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Development of an AI-Powered Merchant Data Extraction and Categorization System for Financial Institutions
  1. case
  2. Development of an AI-Powered Merchant Data Extraction and Categorization System for Financial Institutions

Development of an AI-Powered Merchant Data Extraction and Categorization System for Financial Institutions

emerline.com
Financial services

Identify Challenges Faced by Financial Institutions in Merchant Data Collection and Customer Insight

The client faces insufficiency of detailed, categorized merchant data derived from customer transactions, hindering targeted marketing, customer retention, and strategic decision-making related to merchant engagement.

About the Client

A mid-to-large size banking or payment processing institution seeking advanced merchant insight capabilities to enhance marketing and risk assessment strategies.

Goals for Implementing a Merchant Data Insights Solution

  • Automate the extraction of detailed merchant information from transaction data.
  • Categorize merchants into predefined groups to enable strategic analysis.
  • Improve identification of key merchants and customer behavior insights.
  • Enhance market position by providing banks with actionable merchant intelligence.
  • Optimize URL scoring to reflect human browsing decisions for better merchant recognition.

Core Functional Features for Merchant Data Extraction and Categorization System

  • Data ingestion module for securely receiving transaction information (merchant name, MCC code, transaction location).
  • ML algorithms for product recognition and merchant categorization based on transaction descriptions and external web data.
  • URL scoring system that assesses merchant website relevance based on learned criteria similar to human browsing patterns.
  • Automated data validation and filtering to ensure accurate merchant profiling.
  • Integration layer for seamless data flow between banking systems and the analytics platform.

Preferred Technologies and Frameworks for the Merchant Data System

Machine learning frameworks such as Gensim, Doc2Vec, TF-IDF
Predictive modeling tools including XGBoost, LightGBM, Logistic Regression
Programming languages such as Python with libraries like Numpy

Essential External System Integrations for the Solution

  • Banking transaction systems for real-time data transfer
  • Merchant websites for URL validation and product recognition
  • Client data warehouses for historical data analysis

Non-Functional Requirements for System Performance and Security

  • High scalability to handle increasing transaction volumes
  • Low latency response times to support real-time insights
  • Data security and compliance with financial industry standards
  • System reliability with minimal downtime

Projected Business Benefits of the Merchant Data Insights System

Implementation of the system will enable banks to gather critical merchant and customer insights more efficiently, leading to improved marketing strategies, increased customer retention rates, and a stronger market position. The platform's capabilities are expected to facilitate faster, more accurate merchant profiling and enhance data-driven decision making.

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