With AI now deeply embedded in financial decision-making, questions around governance, accountability, and ethical use are emerging as the technology reshapes industry practices and regulatory frameworks.
Artificial intelligence is moving from the back office into the heart of finance, but the machinery around it has not yet caught up. What began as software for sorting invoices or reconciling ledgers is increasingly being used to forecast cash flows, assess credit risk, flag fraud and even help answer personal tax and investment questions. The appeal is obvious: faster processing, fewer manual errors and quicker access to information. The harder question is who remains accountable when the technology gets it wrong.
The Times of India reports that AI is already being deployed across bookkeeping, compliance, reporting and reconciliation, while KPMG’s 2026 Global AI in Finance report suggests the shift is accelerating sharply. Based on a survey of 1,013 senior finance leaders across 20 countries and 13 sectors, the report found active AI use in finance had risen from 30% in 2024 to 75%, with 76% of organisations now using it in financial planning. KPMG also said 70% of organisations saw better decision-making quality, 71% faster decisions and 64% improved forecasting accuracy.
For companies, that means AI is no longer just a productivity tool. It is becoming an analytical layer that can scan large volumes of structured data, highlight anomalies and produce summaries that once took teams of people hours or days to assemble. Pei Fu Hsieh, co-founder of AI Accountant, told the Times of India that finance teams using AI are spending less time on manual entry, transaction categorisation and reconciliation. He said the gains show up in faster processing, fewer interventions, quicker book closures and more timely financial information. The same logic is reshaping how businesses and individuals access financial data: instead of waiting for reports, they can ask direct questions such as how much cash is available or who owes money.
That evolution is also changing tax work, but only up to a point. Swaroop Repaka, vice-president for product at ClearTax, told the newspaper that AI’s early use has centred on tax notices, litigation support and tax research, with the biggest gains now appearing in repetitive, rule-based tasks built on structured data. Even so, he said judgment-heavy questions such as tax positions, treaty interpretation and transfer pricing still require human involvement because they demand a defensible view rather than a quick answer. Rajosik Banerjee, partner and national head for risk and finance advisory at KPMG India, said the strongest adoption is in reconciliations, invoice processing, reporting and planning, but added that the real value will come from redesigning finance processes around AI-enabled workflows.
The same pattern is emerging in wealth management. Tushar Bopche, co-founder and chief executive of InvestValue, described the model as “HI + AI”, meaning human intelligence combined with artificial intelligence. In his view, AI can sort through thousands of data points and surface patterns quickly, but advisers still provide the context that machines lack: goals, risk appetite, family needs and changing circumstances. That distinction matters because an AI-generated recommendation can sound convincing while still being unsuitable. In finance, speed is useful, but speed alone is not the point.
The governance problem is where the stakes rise sharply. Animesh Sharma, chief technology officer at Indifi Technologies, argued that AI adoption must be treated as a governance exercise as much as a technology decision, with human checkpoints for outputs that affect financial statements, lending or regulatory filings. Pallav Chaturvedi, partner at Deloitte India, said India is advancing quickly on strategic AI use, but governance maturity is lagging. He noted that fewer than one in 10 organisations in India have the governance structures Deloitte considers necessary for trustworthy AI, while security, privacy and regulatory uncertainty remain major concerns.
Regulators are also moving towards tighter control. The Reserve Bank of India’s 2025 FREE-AI framework calls for governance across the full AI lifecycle, including model validation, ongoing monitoring and stronger safeguards for higher-risk uses. Its “People First” principle says AI should support human decision-making but defer to human judgement, while its accountability principle makes clear that responsibility cannot be handed over to the model or algorithm. India’s Digital Personal Data Protection Act, 2023, and the Digital Personal Data Protection Rules, 2025, add another layer of obligation around the handling of personal data, which is especially relevant for banks and financial firms dealing with sensitive customer information. KPMG and Deloitte have both said data minimisation, consent management and stronger data governance will be central to compliance.
The practical test comes when AI is used in situations that directly affect people’s money: a loan application, an investment recommendation, a tax calculation or a fraud alert. Rahul Katariya, founder of Divya Financial Services, told the Times of India that there must always be someone who understands the numbers, checks the result and takes responsibility for the final call. That means firms cannot treat AI as an autonomous decision-maker. They have to test its outputs, examine the data behind them, monitor for bias and errors, and keep audit trails that show how conclusions were reached.
Even the public-facing AI tools themselves reinforce that caution. When asked basic financial questions, ChatGPT, Claude and Grok gave broadly sensible starting points, but their replies were generic and lacked the personal detail needed for real-world decisions. That is the likely destination for finance as well: a layered system in which AI handles repetitive processing, supports analysis and speeds up monitoring, while humans remain responsible for judgement, context and accountability. The technology is already inside finance. The open question is how far institutions will let it go.
Disclaimer: This article is intended to inform and educate, not to recommend or endorse any financial product, investment or strategy. Please consider your own financial circumstances and seek professional advice where appropriate before making financial decisions.
