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Recommendation System for Banking Industry

Azati helped a renowned banking company develop an automated recommendation system that calculates and analyzes financial metrics to provide banking employees with recommendations to improve their efficiency. The system automates data analysis and enhances workflow management using personalized recommendations.

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All Technologies Used

PostgreSQL
PostgreSQL
Python
Python
Teradata
Teradata
Apache Airflow
Apache Airflow

Motivation

The goal was to automate the analysis of incoming data from various financial indicators and use this information to build tailored recommendations for banking employees to improve their performance. The system needed to handle large datasets and provide accurate insights and actionable recommendations.

Main Challenges

Challenge 1
Data Variability

The banking system had hundreds of thousands of users and diverse metrics. The main challenge was ensuring that the system could analyze such vast amounts of data accurately and provide meaningful recommendations while considering varying performance criteria across departments.

Challenge 2
Metrics Diversity

With over 1000 financial metrics, it was difficult to ensure the recommendations were precise and aligned with each department's specific needs. Maintaining accuracy in the calculation and analysis of these diverse metrics was a key challenge.

Key Features

  • Metric Calculation and Analysis: The system calculates over 1000 metrics related to employee performance and financial indicators to produce actionable insights.
  • Recommendation Engine: Tailored recommendations are generated based on the analysis of these metrics, helping employees improve performance in specific areas.
  • Airflow-based Workflow Management: Airflow is used to schedule, monitor, and manage the system’s tasks, ensuring the entire workflow is efficient and scalable.
  • Personalized Employee Recommendations: Recommendations are personalized for each employee, based on their performance and specific department needs, helping them improve productivity and job satisfaction.

Our Approach

Understanding the Business
The first step was to thoroughly understand the customer’s goals and requirements. This involved defining primary goals, identifying key metrics for recommendations, and determining the visualization methods for the end-users.
Data Collection and Analysis
Our team gathered large volumes of data, ensuring it was clean and relevant. We then developed a process to rank metrics and tailor recommendations based on the most current and meaningful data.
Workflow with Airflow
We used Airflow to design a scalable, efficient system that could handle the complex workflows of analyzing and calculating metrics. Airflow's Directed Acyclic Graphs (DAGs) were ideal for structuring tasks and managing dependencies within the recommendation engine.

Project Impact

The automated recommendation system now helps bank employees identify areas for improvement by providing them with tailored, data-driven insights.

The system has enhanced operational efficiency, enabling managers to make more informed decisions and optimize performance across departments.

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