Despite the challenges that come with using it, AI is fast becoming a core part of fintech strategy.
Across fraud detection, AML, customer service, credit, payments and compliance, AI is helping firms make faster decisions, reduce manual work and deliver more relevant experiences at scale.
For many fintechs, there’s clear appeal: better risk control, lower operating costs and a smoother journey for customers.
But the real story centres on using data more intelligently, spotting patterns humans would miss and improving outcomes in highly regulated, high-volume environments.
This week's Top 10 looks at the areas where AI is already making the biggest impact.
10. Fraud
Companies using AI in this way: Stripe, PayPal, Revolut, Monzo and HSBC
AI has become central to fraud prevention because it can spot suspicious patterns at machine speed across huge volumes of transactions.
Instead of relying only on static rules, models can learn from changing behaviour, device signals, location data and spending patterns to flag anomalies in real time.
As a result, firms can reduce losses while keeping false positives lower, so genuine customers face less friction.
The strongest systems combine machine learning with human review, creating a faster and more adaptive fraud stack.
9. Hyper-personalisation and customer experience
Companies using AI in this way: Revolut, Monzo, Starling Bank, Klarna and Capital One
Hyper-personalisation is where AI becomes a customer experience differentiator rather than just an operational tool.
By combining behavioural data, transactional history and context, AI enables fintechs to tailor product prompts, messaging, offers and app journeys to individual users.
As a result, engagement is improved, product usage is increased and digital experiences feel more relevant.
It also helps firms surface the right action at the right time, whether that is a savings prompt, a credit offer or a payment reminder – without becoming intrusive.
8. Automation and efficiency
Companies using AI in this way: UiPath, HSBC, Lloyds Banking Group, Deutsche Bank and JPMorgan Chase
AI can improve the efficiency of back-office operations by automating repetitive tasks such as document processing, reconciliation, data extraction and workflow triage.
These gains free teams up from doing manual admin, while AI also reduces the risk of human error in high-volume processes.
In fintech, this can make onboarding, operations, finance and compliance functions more scalable without a matching rise in headcount.
It also helps firms respond faster to customer requests and regulatory obligations. The biggest value often comes from combining AI with existing automation tools, creating a more intelligent workflow rather than a fully hands-off system.
7. Cybersecurity
Companies using AI in this way: Microsoft, CrowdStrike, Palo Alto Networks, Darktrace and Santander
AI is playing a growing role in cybersecurity because fintechs face constant attacks across payments, accounts, endpoints and internal systems.
Machine learning can identify unusual behaviour, spot compromised credentials and flag threats that traditional rules may miss.
That matters because cyber risk in financial services is about continuous monitoring across a highly connected stack rather than one-off breaches.
AI can also support threat intelligence by analysing large volumes of logs and alerts much faster than manual teams alone.
6. Identity verification
Companies using AI in this way: Onfido, Jumio, Veriff, Sumsub and Trulioo
Identity verification is a major AI use case because fintechs need to onboard customers quickly without weakening security.
AI can compare documents, assess selfie or biometric checks, analyse device signals and detect inconsistencies across user data in seconds.
As a result, onboarding is sped up while helping firms catch impersonation, synthetic identities and document fraud earlier.
It also reduces manual review for routine cases, which is important for digital-first firms scaling rapidly.
5. Payments optimisation
Companies using AI in this way: Stripe, Adyen, Checkout.com, Worldpay and PayPal
AI is helping fintechs optimise payments by improving routing, timing, authorisation rates and conversion.
In card and digital payments, even small gains can have a big commercial impact, especially for high-volume businesses.
AI models can learn which routes, payment methods or retry strategies are most likely to succeed for a given transaction, reducing failures and unnecessary declines.
They can also help reduce fraud-related friction by balancing risk and approval rates more intelligently.
4. Credit risk and underwriting
Companies using AI in this way: Upstart, Zest AI, OakNorth, Klarna and Experian
Credit scoring and underwriting are being reshaped by AI because models can assess more data points than traditional approaches alone.
By analysing transaction history, cash flow, device data and behavioural signals, lenders can make faster and sometimes more inclusive decisions. This can be especially valuable for thin-file borrowers or customers with limited credit histories.
Although AI does not remove risk, it can improve its pricing and identification when deployed carefully. The key is transparency, explainability and good governance.
3. Personalised financial advice
Companies using AI in this way: Moneybox, Plum, Revolut, Monzo and Nutmeg
AI is increasingly used to deliver personalised financial advice by analysing spending patterns, income trends, savings behaviour and broader financial goals.
Rather than offering generic tips, systems can tailor nudges and recommendations to each customer’s circumstances.
That might include suggesting savings targets, flagging overspending, or highlighting more suitable products and budgeting actions.
Used well, AI can make financial guidance more accessible to mainstream users who may not otherwise seek out a traditional adviser.
2. Customer services assistants
Companies using AI in this way: Klarna, Bank of America, NatWest, Lloyds Banking Group and Capital One
AI-powered customer service assistants are changing how fintechs handle everyday queries by offering instant, 24/7 support.
These tools can answer balance questions, explain card issues, guide users through onboarding and resolve simple account problems without human intervention.
The best examples use natural language processing to understand intent and route more complex cases to an agent when needed.
The result? Faster support for customers that’s cheaper for businesses, as well as also improving consistency.
1. AML
Companies using AI in this way: ComplyAdvantage, HSBC, Standard Chartered, Revolut and Coinbase
Anti-money laundering is one of the clearest use cases for AI in fintech.
This is because compliance teams must sift through massive volumes of alerts and transactions. AI can help prioritise suspicious activity, detect unusual network links and reduce noise from routine customer behaviour.
That means analysts spend less time chasing low-value alerts and more time on cases that matter.
It also improves ongoing monitoring by spotting new laundering typologies faster than rule-based systems alone.
