Summary of "AI Hackathon S4 E6 UC5: Python Tools for CAs AI UC in Audit, Finance & Compliance –CA Siddharth Shah"
Summary: AI Hackathon S4 E6 UC5 – Python Tools for CAs AI Use Case in Audit, Finance & Compliance
Presenter: CA Siddharth Shah
Overview
CA Siddharth Shah presented three Python-based automation tools designed for Chartered Accountants (CAs) to improve audit, finance, and compliance operations. The focus was on data privacy, local execution, and ease of use. These tools run entirely offline on users’ PCs, ensuring client data confidentiality and auditability without relying on cloud services.
Tools Presented
1. PDF Split & Merge Tool
- Functionality: Merge, split, add page numbers, create an index from file names, convert images to PDF and vice versa, and optimize PDF size.
- Unique Value: Fully offline operation avoids uploading data to third-party servers, addressing confidentiality concerns.
- User Experience: Packaged as a single executable for one-click operation; no coding or command-line knowledge required.
- Key Feature: Automatically generates an index page with file names and page numbers for easier navigation.
2. Vendor Master Automation Tool
- Target Users: Small and Medium-sized Enterprises (SMEs) or firms without ERP systems that maintain vendor data manually.
- Functionality: Upload vendor details via forms, auto-assign vendor codes, view and download vendor master data.
- Data Storage: Uses SQLite database, enabling easy file sharing via OneDrive or shared folders for multi-user access.
- Compliance: Includes PAN and GST verification for vendor authenticity.
- Future Plans: Integration with purchase requisition and purchase order (PRPO) modules to form a lightweight customized ERP system.
- Standalone System: Currently not integrated with existing accounting software but designed for modular expansion.
3. Inventory Sampling Tool
- Purpose: Automate audit sample selection from inventory data for physical verification.
- Flexibility: Accepts various Excel formats with a column mapping feature (e.g., map quantity, amount, stock ID).
- Sampling Methods Supported:
- ABC analysis
- Random sampling with seed control
- Random value coverage (e.g., 90% value coverage)
- PPS (Probability Proportional to Size)
- Hybrid methods
- Random Seed Feature: Ensures reproducibility of sample selection for audit trail and regulatory validation.
- Outputs: Generates audit-ready sampling sheets with ABC analysis, variance, anomalies (negative/zero stock), and KPI summaries (total items, quantities, values).
- Materiality: Sampling can be aligned with materiality thresholds (e.g., percentage coverage), and the tool can be extended to transaction-level sampling beyond inventory.
Development Approach & Strategy
- Tool Development Process:
- Ideation and feature discussion with ChatGPT before requesting code generation.
- Iterative debugging and refinement with AI assistance.
- Packaging tools as executable files to ensure usability for non-technical users (no Python or command-line knowledge required).
- Key Strategic Focus:
- Local offline automation to ensure data privacy and compliance with data protection laws (e.g., DRDP Act).
- Accuracy and consistency prioritized over fully automated AI decision-making due to risk of AI errors.
- Emphasis on CAs “thinking like auditors” and “building like engineers” to design reliable tools.
- Development Time: Each tool took approximately one hour to develop, demonstrating rapid prototyping feasibility.
Additional Tools Mentioned (Briefly)
- Bank Statement Analyzer: Categorizes credits, debits, UBI amounts.
- Tally Data Extractor: Extracts data from Tally ERP and creates dashboards.
- Invoice Generator for Company Secretaries: Automates recurring invoice generation.
- Compliance Calendar: Task management for compliance deadlines.
- Email Invoice Extractor: Automatically extracts invoice data from emails daily and prepares bookkeeping-ready Excel sheets.
Key Business & Operational Insights
- Data Privacy & Security: Local execution is critical for client confidentiality in audit and finance workflows.
- Customization & Scalability: Tools can be customized to client-specific needs, enabling small firms without ERP systems to automate key processes.
- Reproducibility & Auditability: Random seed in sampling tool ensures audit trail and regulator acceptance.
- Materiality Integration: Sampling aligned with audit materiality enhances audit quality and documentation.
- Ease of Adoption: Packaging as executables lowers adoption barriers for CA firms lacking technical resources.
- Lean Development: Rapid tool development using AI-assisted coding accelerates innovation in audit tech.
Concrete Examples & Use Cases
- Audit teams can upload client inventory Excel sheets of any format, map relevant columns, and generate statistically valid samples for physical verification with audit trail.
- Vendor master management without ERP systems, including automated PAN/GST validation, reduces manual errors and improves vendor data integrity.
- Offline PDF management tools that add indexing and page numbering improve document handling without risking data leakage.
Discussion Highlights
- Materiality in Sampling: The tool supports materiality thresholds in sample selection and can be extended to transaction-level sampling for purchases/sales.
- Vendor Tool Integration: Currently standalone but future roadmap includes linking with procurement modules.
- Comparison with Existing Tools: The PDF tool’s unique value is free, offline operation with index creation, differentiating it from paid or existing desktop PDF tools.
- AI Role: AI (ChatGPT) is used as a coding assistant rather than decision-maker to maintain accuracy and control.
Frameworks & Methodologies
- Audit Sampling Methodologies: ABC analysis, random sampling with reproducibility, PPS sampling, and hybrid approaches integrated into the inventory tool.
- Lean Startup/Agile: Rapid prototyping (1 hour per tool), iterative development with AI assistance.
- Data Privacy Compliance: Local processing aligned with data protection regulations.
- User-Centric Design: Executable packaging for ease of use by non-technical auditors.
Metrics & KPIs
While not explicitly stated, implied KPIs include: - Sampling coverage percentage (e.g., 90% value coverage) - Number of lines/items sampled vs. total population - Accuracy and reproducibility of sample selection - Reduction in manual effort/time for audit sampling and vendor management - Error rate target: <0.05% error tolerance emphasized for finance tools
Recommendations & Takeaways
- Leverage AI tools like ChatGPT for rapid development but maintain human oversight for accuracy.
- Prioritize offline, local automation to address confidentiality in audit and finance workflows.
- Build modular, customizable tools that can integrate with existing or future ERP systems.
- Incorporate audit standards (materiality, randomness) into tool logic to enhance regulatory acceptance.
- Package tools for easy adoption by non-technical users within CA firms.
- Think like an auditor first, then build like an engineer—balance domain expertise with technical execution.
Presenter: CA Siddharth Shah Moderator/Interviewer: Unnamed panelists from ICI (Institute of Chartered Accountants)
Category
Business
