AI Readiness Training Program for Bank Employees (Kolkata)

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