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How to deal with finance on socials

  //business-finance.news-articles.net/content/2025/09/16/how-to-deal-with-finance-on-socials.html
  Published in Business and Finance on Tuesday, September 16th 2025 at 1:46 GMT by The Financial Times
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AI’s New Frontier in Finance: How the Regulators and the Market Are Racing to Keep Pace

The financial services sector has long been a bellwether for technology, from the first computers that calculated bond yields to the mobile apps that let customers manage their portfolios on the go. Yet no sector has been as startled by the arrival of generative AI as the one that makes it all possible. A new FT piece, “The AI‑enabled future of banking” (https://www.ft.com/content/9d64c18d-9516-4438-a9ed-8de9432f2c68), opens the curtain on a world where language models and reinforcement‑learning agents are being deployed inside the vaults of the world’s largest banks, the underwriting desks of insurers, and the front‑end of fintech start‑ups. The article paints a picture that is both exciting and treacherous, and it turns to regulators, corporate leaders, and data scientists to show how the industry is trying to balance innovation against risk.

The All‑Seeing Eye of Generative AI

At the heart of the article is a simple observation: generative AI can read, synthesize, and produce human‑like text in real time. For banks, that means instant, highly customized loan offers, personalised risk assessments, and even automated responses to customer queries. A handful of banks—HSBC, JPMorgan Chase, and Deutsche Bank—are already testing AI‑powered chatbots that can draft credit memos in minutes, an eternity compared with the hours it used to take a human analyst. The piece notes that while the headline‑grabbing headlines in the tech press focus on image‑generating models, the financial industry is more interested in the “prompt engineering” that lets models understand regulatory language, policy statements, and the nuances of a borrower’s credit history.

The article also highlights a case study from a boutique fintech in Singapore that uses an AI model to predict loan default probabilities with 10 % higher accuracy than traditional logistic regression. The start‑up claims that its system learns from hundreds of thousands of anonymised transaction histories, spotting subtle patterns—such as a sudden change in payment behaviour—that a human would miss. That is the kind of competitive edge that has made AI a “game‑changer” for finance, the article argues.

Regulatory Fences and the Road to Compliance

The article is careful to remind readers that speed in deployment is a double‑edged sword. Regulators worldwide are racing to draft rules that can keep up with the technology without stifling it. In the UK, the Financial Conduct Authority (FCA) has issued a consultation paper outlining a “risk‑based” approach to AI. The paper, which the FT article quotes, says the FCA will look at model governance, explainability, and the potential for algorithmic bias. The FCA’s guidance stresses that banks must keep a human in the loop for any decision that materially affects a customer’s life, such as credit approvals or fraud detection.

In the European Union, the piece points to the European Commission’s Artificial Intelligence Act (the AI Act), a sweeping regulatory framework that classifies AI applications by risk. Banks using AI for credit risk assessment are considered to fall into the “high‑risk” category, and must therefore undergo a conformity assessment, maintain robust technical documentation, and undergo periodic audits. The article provides a link to the full text of the AI Act, which the author summarises as a “two‑tier” system: minimal requirements for low‑risk systems and a rigorous “certification” process for high‑risk systems.

The United States has a more fragmented regulatory landscape, with the Federal Reserve, the Office of the Comptroller of the Currency (OCC), and the Securities and Exchange Commission (SEC) each issuing their own guidelines. The article notes that the Federal Reserve’s new “AI & Emerging Technology” working group has published a set of principles that focus on data quality, model risk management, and cyber resilience. The piece links to a press release from the Fed, which emphasizes that “regulatory oversight will be a shared responsibility between federal regulators and the institutions themselves.”

The Dark Side: Bias, Cybersecurity, and Market Concentration

No story about AI in finance would be complete without a sober look at the risks. The FT article quotes Dr. Priya Singh, an AI ethics researcher at Oxford, who warns that generative AI can inadvertently amplify historical biases. “If a model is trained on loan data that reflects past discrimination, it will learn to replicate that discrimination,” she says. The article cites the example of an AI system deployed by a US lender that, when presented with a certain demographic profile, flagged the borrower as “high risk” 23 % more often than a control group. That kind of bias is not just a social problem; it’s a compliance problem that regulators are increasingly eager to crack down on.

Cybersecurity is another area of concern. Because generative AI can produce convincing phishing emails or mimic a customer’s voice, banks are investing heavily in AI‑driven security tools that can detect and neutralise such attacks in real time. The article links to a report from McKinsey that estimates the cost of AI‑driven cybercrime could reach $7.4 billion by 2027, underscoring the need for investment in secure architectures.

Finally, the article touches on market concentration. A handful of tech giants—Microsoft, Google, Amazon—are providing the cloud infrastructure that AI relies on. “There’s a risk that banks become locked into a single vendor’s ecosystem, which could limit their ability to switch providers if a price war or a service outage occurs,” warns a senior executive at a large European bank. The piece ends by calling for a “multi‑stakeholder approach” that includes academia, regulators, and the industry to ensure that AI remains a tool that amplifies human judgment rather than replaces it.

Bottom Line

The FT article offers a comprehensive snapshot of a sector in transition. Generative AI is moving from the realm of hype to that of practical application, with banks and fintechs scrambling to reap the benefits of faster, more accurate decision‑making. Yet the same technology carries risks that cannot be ignored: bias, regulatory non‑compliance, cyber threats, and the risk of vendor lock‑in. Regulators are stepping up, drafting frameworks that aim to mitigate those risks while still allowing the innovation that could reshape the financial services landscape. The race is on, and the outcome will shape how we think about credit, risk, and the future of money.