Finance & Accounting AI Training Singapore
Finance AI training in Singapore on Copilot, ChatGPT, Claude or Gemini: one onsite day on variance analysis, month-end close and FP&A, on your own files.
AI training for finance & accounting teams
Finance teams are being handed AI assistants without being shown how to use them on finance work. This is a hands-on programme for controllers, FP&A leads, accountants and finance analysts who need to apply Microsoft Copilot, Claude Cowork, Gemini Enterprise, Amazon Q Enterprise or ChatGPT Enterprise to real ledger, reporting and close tasks — with the checks that make the output safe to sign off. Sessions are delivered in Singapore, on site or remote, using your tool licences and anonymised versions of your own working files.
Who this programme is for
Finance teams, controllers and FP&A leads.
- Controllers and finance managers responsible for the accuracy of what gets reported
- FP&A analysts who build variance commentary, budgets and forecasts
- Accountants and finance operations staff running reconciliation and month-end close
- Finance leaders deciding where AI is allowed in the reporting chain and where it is not
Who it is not for
- Teams looking for a vendor demo rather than hands-on practice
- Anyone expecting AI to post entries or approve transactions without human review
- Data science teams building models — this is applied assistant training, not ML engineering
Assistants covered in the programme
The programme is delivered on the assistant your organisation already licenses. The techniques transfer between them.
Microsoft Copilot
Excel, Outlook, Teams and Word inside Microsoft 365. Used for variance analysis, reconciliation prep, meeting follow-ups and drafting commentary against workbooks you already own.
ChatGPT Enterprise
Analysis over uploaded files, custom GPTs for repeat finance tasks, and prompt patterns for reporting packs.
Gemini Enterprise
Google Workspace finance work: Sheets analysis, Docs reporting and grounded answers over Drive content.
Claude Cowork
Long document and contract review, structured extraction and multi-step analysis where reasoning needs to be explainable.
Amazon Q Enterprise
Grounded answers over connected enterprise sources for teams standardised on AWS.
Curriculum and hands-on modules
Module 1: Analysis and reconciliation
Hands-on exercise. Work through a sample ledger, identify exceptions and validate calculations against the source.
What you leave with. A reviewed analysis worksheet and reconciliation checklist.
Module 2: Reporting and commentary
Hands-on exercise. Turn verified results into variance commentary, management reports and a board briefing.
What you leave with. A reporting template with source references and review checkpoints.
Module 3: Close and planning
Hands-on exercise. Draft a close checklist, explain budget movements and prepare follow-up questions for budget owners.
What you leave with. A repeatable month-end workflow and planning brief.
The same finance & accounting day on each assistant
The modules stay the same; the exercise changes with the tool your team is licensed for.
Microsoft Copilot: Month-end bank reconciliation in Excel
- What goes in
- One month of bank statement export and the general ledger cash account, side by side in a single workbook.
- What is asked
- List every item in the bank that is not in the ledger and every ledger entry not in the bank, grouped by likely cause, with the row each one came from.
- What comes out
- An unmatched-items table that separates timing differences, missing postings and probable duplicates.
- What the reviewer checks
- Each unmatched item is traced to its source row, and the closing balances are tied out with a worksheet formula rather than the assistant's own arithmetic.
ChatGPT Enterprise: A variance commentary custom GPT
- What goes in
- A budget-versus-actual export and last quarter's approved commentary, loaded into a custom GPT.
- What is asked
- Draft commentary for every line over the materiality threshold in house style, stating a driver only where the data shows one.
- What comes out
- Draft commentary with each unexplained variance marked "driver to be confirmed by owner".
- What the reviewer checks
- The GPT is tested against a quarter it has not seen; any driver it states that the data does not show fails the test.
Claude Cowork: Lease agreement to ledger inputs
- What goes in
- A 60-page property lease and its side letter.
- What is asked
- Extract commencement date, term, rent schedule, escalations, break options and incentives, quoting the clause for each, for the SFRS(I) 16 lease schedule.
- What comes out
- A one-page extraction table your team loads into the lease schedule.
- What the reviewer checks
- A reviewer opens each quoted clause and confirms the figure; the accounting judgement on options stays with the accountant.
Gemini Enterprise: Reconciliation in Google Sheets
- What goes in
- A ledger extract and a bank export in one Sheet.
- What is asked
- Match the two, list the unmatched items by likely cause and reference the row of each.
- What comes out
- An exceptions tab inside the same Sheet.
- What the reviewer checks
- Balances are tied out with Sheet formulas and each exception is opened at source before it is cleared.
Amazon Q Enterprise: Close questions against connected sources
- What goes in
- Your connected finance sources and the questions the team asks every close.
- What is asked
- Answer each close question, naming the source system behind every figure.
- What comes out
- A standard close question set the team reruns each period.
- What the reviewer checks
- Each answer is compared with the system of record, and any question touching a source outside scope is flagged.
Singapore rules the exercises respect
Finance work in Singapore runs against local standards and regulators, and the exercises are set up with that in mind.
- SFRS(I) treatment, such as lease classification under SFRS(I) 16, is a judgement. The assistant can surface the clauses; the accountant makes the call.
- Figures that feed GST F5 returns to IRAS or ACRA filings keep a named human preparer and reviewer.
- For MAS-regulated firms, assistant use sits within your technology risk management and outsourcing assessments. We work inside what your risk team has already approved.
- Customer and counterparty data stays subject to the PDPA, so exercises use anonymised extracts unless your data protection officer has cleared otherwise.
How the sessions run
Delivery
On site in Singapore or remote, delivered live. Cohorts are kept small enough that every participant works on their own files rather than watching a screen share.
Duration and scheduling
Typically one full day, or two half-days scheduled around close. Module order is adjusted to your calendar and tool stack.
