AI in finance is no longer a pilot project tucked away in an innovation lab. Banks use it to stop fraudulent payments, lenders use it to assess credit risk, and accounting firms are starting to rely on AI assistants for research that used to take hours. Yet finance is also one of the least forgiving places to deploy AI, because a wrong answer can mean a compliance breach or a real financial loss. This article looks at where AI is delivering value today, why generative AI raises new questions, and what separates a useful financial AI tool from a risky one.
1. Where AI is already used in finance
Most of the AI running in financial services today is not the chatbot kind. It is machine learning working quietly in the background, analyzing large volumes of transactions and records faster than any human team could. Three areas stand out:
1.1. Fraud detection and risk monitoring
Fraud detection was one of the earliest successful uses of machine learning in finance. Instead of relying only on fixed rules such as “flag any transfer above a certain amount”, models learn what normal behavior looks like for each account and flag activity that does not fit. This allows banks and payment providers to catch suspicious transactions in real time while reducing the number of legitimate payments that get blocked by mistake.
The same approach is used for anti-money laundering checks, where AI helps compliance teams sort through thousands of alerts and focus on the cases that genuinely need human review.
1.2. Credit scoring and lending decisions
Lenders use AI models to assess credit risk more quickly and, in some cases, to evaluate applicants who have little traditional credit history. By looking at a wider range of data, these models can speed up approvals and widen access to credit.
This is also an area where caution is essential. A model trained on biased historical data can reproduce that bias at scale, and applicants have a right to understand why they were rejected. For this reason, regulators treat AI in lending seriously. The EU AI Act, for example, classifies AI systems used to assess the creditworthiness of individuals as high-risk, which brings stricter requirements for transparency and human oversight.
1.3. Customer service and back-office automation
On the customer side, virtual assistants in banking apps now handle routine requests such as checking balances, tracking spending, or explaining fees. Behind the scenes, AI is used for tasks that are repetitive but time-consuming:
- Extracting data from invoices, receipts, and contracts
- Matching transactions during account reconciliation
- Drafting first versions of financial reports and summaries
None of these replace finance professionals, but they free up time for work that requires judgment.
Source: roh.co
2. Generative AI and the rise of financial copilots
Large language models (LLMs) have opened a different category of use. Rather than scoring a transaction or classifying a document, they can read a question written in plain language, search through complex material and produce an explanation. This has led to “copilot” tools designed to support accountants, tax advisors, analysts and compliance officers in their daily work.
2.1. From answering questions to supporting professional judgment
The appeal is easy to see. A tax advisor dealing with an unusual client situation may need to check several pieces of legislation, rulings and guidance notes. A well-built AI copilot can surface the relevant material, summarize it and suggest how it might apply, leaving the professional to make the final decision.
The keyword is “support”. In regulated fields, the professional remains accountable for the advice given. A copilot is valuable only if it makes that person faster and better informed, not if it encourages them to accept answers without checking.
2.2. The hallucination problem
The main weakness of general-purpose LLMs is that they can produce answers that sound confident but are wrong, a problem commonly known as hallucination. In casual use, this is an inconvenience. In finance, it can be costly. There have already been widely reported cases in the legal field where professionals submitted AI-generated documents citing court cases that did not exist.
For financial and tax work, the lesson is clear: an AI tool that cannot show where its answer came from should not be trusted with decisions that carry legal or financial consequences.
Source: dongascience
3. What makes an AI tool trustworthy in finance
Building AI for finance is therefore less about choosing the most powerful model and more about designing the system around it. In practice, trustworthy tools tend to meet three requirements.
3.1. Accuracy backed by traceable sources
Finance and tax rules change frequently, and small details often decide the outcome. A useful tool should retrieve information from verified, up-to-date sources rather than relying only on what the model learned during training. A common technique is retrieval-augmented generation (RAG), where the system first searches a curated knowledge base and then uses those documents to generate its answer. Equally important, the answer should point users to the exact legislation or guidance it is based on, so they can verify it themselves.
Insight from a PowerGate project – an AI tax copilot in Australia: PowerGate worked with an Australian client to build an AI Tax Copilot, described as the first AI copilot of its kind for the country’s tax and finance professionals. Our team developed the front-end, back-end, and API system for the product.
One feature that made a practical difference was automatic reference checking: when the assistant answers a question, users can go straight to the relevant legislation instead of searching for it separately. This keeps every answer open to verification, which matters in a field where professionals are accountable for the advice they give. In 2024, the product competed against more than 250 startups over three rounds and took third place in Australia’s first AI Sprint.
3.2. Explainable reasoning
Finance professionals need to understand why an AI tool concluded, not just what the conclusion is. A tool that breaks a complex issue into clear steps, shows the reasoning behind its suggestion and highlights any assumptions is far easier to check than one that returns a single answer. Explainability also matters for regulators, auditors and, in lending, for customers who have the right to know how decisions about them were made.
In the tax copilot project mentioned above, this was a core design goal: the assistant explains the reasoning behind each suggestion and breaks complex tax issues into clear, practical steps that a professional can review.
3.3. Privacy and data security
Financial data is among the most sensitive information a company holds. Any AI system that processes client records, tax files or transaction histories must be designed with privacy in mind from the start. This includes:
- Clear rules on what data is sent to third-party AI providers and whether it can be used for model training
- Encryption and strict access control for stored data
- Compliance with relevant regulations, such as GDPR in Europe or the Privacy Act in Australia
- Audit logs that record how the system was used and what it produced
4. Practical advice for teams building AI finance products
For companies planning to bring AI into a financial product, a few principles help avoid expensive mistakes:
- Start with a narrow, well-defined problem, such as a specific type of tax question or document, rather than a general assistant. Start with a narrow, well-defined problem rather than a general assistant. The Australian tax copilot by PowerGate Software, for instance, was built on a version of ChatGPT customized for tax work, supported by a large and diverse knowledge base.
- Invest in the quality of the knowledge base, because the model can only be as reliable as the sources it draws from
- Keep a human in the loop for decisions with legal or financial consequences
- Test the system with real professionals and real edge cases before launch, not just with sample questions
- Plan for regulatory changes, as rules on AI in financial services are still evolving in many markets
AI in finance delivers the most value when it is treated as a tool for professionals rather than a replacement for them. Fraud detection, credit assessment and back-office automation have already proven their worth, and generative AI copilots are now extending that support to research and advisory work. Whether these tools succeed depends less on the model itself and more on accuracy, traceable sources, explainable reasoning and strong data protection. Teams that build with those foundations in place are far more likely to earn the trust that financial services require.
