Summary of "AI Hackathon S4 E4 UC6 - Automated variance analysis and dynamic Dashboard - CA. Jagjeet Singh"

Summary

The video features CA Jagjeet Singh presenting a use case on automated variance analysis and dynamic dashboards designed for industry professionals, particularly chartered accountants dealing with financial reconciliations and analysis.

Key Technological Concepts and Product Features

AI and BI Integration

The solution combines Artificial Intelligence (AI) with Business Intelligence (BI) tools, specifically Power BI for visualization and dashboard creation.
AI tools such as ChatGPT and Claude were leveraged for generating and validating M code (Power Query scripts) and DAX measures (Data Analysis Expressions) used in Power BI.
Power BI’s built-in intelligence assists in generating insights dynamically from large datasets.

Automation Pipeline

Inputs include monthly Trial Balances (TBs), budgets, and product-wise contribution data.
A blueprint for data linkage and mappings was designed, using cloud AI tools to help define output formats.
The pipeline automates the ETL (Extract, Transform, Load) process with reusable M scripts and DAX measures, enabling end-to-end automation.
The user only needs to place input files (TB and contribution data) into a folder; the system refreshes and produces updated reports automatically.

Dashboard and Reporting Features

Dynamic, interactive dashboards with drill-down capabilities for variance analysis between actuals, budgets, and periods.
Visual elements include color-coded variance indicators, arrows for trend direction, and waterfall charts for revenue and contribution analysis.
Flexible time-period selection (month-wise, YTD, quarterly).
Reports include detailed P&L and balance sheet components derived from Schedule 3 data.
Designed to reduce manual errors and accelerate reporting timelines (from 5-8 days to much faster).

Validation and Reliability

Extensive manual and AI-assisted validation loops to ensure accuracy of the output.
Emphasis on CA intelligence (professional review) to build trust and reliability in automated reports.
Scalable model designed to adapt to different accounting software inputs via a standardized trial balance format and mapping tables.

Scalability and Business Relevance

The system supports horizontal (across companies) and vertical (deeper financial analysis) scalability.
Potential to extend to profitability variance, working capital, cash flow, and free cash flow analysis.
Faster, consistent, and reliable reporting for CFOs and board members to enable timely decision-making.

Use of AI

AI tools (ChatGPT and Claude) were primarily used for:

Generating M code and DAX formulas.
Validating logic and outputs.
Assisting in defining report formats and improving code quality through iterative feedback.

Power BI itself provides intelligent data modeling and visualization capabilities.

Technical Details

Input data mainly in Excel format (due to SAP connector limitations).
Core input columns standardized: account number, item flag, period, and amount (INR).
Mapping tables used for aligning data fields.
M code scripts and DAX measures are reusable and adaptable for various analyses beyond just budget vs. actual variance.

Demonstration Highlights

Live walkthrough of the Power BI model showing variance metrics, contribution analysis, and dynamic waterfall charts.
Explanation of the simplicity and flexibility of DAX language for building measures.
Interaction between different metrics and time periods in the dashboard.

Participants

Main Speaker:
- CA Jagjeet Singh – Industry professional and presenter of the use case.
Other Participants:
- Moderator and questioners including “Sir Rama” and “Kumar Bes” who asked about AI usage and scalability.