Customer Management

Categorization Engine

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

What is CRIF Categorization Engine?

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.

Key Benefits

Deeper customer understanding

Focused on gaining a richer, data‑driven view of customer behaviors, needs, and financial patterns.
Stronger risk assessment and mitigation

Platform’s ability to assess risk more accurately, identify vulnerabilities early, and support safer decision‑making even for thin‑file customers.
Enhanced customer management and portfolio performance

The ability to manage customers proactively, detect changes in behavior, and optimize portfolio health.
Growth acceleration through targeted commercial strategies

Advanced categorization enables more precise segmentation and more effective commercial actions.

Success Stories

How we helped our clients unlock their potential

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 automated applications

3x

Marketing campaign redemption compared to traditional approach
Risk Management Requirements

A Tier 1 lender wanted to strengthen the existing scoring model by further refining the medium‑risk segment, improving KYC, reducing the risk of loss, enhancing active customer profiling, and defining 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 account transaction data

Main Features

Combines KPIs calculation, score calculation, and account transaction categorization. This macro‑feature covers all analytical capabilities that transform raw transaction data into structured, decision‑ready insights, from categorization to performance indicators and scoring models.

Unifies cash flow indicators, customer portfolio risk monitoring and profiling: these elements all relate to understanding financial stability, detecting early warning signals, and assessing portfolio‑level risk and behavior.