AI Training for Finance | Close faster, reconcile cleaner and report with confidence
Faster close, cleaner reconciliations and AI-assisted reporting. Designed and delivered by CA Dhaval Kodilkar.
Clients include Larsen & Toubro, Safaricom, Bajaj Finserv, Indian Oil, Galderma, Abbott, Lanxess, Bajaj Finance, VE Commercial Vehicles (Eicher), UPL, Marico, WPP Media, IndusInd Bank, KPMG, Income Tax Appellate Tribunal (ITAT), NPCI, Mayfair Insurance, Copy Cat Group, Ramco Group, HCG Group, Clutterbot.
AI training for finance teams teaches controllers, accountants and CFO-office staff to use generative AI on month-end close, reconciliations, AP/AR, working capital and board reporting. CA Dhaval Kodilkar designs each programme around the team's own ERP and Excel workflows, so participants leave with 5 to 10 use cases they can run the following week.
Built for your team.
- Financial controllers
- Accounts and close teams
- AP/AR and treasury staff
- MIS and reporting analysts
- CFOs and finance heads
- Group size
- 8 to 60 participants per cohort
- Prior knowledge
- None needed
Where the time goes today.
- Month-end close stretches past day 7 because reconciliations and accruals are prepared by hand.
- MIS commentary is rewritten from scratch every month and arrives too late to influence decisions.
- AP and AR teams spend hours matching invoices, statements and remittances across systems.
- Board packs are assembled from a dozen spreadsheets with no single source of numbers.
- Staff are already using public AI tools with no policy on what data can go into them.
7 modules, each with a hands-on lab.
- Module 1
AI foundations for the finance function
Know what generative AI can and cannot do with financial data, and where it fails.
Lab: Stress-test an AI tool on a trial balance and log every error it makes.
- Module 2
Month-end close acceleration
Build a prompt-driven close checklist, accrual reviews and flux explanations.
Lab: Generate variance explanations for a 40-line P&L with drivers and caveats.
- Module 3
Reconciliations at scale
Use AI and Excel together for bank, vendor and inter-company reconciliations.
Lab: Reconcile a sample bank statement against a ledger extract and classify breaks.
- Module 4
AP, AR and working capital
Automate invoice extraction, ageing commentary and collection prioritisation.
Lab: Extract fields from 20 sample invoices and draft dunning emails by risk tier.
- Module 5
MIS and board reporting
Turn numbers into board-ready narrative with consistent tone and structure.
Lab: Draft a one-page CFO commentary from a monthly MIS pack.
- Module 6
Controls, privacy and governance
Set rules for which data goes into which tool, with review and audit trail.
Lab: Write a one-page AI usage policy for your finance team.
- Module 7
Your 30/60/90-day plan
Prioritise use cases by effort and value and assign owners.
Lab: Score your team's top 10 use cases on a value-effort grid.
What Finance teams automate first.
| Workflow | Typical tool |
|---|---|
| Draft flux and variance explanations for the monthly P&L |  ClaudeClaude |
| Classify bank reconciliation breaks and suggest journal entries for review |  ExcelExcel with Copilot |
| Extract invoice data from PDFs into a structured upload file |  ChatGPTChatGPT |
| Write customer-specific collection emails based on ageing |  Microsoft CopilotMicrosoft Copilot |
| Summarise vendor contracts for payment terms and penalties | ClaudeClaude |
| Build a cash-flow forecast commentary from treasury data | ExcelExcel with Copilot |
| Generate first-draft board pack narrative from MIS tables | ChatGPTChatGPT |
| Create a close calendar with dependencies and owners | Microsoft CopilotMicrosoft Copilot |
| Answer policy questions from the accounting manual in plain English |  NotebookLM (Gemini Notebook)NotebookLM |
- ChatGPT
- Claude
- Microsoft Copilot
Copilot Studio- Excel
- NotebookLM (Gemini Notebook)
Power BI
Gamma
Choose the depth that fits.
| Format | Duration | Group size | Outcome | Typical deliverables |
|---|---|---|---|---|
| Executive briefing | 90 minutes to 2 hours | 4 to 25 | Shared understanding and priorities | Pre-read, oversight or priority list |
| Half-day workshop | 3 to 4 hours | Up to 40 | Core skills and 3 to 5 use cases | Prompt library, use case list |
| 1-day workshop | 6 to 7 hours | Up to 40 | Hands-on labs and 5 to 10 use cases | Prompt library, templates, 30/60/90-day plan |
| 2-day programme | 2 days | Up to 35 | Deeper labs, team-specific workflows | Templates, workflow designs, adoption plan |
| Multi-sprint programme | 4 to 8 weeks | Up to 30 | Use cases built and tested on live work | Working use cases, measured results |
| Train-the-trainer | 2 to 3 days | 6 to 15 | Internal champions who can run sessions | Trainer kit, slides, lab files |
Related client work.
Two-day finance AI workshop for 25 LANXESS India professionals across Record-to-Report, Accounts Payable, Accounts Receivable, Treasury, Direct and Indirect Tax and Controlling.
Read case studyTwo-day AI workshop for finance professionals from Bajaj Finserv group companies, from lending and housing finance to general and life insurance and broking, ending with an AI strategy roadmap for each participant's problem.
Read case studyTwo-day AI programme for Galderma India's finance team covering research, tax litigation support, Copilot agents, Excel automation and AI governance. 32 participants completed the programme and received certificates.
Read case studyAI training and automation design for HCG Kenya's finance team, covering report generation, hospital ERP (Hospedia) reporting, Tally entries, payables and receivables, and data privacy.
Read case studyRecent sessions.
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What your team keeps.
- 5 to 10 implementable use cases mapped to your workflows
- A prompt library for your team's tasks
- Workflow templates and checklists
- A one-page AI usage policy
- A 30/60/90-day implementation plan with owners
- Post-training Q&A support
AI training for Finance: common questions.
Often booked together.
Get a Finance AI training proposal.
- Response within 1 business day
- Details used only to reply, never shared
- Trusted by teams at Larsen & Toubro, Safaricom, Bajaj Finserv