Categorization & Analytics

  • Categorization Engine

  • Open Banking Scoring & Financial KPIS

  • Early Warning & KYC

CATEGORIZATION & ADVANCED KPIS

CRIF Categorization Engine is designed to gain insights on your customer base by leveraging on the accounts and credit cards transactional data.

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

HOW WE HELPED OUR CLIENTS UNLOCK THEIR POTENTIAL

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.

for a Tier-1 Bank, and leading financial institution

Categorization & Custom Early Warnings

An Italian Tier-1 Lender needed a better scoring model, time to market, and digital customer profiling

  • Solutions they Adopted
  • Transactions categorization model running for whole customer base
  • Integration of the CRIF Advanced Analytics model
  • Integration of specific early warnings impacting on active customers monitoring and risk management
  • 50 %

    Increase in KYC procedures and automatic applications

  • 3 x

    Marketing campaign redemption compared to traditional approach

For Tier 1 Lender

RISK MANAGEMENT REQUIREMENTS

A Tier 1 Lender wanted to strengthen the existing scoring model by further refining the medium risk level, Improve KYC and reduce risk of loss, Improve active customer profiling and define risk avoidance plans

  • Transaction categorization model for the whole customer base
  • Integration of the CRIF Score into the lender’s scoring model
  • Integration of KPIs impacting the upgrade or downgrade
  • 92 %

    Categorization Accuracy

  • >50 %

    GINI Credit Score of current acount transaction data

GET IN TOUCH WITH THE SALES TEAM

What does the next digital journey of your business look like? Let's find out together.