ML and developed using Advanced Analytics techniques.
CATEGORIZATION & ANALYTICS KEY BENEFITS
- Improved profiling processes
- Powerful set of insights
- Improved active customer management
- Evaluation of new-to-credit & new-to-country subjects
- Reduced risk of loss
- Risk mitigation
- Improved customer knowledge
- Growth in business opportunities
- Differentiated and targeted marketing intelligence
- Cross-/upselling strategies and plans
CATEGORIZATION & ANALYTICS MAIN FEATURES
- KPIs calculation
- Score calculation
- Account transaction categorization
- Cash flow Indicators
- Customer portfolio risk monitoring
- Customer portfolio profiling
- Marketing intelligence insights
THE EVOLUTION OF EARLY WARNING
The objective of an Early Warning system is to anticipate the signs of deterioration of the most fragile loans: all exposures that do not yet present objective evidence of deterioration, but which are close to a significant increase in risk, must enter the Early Warning scope.
NON-TRADITIONAL DATA: CONTRIBUTION TO EARLY WARNING
- The state-of-the-art of Early Warning models in banks uses so-called "traditional" information sets, e.g., quick and easy data sources with a low impact in terms of costs and resources (e.g., customer behavior)
- Over the years, CRIF has gained significant experience in evaluating the contribution to Early Warning systems of "non-traditional" data sources, in particular Credit Bureau data and Current Account data in its most granular form (individual transactions), appropriately categorized
- "Non-traditional" data sources allow the already good performance of models based only on "traditional" data to be improved. Overall, there was an increase of up to 10 points (GINI Index) thanks to alternative data sources
