Artificial Intelligence in Finance Can Advise, but It Can Also Be Convincingly Wrong

Artificial intelligence is changing how people look for information about loans, savings, investments and insurance. A tool can explain a complex term in simple language, adapt the answer to the user's situation and offer help to someone who feels uncomfortable approaching an adviser. The OECD warns, however, that independent evidence is still lacking on whether frequent use of such services actually improves households' long-term financial position. The most visible risk is an answer that sounds credible but is false. A model may confuse a tax rule, overlook a fee or recommend a product that does not meet the customer's needs. Users often cannot tell whether an answer is based on current regulation, an advertising instruction or a statistical estimate. An error in a financial decision may not become apparent for years, and correcting it can be more expensive than fixing an ordinary factual mistake. Commercial influence creates another risk. A provider may favour its own products, a paying partner or an offer that generates commission. Personalising an answer also requires sensitive information about income, debts, family and spending. People may not understand that their query could also be used to train the model further. The OECD therefore stresses privacy protection, clear disclosure of commercial relationships and the possibility of human review. The report states directly: “AI tools cannot be seen as a substitute for financial literacy.” A user must be able to recognise a suspicious promise, compare total costs and verify an important fact through an independent source. Otherwise, a convenient answer may weaken personal judgement. People with low digital skills, limited internet access or a language barrier are particularly vulnerable, even though the technology is often presented to them as a tool of inclusion. Banks, insurers and financial-technology companies need to distinguish a general explanation from advice tailored to an individual customer. The more a system knows about personal circumstances and recommends a specific action, the greater the risk of harm, conflicts of interest and provider liability. Practical safeguards include continuous testing of answers, records of the evidence used, warnings about uncertainty, a ban on hidden preference and an easy handover to a trained employee. Fraudsters can use the same tools to imitate the voice, image or communication style of a bank or relative. Financial institutions should therefore not separate the development of advisory tools from fraud prevention. They need consistent identity verification, clear rules for confirming transfers and customer education based on realistic scenarios. The economic benefit will arise when technology lowers the price of good advice, not when it merely shifts the cost of errors and fraud to the customer.

Artificial Intelligence in Finance Can Advise, but It Can Also Be Convincingly Wrong

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