Case Studies - DGLiger

DIGITAL BANKING

Transforming Banking Operations

We developed a digital cockpit that transformed a bank into an intelligent enterprise that enhanced customer acquisition and transaction processes to foster a data-driven culture and improved operational efficiency through real-time insights and analytics.

Digital Banking

Platform

Mobile & Desktop

Technology

Python, TensorFlow, Spark-ML, FB Prophet, PySpark

Solutions

Forecasting Engine, Customer Targeting, Model Automation, Dashboard Reporting

Data-Driven Digital Transformation for Banking Efficiency

To address inefficiencies in traditional banking analytics, the project focused on building an advanced, real-time forecasting and targeting solution. Legacy systems lacked scalability, leading to delayed insights and inconsistent performance.

By leveraging cloud-native technologies, distributed processing, and machine learning models, the solution enabled seamless integration of multi-source data, automated model retraining, and accurate predictions.

The system now delivers real-time forecasts, dynamic customer segmentation, and personalized insights via responsive dashboards and mobile interfaces—empowering data-driven decisions across banking operations and improving customer engagement through timely and actionable intelligence.

Project Goal

The Vision is to Build an Agile & Futuristic Digital Cockpit driven by Bank’s Adoption to Digital Processes.

Requirements & Key Challenges

The primary objective is to build the digital cockpit to have a wholistic view of how processes of customer acquisition and transactions are moving towards digital journey of Bank.

Data Integration
Handling large-scale SME, financial, and transaction data efficiently without latency or performance bottlenecks across platforms.

Digital Dashboards
Managing data accuracy, real-time integration, and scalability for consistent, insightful, and automated digital dashboards.

Future Proof Best Practices
Embedding accurate forecasting outputs into core systems with real-time live alerts and minimal manual intervention required.

The Solution

Tabbed Solution Layout

CxO dashboard

This comprehensive analytics suite spans BI dashboards, sales intelligence, and data lakehouse innovations. It empowers real-time decision-making, customer targeting, and risk management through predictive modeling, geospatial insights, web analytics, and cloud-native tools.

Sales Location Analytics

An advanced predictive analytics solution integrating geoprocessing and plotting to optimize sales performance. The system clusters 100,000+ sales locations across India and predicts card sales for each hour based on multiple parameters, including seasonality, location type, and active sales representatives.

Digital Cockpit - Approval

A powerful approval analytics tool with multiple insights across channels, account types, industries, and customer segments. The system provides a trend-based approval mix, funnel rates, and bureau score tracking. It includes interactive line graphs and slideshow-based insights to track rejections, approvals, and creditworthiness over time.

Customer Data Platform

A comprehensive web analytics platform that builds a persona for each identified user based on demographics, affinity, and device data. The system enables audience segmentation and targeting, allowing businesses to analyze, target, and retarget users based on their behaviors.

Tracking Business & KPIs

A real-time analytics system that transforms collected user data into visual dashboards for business teams. It provides insights into campaign performance and customer behavior through various Business, Persona, and Monitoring Dashboards.

Campaign Funnels

A detailed funnel analysis tool that maps customer journeys, tracks drop-offs, and optimizes conversion rates. It enables businesses to refine their marketing strategies and sales processes for improved efficiency.

Web Analytics Platform

A real-time event tracking framework that captures customer actions across websites and mobile apps. It includes API-driven architecture, event parsing, and audience journey tracking to deliver 360-degree customer insights.
✔ Technical Features: Data encryption, secure transfer, user authentication, RBAC integration, regulatory compliance
✔ Deployment Models: Public Cloud, Private Cloud, Hybrid Cloud, On-Premise

MIS reporting

A unified Reporting Automation Framework leveraging ADF, Databricks, and Azure tools to automate master data loading, eliminate manual reports, and enhance data reconciliation, enabling secure, consistent, and scalable MIS delivery across banking operations.

Architecture

A robust cloud-based architecture integrating batch and real-time data sources into Azure Data Lake, Databricks, and ML pipelines, delivering curated insights via Power BI and Power Apps, with strong governance, DevOps, and scalable analytics foundation.

Location-Based Sales Improvement for Credit Card Provider

Predicts daily customer credit card applications at POS locations. Forecasts drive automated SMS/email alerts for underperforming teams. Data is processed via Oracle SQL, Elasticsearch for analysis, and Kibana/Grafana for visualization, helping optimize sales strategies based on real-time performance insights.

Conversational AI Model & Generative AI (ChatGPT)

Conversational AI enables natural interactions via digital channels, converting speech to text and vice versa. Advanced models process inputs using NLU, GUI, and context awareness to generate intelligent responses. Deployed across mobile, web, and messaging platforms, improving user engagement and automation.

Credit Risk Evaluation for Lending

Predicts loan default risk using SME financial and operational data. Combines historical data with machine learning for binary classification of defaulters. Incorporates surrogate data sources for improved accuracy. Enables proactive risk management, ensuring better lending decisions.

Multidimensional Customer Segmentation Model

Groups customers based on needs, behaviors, and transaction data to optimize marketing and retention. Uses rule-based segmentation and analytics to identify profitable customer segments. Enables targeted communication and personalized offerings, maximizing sales efficiency and promotional impact.

The Outcome

Advanced Machine Learning & AI Integration

Implemented Spark-ML, TensorFlow, and FB Prophet for forecasting
Optimized Python (PySpark, PyHive) for data processing
Automated model training and evaluation pipelines
Improved feature selection with statistical testing
Enabled deep learning for complex pattern recognition

Scalable & Automated Data Processing

Integrated API-driven forecasting and prediction jobs
Leveraged Oracle SQL for structured data handling
Streamlined ETL with PySpark for large datasets
Enabled real-time analytics with Elasticsearch & Kibana
Automated reporting via Grafana dashboards

Optimized Cloud, Big Data, and Analytics

Deployed models in a cloud-hosted secure environment
Utilized distributed computing for high-speed processing
Integrated Kafka for real-time data streaming
Enhanced storage and retrieval with Elasticsearch
Ensured scalability with containerized ML workflows

Business Enquiries

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Career Queries

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Data Platform

Visualization & Analytics

Data Management

Machine Learning

LLM & GenAI Solutions

Agentic AI Workflows

AI Platform & Governance

Automation & Integration

Cloud Computing

Campaign Management

Internet of Things

Platform Modernization

Cyber Security Consulting

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