REST API (built using AWS Lambda and Amazon Textract) to extract structured text data from images of Indian OVD(s) (Officially Valid Documents) like Aadhaar card, PAN Card, Driving License, Passport, etc.

Build a response model for predicting the probability of a response from each prospect and target the ones most likely to respond to the telemarketing campaign. The objective is to reduce the cost of the bank by doing a targeted campaign.

🛡️ RakshaQuant - AI-powered paper trading for NSE using LangGraph multi-agent orchestration. Features 4 LLM agents, learning feedback loop, LangSmith observability & risk guardrails. Built with Groq, DhanHQ sandbox & PostgreSQL. Paper-first design.

End-to-end KYC/AML compliance data analysis using mock datasets. Includes customer risk scoring, suspicious transaction flagging, and compliance reporting in Python (Pandas, Matplotlib).

Horizon is the modern banking platform

BFSI & AI LITERACY HINGLISH KNOWLEDGE GRAPH™ HINGLISH COGNITIVE ANCHORING MODEL™

This project demonstrates a mock QA test case automation and reporting workflow, using simulated data to showcase analysis, visualization, and insights. It highlights how test execution, status, priority, module, and tester performance can be tracked and analyzed for effective QA management.

In this project, task is to help banking organization to identify the right customers using predictive models. Using past data of the bank’s applicants, you need to determine the factors affecting credit risk, create strategies to mitigate the acquisition risk and assess the financial benefit of the project.

Docs-only case study of a compliance & anomaly detection platform on Azure + Databricks (Streaming ETL + Batch ELT + ML).

Production-grade banking document processing pipeline using Azure AI Document Intelligence, GPT-4o, and OpenCV. Extracts structured data from cheques, invoices, KYC forms, ID cards, and trade finance documents with KYC/AML compliance validation. Deployable as Azure Web App

Multimodal RAG pipeline for banking — upload financial reports with charts and tables, ask questions, get grounded answers with citations. Uses Azure OpenAI GPT-4o, Azure AI Search (hybrid vector + keyword), and Azure AI Vision Florence image embeddings for cross-modal retrieval.

Sanitized ML-based risk scoring pipeline for a Tier-1 UK Retail Bank (GCP + BigQuery ML). Includes Batch/ETL ingestion, feature engineering, BQML training, scoring workflows, governance, lineage, and runbooks. No client code/data.

GCP-based Regulatory Reporting Lakehouse — Tier-1 Swiss Bank (Simulated Case Study):- Documentation-only repo illustrating a cloud-native data lakehouse architecture for regulatory reporting on Google Cloud Platform (GCS + BigQuery + Dataflow + Composer). Includes ADRs, runbooks, and compliance data contracts.

AI-powered BFSI Call Center Assistant using RAG, similarity matching, and guardrails for secure financial query handling.

Comprehensive Kubernetes Zero to Hero course for corporate professionals in Telco, BFSI, and Cloud domains. Complete hands-on training from Git basics to production K8s deployments.

This project implements a BFSI Multilingual Voice Assistant that enables banks to: Upload recorded customer calls OR Process customer queries via text input

Computer Vision and OCR pipeline for banking documents — cheque reading (MICR extraction), ID card verification (MRZ parsing, face detection), signature verification (ORB feature matching), fraud detection (ELA, copy-move, noise analysis), and image quality assessment. Built with OpenCV, Azure AI Vision, and GPT-4o.

Docs-only case study – Compliance Reporting data platform on Azure for a Big-4 Audit & Consulting Firm (BFSI, healthcare-style datasets) using Streaming Pipeline (ETL) + Batch Pipeline (ELT) with Snowflake, Synapse, ADF, Power BI, ML risk scoring, DQ, governance, and lineage.

Tiered BFSI AI Assistant built with FastAPI, FAISS, fine-tuned DistilGPT2 and Retrieval-Augmented Generation (RAG) with compliance guardrails.

🔍 Detect compliance anomalies in financial transactions with Azure and Databricks, ensuring accuracy and transparency for audit processes.