AI Banking Insights

Transforming banking datasets into actionable insights enhances customer segmentation, predictive forecasting, and strategic decision-making in the fintech industry.

Fintech

2018

Client and Industry

An award-winning fintech company from India serves as a global solution provider for digital banking, catering to over 60 banks and financial institutions worldwide.

Industry

Fintech

Business Type

Enterprise/Start-up

Services

Development & Deployment

Our Role

We have deployed ML-based solutions to transform extensive banking datasets into actionable insights, enhancing customer segmentation and forecasting. Our advanced algorithms uncover complex patterns, optimize data processing, and aid strategic decision-making in the banking sector.

Project Goals

01

Insightful Data Analysis: Extract meaningful insights from bank transaction data to provide customers with valuable forecasting and clustering information.

02

Dual-Level Forecasting: Perform forecasts at both the overall bank and individual customer levels, predicting transaction volumes within a specified future timeframe.

Business
Challenges

01.

Limited Data Availability: Address the challenge of insufficient data for effective forecasting and clustering, necessitating innovative solutions.

02.

Model Selection Constraints: Navigate the complexity of selecting appropriate statistical models to handle forecasting due to the limited variability of data.

Solution

We applied advanced data analysis to segment customers by account types and transaction volumes and enhance personalized engagement. We used ARIMA models for accurate forecasting of banking activities at both the bank and customer levels.

Key Features

This approach not only improved predictive accuracy but also ensured our models remained adaptive and relevant to evolving banking trends.

01

Predictive Modelling: ARIMA models replaced LSTM and other neural networks for time series forecasting, significantly reducing prediction errors by up to 70% through meticulous fine-tuning.

02

Dynamic Adaptation: Implemented an inference algorithm for forecasting various transaction metrics and provided training code to refine models with new data, maintaining accuracy and relevance.

Impact

01.

Customer Service Optimization: Improved customer clustering led to more tailored service strategies, increasing customer satisfaction and retention.

02.

Proactive Forecasting: The forecasting models provided valuable insights, allowing the bank to anticipate and adapt to future trends effectively, enhancing overall operational responsiveness and strategic planning.