Job Description

Objective: Enable employees to use AI (cloud-based & local) for daily banking workflows, analytics, reporting, and decision support: with very less coding.

This 5-day program is designed for non-programmer bank employees who are proficient in Excel but need to become AI-ready.

*Only Open-Source applications and publicly available data will be used in the Training Program.

Day 1 - Foundations of AI & Prompting for Banking Tasks

Morning Session (3 hrs)

Ice-Breaking and Course Introduction

Conduct of Pre-assessment test? Ice-breaking (know each other and know your faculty) Brief introduction to the program outline Training Objectives
- Introduction to AI & Generative AI

Evolution of AI and its impact on the BFSI sector Use cases in banks: customer support, fraud detection, credit scoring, compliance, reporting Business Impact: Helps employees contextualize AI in their daily roles
- Prompt Engineering Basics

Anatomy of a good prompt Instruction tuning (role, context, task) Iterative prompting (refine -> test -> improve) Example: Drafting customer letters, summarizing RBI circulars
Post-Lunch Session (3 hrs)

- Advanced Prompting & Pitfalls

Zero-shot vs Few-shot prompts Structured outputs (tables, summaries) Limitations: hallucinations, privacy risks, factual errors Important Note: LLMs sometimes generate answers that sound confident but are factually incorrect (called hallucinations). In banking, hallucinations may misinterpret compliance or financial advice. Always double-check AI outputs. Demo: Generate product comparison, summarize MIS reports Business Impact: Prepares employees to use AI as a smart assistant
- Hands-on Exercises:

Drafting customer FAQs Summarizing daily branch reports Generating loan appraisal templates
Day 2 - AI without Internet: Local LLMs & Secure AI Practices

Morning Session (3 hrs)

- Local AI (Ollama, Llama, Deepseek)

Why local models matter (data confidentiality) Comparison with cloud AI Demo: Setting up Ollama Workplace Application: Builds confidence that AI can be used securely
Post-Lunch Session (3 hrs)

- RAG (Retrieval-Augmented Generation) Demonstration

Upload RBI circulars ? query in natural language Querying branch SOPs for faster access Limitations of local AI Business Impact: Enables faster compliance checks and knowledge access
- Hands-on Exercise:

Load FAQs/policies into local AI Employees query them for compliance answers
Day 3 - Data Analytics Beyond Excel

Morning Session (3 hrs)

- Analytics Fundamentals for Bankers

Core BI Operations: Slicing, dicing, pivoting, filtering, sorting, aggregation (roll-up), drilldown, drill-through, ranking, trend analysis, what-if analysis, exception/outlier detection,
cross-dimensional analysis

Why Excel is limited for large data Introduction to Superset (no coding needed) Business Impact: Empowers employees to analyze branch performance, NPA trends, and deposit growth beyond Excel
Post-Lunch Session (3 hrs)

- Superset Hands-On

Importing CSV data Creating pivot tables, trend charts, and filters Comparing performance across branches using drill-down and ranking Business Impact: Enhances decision-making dashboards
Day 4 - From Analytics to Dashboards (PowerBI)

Morning Session (3 hrs)

Introduction to Power BI

Business Intelligence What is Power BI Why Power BI? Key Benefits of Power BI Flow of Power BI Components of Power BI Architecture of Power BI Building Blocks of Power BI
Power BI Desktop

Learning Objective: This module will introduce you to Power BI Desktop. You will know how to extract data from various sources and establish connections with Power BI Desktop, perform transformation operations on data and the Role of Query Editor in Power BI.

Overview of Power BI Desktop Data Sources in Power BI Desktop Connecting to a data Sources Query Editor in Power BI Clean and transform your data with Query Editor Combining Data - Merging and Appending Cleaning irregularly formatted data Views in Power BI Desktop Modelling Data Manage Data Relationship Cross Filter Direction Create calculated tables and measures Optimizing Data Models
Post-Lunch Session (3 hrs)

Learning Objective: This module will help you understand the benefits and best practices of Data Visualization. It will also help you in creating charts using Custom Visuals.

Introduction to visuals in Power BI Charts in Power BI Matrixes and tables Slicers Map Visualizations Gauges and Single Number Cards Modifying colours in charts and visuals Shapes, text boxes, and images
Day 5 - Introduction to Power BI Q&A and Data Insights

Morning Session (3 hrs)

Learning Objective: This module will help you in creating Dashboards and publishing it on Power BI services.

Introduction to Power BI Service Dashboard vs. Reports Quick Insights in Power BI Creating Dashboards Configuring a Dashboard Filters in Power BI
Learning Objective: The following power bi Training section explains you about the types of Filters with a practical example

Slicer Basic Filters Advanced Filters Top N Filters Filters on Measures Page Level Filters Report Level Filters Drill through Filters
Post-Lunch Session (3 hrs)

Microsoft Copilot Project
Job Type: Full-time

Work Location: In person