Session structure
Each module follows the same pattern: a short walkthrough of the technique, a hands-on exercise on your own data, then a review of what the assistant got wrong and how to catch it.
After the session
Participants leave with the prompt patterns, templates and review checklists produced during the session, so the work continues after the training day.
Prerequisites
- A licensed seat on at least one of the covered assistants (we can run the programme on whichever your organisation has)
- Working knowledge of Excel or Google Sheets and your own close or reporting process
- Anonymised or sample working files — ledger extracts, a variance pack, a close checklist
- No coding background required
Data handling and human review
- Exercises run inside your own tenant and your own licences, so your data does not leave your environment for the training.
- Files shared with the trainer in advance are anonymised extracts; live production data stays inside your own tenant and licences.
- Every module includes verification: tracing a figure back to source, checking totals independently, and identifying what the assistant omitted.
- Approval and posting stay with your people. The programme teaches AI-assisted preparation and review, never unattended financial action.
Who delivers the training
Abhijit Ghosh
The programme is delivered by Abhijit Ghosh, who has spent 25 years in data and AI, including roles at Accenture, IBM, Deloitte, Cloudera, Tibco and Wipro. Sessions are run by the practitioner, not handed to a junior facilitator.
Frequently asked questions
Which AI assistant should our finance team use?
Start with the one your organisation already licenses. Microsoft Copilot suits teams living in Excel and Outlook; Gemini Enterprise suits Google Workspace; ChatGPT Enterprise and Claude Cowork are stronger for document-heavy analysis; Amazon Q suits AWS-standardised estates. The programme covers whichever you have, and the underlying techniques transfer between them.
Do participants need any technical or coding background?
No. The programme is built for finance practitioners. If you can build a pivot table and explain your close process, you have the prerequisites. Nothing in the sessions requires scripting.
Can Copilot be trusted with month-end close?
Not unattended. It is useful for preparing reconciliations, drafting commentary, summarising movements and building checklists. The figures still need tracing back to source and the entries still need a human approver. A large part of the training is teaching the specific checks that catch AI errors in finance output.
Will we work on our own data?
Yes. Private onsite sessions are run on your own finance data inside your own tenant; where a file has to be shared with the trainer in advance, an anonymised extract is used. Generic exercises do not transfer back to the job — working on your own ledger, variance pack and close checklist means the templates produced during the day are usable the following week.
How long is the programme and how is it scheduled?
Typically one full day, or two half-days. Finance teams usually schedule it away from close week. Module order is adjusted to your tool stack and reporting calendar.
Is the training delivered in Singapore?
Yes. Sessions are delivered on site in Singapore, or remotely for distributed finance teams across the region.
What do participants take away?
A reviewed analysis worksheet and reconciliation checklist, a reporting template with source references and review checkpoints, and a repeatable month-end workflow — all produced during the exercises using your own material.
How is this different from a vendor product demo?
A demo shows features on prepared data. This programme applies the tool to your reconciliation, your variance pack and your close, including the cases where the assistant produces confident, wrong output — because recognising those is the skill that matters in finance.
Does our confidential financial data leave our Microsoft 365 tenant or train AI models?
No. All hands-on exercises take place entirely inside your organization's approved Microsoft 365 tenant under your existing security policies, identity management, and role-based access controls. Microsoft Commercial Data Protection guarantees that your enterprise queries, ledger extracts, prompts, and Microsoft Graph contents are never used to train foundation models, with Zero Data Retention (ZDR) enforced. We sign enterprise NDAs prior to delivery.
What Microsoft 365 licenses and system prerequisites are required before the workshop?
Participants need an active Microsoft 365 Copilot license (assigned to an underlying Microsoft 365 Business Standard/Premium or Office 365 E3/E5 tenant) and access to Microsoft Excel and PowerPoint desktop or web apps. Participants should be comfortable using Microsoft Excel. No coding, Python, VBA, or data engineering knowledge is required.
What post-training support, asset playbooks, and prompt libraries are provided?
Your team walks away with the working production workflows built during the day, our complete 50+ Finance & Accounting Prompt Playbook, custom Copilot Studio agent blueprints, and 30 days of asynchronous implementation support clinic via Slack or Teams to resolve edge cases during your next month-end close.
How does this workshop integrate with our existing ERP (SAP, Oracle NetSuite, Xero, Microsoft Dynamics)?
The training works directly with standard general ledger, trial balance, and transaction extracts exported to Microsoft Excel from any major ERP. There is no need for intrusive IT backend integrations or custom API connectors to start achieving immediate operational leverage.
Are training grants or subsidies (SkillsFuture or IBF) available for this training?
Not at present. This programme is not listed as an accredited SkillsFuture or IBF course, so course-fee subsidies tied to those listings do not apply. We will update this page if that changes.
Is this suitable for MAS-regulated finance teams?
Yes, provided assistant use has been approved under your technology risk and outsourcing assessments. The session works inside that approval and does not extend it. Every exercise keeps a human preparer and reviewer, which is what those frameworks expect for material outputs.
Can we use real ledger data in the session?
Inside your own tenant and licences, yes. Anything shared with the trainer beforehand is an anonymised extract. Counterparty and customer details are removed unless your data protection officer has cleared their use.
Choose the assistant your team already licenses
- Microsoft Copilot training for finance & accounting teams — Teams licensed for Microsoft 365
- ChatGPT Enterprise training for finance & accounting teams — Teams building shared internal tools
- Claude Cowork training for finance & accounting teams — Knowledge, legal and research teams
- Gemini Enterprise training for finance & accounting teams — Teams working in Google Workspace
- Amazon Q Enterprise training for finance & accounting teams — Teams standardised on AWS