How does the recommendation system improve employee performance?

The system analyzes over 1000 financial metrics and generates personalized recommendations for employees, helping them identify improvement areas and take actionable steps to enhance productivity.

What technologies power the recommendation engine?

The engine leverages Python for analytics, PostgreSQL and Teradata for data storage, and Apache Airflow to manage complex workflows, ensuring scalable and accurate processing.

How does Airflow contribute to the system?

Airflow orchestrates tasks using Directed Acyclic Graphs (DAGs), enabling scheduling, monitoring, and scalable execution of complex data processing pipelines required for recommendation generation.

What kind of metrics are used to evaluate system impact?

Metrics include accuracy of recommendations, adoption rate by employees, workflow efficiency improvements, reduction in manual analysis, and overall departmental performance gains.

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.

Discuss an idea

500+

recommendations continuously analyzed

1,000+

performance metrics considered

85%

improvement in personalized employee recommendations

All Technologies Used

PostgreSQL

Python

Teradata

Apache Airflow

Motivation

The customer, a leading banking company, struggled to track hundreds of thousands of employees’ performance metrics across multiple departments. The goal was to automate the analysis of incoming financial data and generate personalized recommendations to help employees identify areas for improvement, optimize workflows, and increase overall efficiency while handling large, diverse datasets accurately.

Main Challenges

The banking system included hundreds of thousands of employees and objects with diverse properties, making it difficult to analyze all data accurately. Azati addressed this challenge by designing a robust data collection and processing workflow, ensuring that all user and departmental variations were considered to generate meaningful recommendations.

With over 1000 financial metrics across departments, it was challenging to provide precise and relevant recommendations for each employee. Azati proposed a tailored scoring and ranking system, combined with Airflow-managed workflows, to accurately calculate and analyze metrics while delivering actionable, personalized recommendations for each department and employee.

Our Approach

Understanding the Business

Azati started by thoroughly analyzing the banking client's goals and workflows. This involved discussions with managers, product owners, and department leads to identify key performance indicators, understand existing bottlenecks, and determine how recommendations should be visualized for employees. This step ensured that the system would provide actionable insights aligned with the company's business objectives.

Data Collection and Preprocessing

The team gathered large volumes of financial and operational data from multiple sources, including internal databases and Teradata systems. Data cleaning and normalization processes were applied to remove outdated or irrelevant information and to ensure consistency across departments. Weighting factors were applied to prioritize recent and relevant metrics for more accurate recommendations.

Metrics Analysis and Recommendation Engine

Azati developed algorithms to calculate over 1000 metrics related to employee performance and financial indicators. Metrics were ranked and analyzed to generate personalized recommendations for each employee. The recommendation engine was designed to consider department-specific criteria, ensuring that insights were relevant and actionable for each workflow.

Airflow-based Workflow Management

The system’s data processing and recommendation generation were managed using Apache Airflow. DAGs (Directed Acyclic Graphs) were created to schedule, monitor, and manage all tasks efficiently. This approach allowed for scalable, repeatable workflows, easy error recovery, and timely delivery of recommendations to employees.

Visualization and Delivery

Finally, the recommendations and metrics were presented to employees via a user-friendly interface. Dashboards were designed to clearly highlight areas for improvement, prioritize recommendations, and provide actionable steps. Managers could also monitor overall performance trends across departments, enabling data-driven decisions.

Want a similar solution?

Just tell us about your project and we'll get back to you with a free consultation.

Schedule a call

Solution

Automated Data Collection

The system automatically collects large volumes of financial and operational data from multiple internal and external sources. It ensures that the information used for analysis is clean, consistent, and up-to-date, significantly reducing manual effort and data errors.

Key capabilities:

Daily automated data extraction
Data cleaning and normalization
Prioritization of recent and relevant metrics
Support for multiple database types including PostgreSQL and Teradata

Metrics Calculation and Analysis

The system computes and analyzes over 1000 financial and performance metrics to provide actionable insights. It ranks and weights metrics according to relevance and department-specific priorities, ensuring that recommendations are precise and meaningful.

Key capabilities:

Computation of diverse financial and operational metrics
Ranking and weighting of metrics based on relevance
Department-specific analysis for precise recommendations
Continuous metric updates for real-time insights

Personalized Recommendation Engine

Based on the analyzed metrics, the system generates tailored recommendations for each employee. These recommendations focus on actionable steps to improve performance and optimize workflow efficiency, taking into account individual and departmental goals.

Key capabilities:

Employee-specific recommendations
Departmental performance alignment
Actionable suggestions to improve workflow
Automatic updates with new data inputs

Airflow Workflow Management

Apache Airflow orchestrates the entire recommendation system workflow, scheduling, monitoring, and executing tasks efficiently. It ensures scalability, reliability, and the seamless handling of complex pipelines for large-scale data processing.

Key capabilities:

Directed Acyclic Graph (DAG) workflow orchestration
Task scheduling, monitoring, and error handling
Scalable and repeatable processing pipelines
Seamless integration with data sources and recommendation engine

Dashboard Visualization

Dashboards present metrics and recommendations in an intuitive, visual format. Managers can quickly identify areas of improvement, monitor employee and department performance, and take informed actions to boost productivity and efficiency.

Key capabilities:

Clear visualization of employee performance metrics
Prioritized recommendation presentation
Department-wide performance tracking
Support for actionable decision-making

Business Value

Improved Employee Performance: Employees receive actionable, personalized recommendations, allowing them to optimize workflow and enhance productivity.

Enhanced Operational Efficiency: Managers can make data-driven decisions, monitor department performance, and identify improvement areas quickly.

Scalable and Reliable System: Airflow-based workflows ensure timely processing of large datasets with minimal errors, improving system reliability and performance by 30–45%.

Data-Driven Insights: Continuous analysis of over 1000 metrics provides actionable insights, supporting performance optimization and strategic planning across departments.

Ready To Get Started

Full Name^*

Email^*

Your request^*

Upload additional information or RFP

Search for file

I permit to collect my data according to Privacy Policy and Terms of Use

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Recommendation System for Banking Industry

All Technologies Used

Motivation

Main Challenges

Our Approach

Want a similar solution?

Solution

Automated Data Collection

Metrics Calculation and Analysis

Personalized Recommendation Engine

Airflow Workflow Management

Dashboard Visualization

Business Value

Ready To Get Started

Related Case Studies

Jira-Based Workflow Automation

Enterprise Banking Platform

Automated Billing Management System

Digital Banking Services Enhancement

Revolutionizing Banking: Automated Promotions Management

Online Solution to Transform Loan Processing in Banking Sector