Technology and AI in finance
How we use analytics, automation, and machine learning across our work, and the rules we follow when we do.
Machine learning can read every contract and test every journal. Someone still has to sign the conclusion.
Much of finance work has always been limited by how much a team can look at. Auditors sample. Forecasters extrapolate. Contract reviews stop at the largest agreements. Analytics and machine learning remove a good deal of that limit.
We use them where they let us examine a whole population, see further ahead, or take routine volume off a team that should be spending its time on exceptions. We use them inside the same discipline that governs the rest of our work: every output is reviewed by a person, every model can be explained, and client data is kept under the same confidentiality it has always had.
Where technology changes the work
Financial Reporting
AI-assisted review reads whole contract populations for lease and revenue terms, so IFRS 16 and IFRS 15 conclusions rest on every contract rather than a sample. A person checks each extraction before it reaches the accounts.
Audit & Assurance
Full-population analytics test every journal entry rather than a sample: postings outside working hours, round amounts, unusual combinations of user and account, and duplicate payments. Exceptions arrive ranked by risk, and a person investigates each one.
Finance Transformation
Bank, intercompany, and vendor reconciliations matched automatically, with machine learning handling the near-matches that fixed rules miss, and invoices captured from documents rather than typed in. Routine volume leaves the close, and the team spends its time on exceptions.
Cross-Border Advisory
Monthly monitoring that matches export invoices, bank realisations, and EDPMS status and flags entries as they age, and transfer pricing benchmarks refreshed from databases each year instead of rebuilt by hand.
Structuring & Tax
Withholding and indirect tax analytics across entire vendor and customer populations, catching rate mismatches, missing PANs, and credits that do not agree to returns, alongside scenario models that compare the tax cost of each structuring route before a decision is taken.
Performance & Analytics
Revenue, demand, collections, and cash forecast with machine learning on the company’s own history, each with a range and the drivers behind it, and refreshed every month as actuals come in.
Sustainability & ESG
Utility bills, meter readings, and fuel invoices captured automatically into controlled registers, with anomaly checks that flag a site whose figures move out of line before the number reaches the report.
Audit-readiness diagnostic
An interactive self-assessment that scores readiness across five areas and points to the fixes that matter most.
A forecast you can question
A budget set months ago is a single guess. A machine learning forecast updates as actuals arrive, shows a range that widens with the horizon, and explains which drivers moved it. That last part matters most, because a forecast nobody can explain should not go in front of a board.
Performance management and analyticsA machine learning forecast gives a range and the reasons behind it, not only a number
Illustrative monthly revenue with a static budget and a machine learning forecast, ₹ crore
Illustrative figures. The interval widens with the horizon; the driver attribution shows which inputs account for the change against last year, so finance can challenge the forecast before it reaches the board.
Source: SA&A analysis
Soni Aditya & Associates
The rules we work by
A person reviews every output
Models extract, match, rank, and forecast. People check the results and take responsibility for the conclusions.
If it cannot be explained, it is not used
Every model comes with the drivers behind its results, so finance teams, auditors, and boards can challenge it.
Client data stays protected
We do not put client data into public AI tools. Data is kept in controlled environments, only for as long as the engagement needs it.
Proportionate by design
We use a model where it improves coverage, accuracy, or speed enough to matter. Plenty of problems are still best solved with a well-built spreadsheet.
Where technology helps, and where it does not
Describe the forecast, the reconciliation, or the contract population. We will say whether a model earns its place, or whether a spreadsheet is enough.