Is AI in Fintech Worth The Risk?

AI in fintech means using machine learning, generative AI and, increasingly, AI agents to make the decisions and run the workflows financial products depend on: verifying customers, scoring risk, flagging fraud and answering questions. It's no longer an experiment.

In NVIDIA's 2026 survey of more than 800 financial services professionals, 65% said their company actively uses AI, up from 45% a year earlier.

The question has shifted from whether to adopt AI to where it earns its keep, and what it takes to run it in one of the most regulated industries there is.

Why Are Financial Institutions Investing in AI Now?

Financial firms invest in AI because their core business is turning data into decisions about risk, and AI does that faster and at a larger scale than rule-based systems. The roots go back to the 2008 crisis, after which an international group of supervisors concluded that many firms' IT infrastructure was inadequate to monitor risk exposures accurately. Banks spent the following decade rebuilding their data foundations while fintech challengers showed what data-driven products could do.

Most institutions began with robotic process automation (RPA): software bots that follow fixed rules to move data between systems. RPA still carries a lot of back-office work, but it breaks down when inputs are messy or a decision needs judgment. That's where machine learning takes over, and generative AI extends it to documents and conversations.

The returns are showing up in budgets. In the same NVIDIA survey, 89% of respondents said AI is helping increase revenue and cut costs, and nearly all expect their AI budgets to hold or grow. These figures are self-reported, but they explain why AI has moved from the innovation lab to the operating plan.

Where Does AI Deliver Measurable Value in Fintech?

AI delivers the clearest value in three workflows: fraud detection, customer onboarding and customer service. Each runs at high volume, has a measurable cost, and generates enough historical data to learn from.

How Does AI Improve Fraud Detection in Fintech?

Machine learning models score each transaction in real time against patterns learned from millions of past ones. They catch fraud that fixed rules miss and block fewer legitimate payments. American Express, for example, improved fraud detection accuracy by 6% with deep learning models. At card-network volumes, even a single-digit gain means substantial losses avoided and fewer frustrated customers.

This works only with labeled fraud history, low-latency inference and a human review loop for borderline cases, because customers and regulators will ask why a payment was blocked.

How Does AI Speed Up KYC and Customer Onboarding?

Document AI reads IDs, passports and proof-of-address documents, checks them for signs of tampering and matches a selfie to the ID photo. For most applicants, that turns a manual review into a check that takes minutes.

The business case is conversion: every extra step or day in onboarding loses applicants. Keep a manual path for cases the model can't verify confidently, and make sure your vendor or model meets the identity-verification requirements your regulator sets.

Can Generative AI and AI Agents Handle Financial Customer Service?

LLM-based assistants can resolve routine requests, like checking a balance, freezing a card or opening a dispute, and draft replies for human agents on complex cases. AI agents go further and carry out multi-step tasks across systems. Adoption is still early: 42% of financial institutions are using or assessing agentic AI, and 21% have deployed agents.

The constraint is control. In finance, an agent acting on a hallucinated fact creates a compliance incident. Production setups ground the model in the company's own data (retrieval-augmented generation, or RAG), restrict which actions it can take, and log every step for audit.

What Data Foundations Does AI in Fintech Need?

An AI model is only as good as the data it learns from, so audit the data before choosing a model. Check four things:

• Consistency: the same customer, account or transaction is formatted the same way across core banking, CRM and payment systems.

• Accuracy: errors from manual entry, system glitches or migrations are found and corrected.

• Completeness: there are no gaps from data that was never collected or was lost between systems.

• Deduplication: records merged from several sources are checked for duplicates, which otherwise skew training.

Financial institutions start with an advantage: years of structured transaction records and standardized processes. Their weak spot is usually legacy systems that don't talk to each other, which is why integration work often takes a bigger share of an AI project than the model itself.

What Regulations Govern AI in Financial Services?

In the EU, AI in financial services falls under the AI Act, on top of GDPR and sector rules. The AI Act entered into force on August 1, 2024. It classifies AI used to assess individuals' creditworthiness or set credit scores, and to price life and health insurance, as high-risk. These systems need risk management, data governance, human oversight, logging and technical documentation. The Digital Omnibus amendment pushed the deadline for these obligations from August 2, 2026, to December 2, 2027.

Explainability is the hard part. A rules engine can show exactly why it reached a decision; a deep learning model can't easily do the same. GDPR already gives individuals the right not to be subject to solely automated decisions with significant effects and to contest them, so a credit model nobody can explain is also a legal liability. Since January 2025, DORA has also required EU financial entities to manage ICT risk, including risk from third-party technology providers such as AI vendors.

What Are the Real Costs and Risks of Adopting AI in Fintech?

Most of the cost of AI in fintech sits outside the model: data work, compliance and running the system reliably in production. Before committing a budget, weigh these trade-offs.

For identity verification or standard fraud screening, buying from a mature vendor usually beats building an in-house model on cost and time to market. Build when the model is your differentiator, or when your proprietary data gives you an edge no vendor has.

Hosted LLMs from major cloud providers now come with enterprise data controls and regional hosting, which makes them a production option. Usage-based pricing can still surprise you at scale. Self-hosting open-weight models gives you more control over data and cost but needs infrastructure and MLOps skills. Open source is on the table: 84% of respondents in NVIDIA's 2026 survey called it important to their AI strategy.

Talent is the next constraint. You need people who understand machine learning, financial regulation and your product at the same time. That combination is scarce, whether you hire for it or partner for it.

Because ROI is hard to predict, run a proof of concept with a success metric agreed in advance, and be willing to stop projects that don't hit it.

And sometimes the right answer is not to use machine learning at all. If a process is stable, rule-based and runs on structured data, a rules engine or RPA is cheaper, easier to audit and easier to explain to a regulator.

How Should a Fintech Get Started With AI?

AI in fintech comes down to discipline more than technology. Pick one workflow with a measurable cost, make sure the data can support it, design for explainability and human oversight from the start, and expand only once it works in production. Trust is the industry's core asset, and an AI system nobody can explain or control puts it at risk.

If you're weighing where AI fits in your financial product, Monterail's AI development team can help you assess the use case, the data and the build-or-buy decision before you commit to a roadmap.