Banking & Financial Services
Data Science and AI are transforming banking and financial services by enabling real-time fraud detection, predictive risk modeling, and personalized customer engagement. AI-driven credit scoring, algorithmic trading, and compliance monitoring enhance decision-making and regulatory adherence, while advanced analytics optimize lending strategies and investment portfolios for sustainable growth.
Industry Challenges Today
Use Cases
Loan Application Processing
To automate the extraction and processing of information from various documents, including scanned files, PDFs, and handwritten forms, using OCR, Deep Learning, and Gen AI
Challenges
Solution
Seamless extraction of data from PDFs, images, handwritten notes, and unstructured text using GenAI and Deep Learning.
Learns new formats on the fly; no hardcoding needed for every document type.
Understands context across multiple document types; 90%+ precision. Real-time data flow via APIs — enabling faster decision-making and better compliance
Features
- Utilize NLP and computer vision to identify key fields, entities, and layout structures.
- Train models on historical documents for context-aware extraction (e.g., identifying policy numbers, addresses, medical codes).
- Implement feedback loops to improve extraction accuracy over time
- Enable users to ask questions like: “Show claims over ₹10L where discharge summary was missing”
- Automatically generate dashboards from extracted document data.
- Visualize extracted insights in real-time, filtered by department, document type, or location
- Summarize long documents (e.g., contracts, KYC reports) into key decision points.
- Automatically classify document types and extract custom fields using prompt-driven models.
- GenAI co-pilot suggests next steps (e.g., “This document lacks PAN validation — flag for compliance review”)
Results
Reduction in processing time
Accuracy in Data Extraction
Cost savings on operations
Claims Automation
To automate the extraction and processing of information from various documents, including scanned files, PDFs, and handwritten forms, using OCR, Deep LearnTo streamline and accelerate the claims lifecycle (credit protection, insurance, fraud, chargebacks) using intelligent automation, improving customer trust, compliance, and operational efficiencying, and Gen AI
Challenges
Solution
By automating claims processing with machine learning applied to unified structured and unstructured data, organizations can significantly reduce delays and errors. Real-time verification powered by streaming transaction and credit data enhances accuracy and responsiveness. A 360° customer claims view across all data sources improves transparency and decision-making, while continuous model improvements enabled through MLOps, Lakehouse-native ML workflows, and feature stores ensure adaptability, scalability, and long-term efficiency
Features
Predict claim validity based on historical patterns
Score each claim for risk, urgency, and completeness
- Integrate OCR/NLP pipelines to extract structured data from scanned forms, receipts, discharge summaries, KYC docs, etc.
- Detect anomalies in claim amount, policy type, and geolocation vs expected patterns
Allow claim officers to query data like: “List all high-value claims pending >5 days from Tier-1 customers”
Auto-generate dashboards showing SLA breaches, fraud-prone claims, or bottlenecks in processing
GenAI copilots assist agents by summarizing case history, recommending decisions, or auto-drafting rejection/approval notes.
Auto-generate emails/SMS for customers regarding claim status in plain language
Results
Faster Claims Processing
Single source of truth
Lowering fraud payouts by
Loan volume forecasting and case resolution prediction
Forecast loan volumes and case resolution timelines to improve operational efficiency, staffing, and customer service agility
Challenges
Solution
- Implemented an AI-driven forecasting framework using SARIMA and regression models to predict incoming loan and service case volumes
- Integrated historical data across all touchpoints into a unified data lake with automated data refresh pipelines.
- Developed Tableau dashboards to visualize loan inflow trends, expected resolution timelines, and staffing recommendations.
Features
Loan Volume Forecasting
Case Resolution Time Prediction
Automated Capacity Planning Dashboard
Results
Improvement in forecasting accuracy
Faster resolution cycles
Reduction in manual data preparation
Customer 360
To build a unified, intelligent view of each customer to drive personalized engagement, improved risk management, and lifetime value
Challenges
Solution
By creating real-time, unified customer profiles across all touchpoints, organizations can leverage AI-driven segmentation and predictive behavior modeling to deliver personalized experiences seamlessly across digital and physical channels. This approach enhances customer satisfaction, improves retention, and drives cross-sell success, unlocking greater value from every interaction
Features
- Entity resolution and graph models to stitch together multi-channel customer identities.
- Enrich profiles with behaviour, transaction and sentiment data.
- Enable predictive scores for churn, lifetime value, and cross-sell likelihood
- Business users ask natural language queries like: “Show me customers with high NPS but no insurance product”
- Real-time, personalized dashboards generated instantly from the Lakehouse.
- Automate insights: “Top 10 high-value customers at risk of attrition
- GenAI agents generate custom offers based on intent + data. Example: “Hi, based on recent travel spend, here’s a curated travel card with 20% reward boost.”
- GenAI copilots assist RMs by summarizing 360° profiles and suggesting actions in meetings.