Scam detection for banks Market Outlook 2026–2033: Growth Drivers, Key Companies, Strategic Developments & Adoption Forecast — A Meticulous Next™ Foresight Brief

This Meticulous Next™ brief examines AI fraud and scam detection in payments, where models score each payment and customer session in milliseconds to stop fraud before money leaves the account. It tests how far AI detection becomes a standard control in payment security by 2033, and what that means for banks, payment firms, card networks and fraud detection providers.

UK Finance reported that total fraud losses in the United Kingdom in 2025 were GBP 1.28 billion, up 4%, with authorised push payment fraud losses of GBP 576.4 million, up 19%, across 248,070 confirmed cases, and unauthorised fraud losses of GBP 703.4 million, down 5%, with GBP 1.68 billion prevented. The conclusion is that scams, in which the customer is tricked into sending the payment, now rise while unauthorised fraud falls, and detection must focus on customer behaviour as well as card and account data.

Liability rules change the economics. The Payment Systems Regulator reported on 1 July 2026 that authorised push payment losses sent over Faster Payments fell by about 21% after the reimbursement requirement took effect, equal to about GBP 73 million a year, while the reimbursement rate rose from 54% to 65%, and the regulator announced a formal consultation for December 2026. The conclusion is that shared liability between sending and receiving banks gives banks a direct financial reason to invest in detection, and the same logic is spreading to other countries.

Vendors are responding with scam-specific products. Visa announced an enhanced A2A Protect service on 1 September 2026 that scores account-to-account transfers before money leaves the account, using technology from Featurespace, and states that it can raise detection by 75% in the first six months of deployment. The conclusion is that card networks are extending fraud scoring from card payments to bank transfers, and the vendor claims are the figures that banks will test in their own pilots.

The brief treats AI fraud detection as a high-impact signal with a seven year horizon to 2033. It identifies which banks, payment types and providers move first and what each means for banks, networks and fraud specialists. It is a focused 109-page decision brief that presents an indicative trajectory rather than a segmented market model.

AI fraud detection collects data on the payment, the device and the customer behaviour, scores the payment within milliseconds and sends high-risk payments to a warning, a hold or an investigator. Visa states that its enhanced service can cut unnecessary fraud alerts by over 40% and was shown in a proof of concept to identify 54% of fraud that passed existing bank systems, with a forecast of up to GBP 330 million of annual losses prevented. The conclusion is that false alerts are as important as detection, because every unnecessary hold adds cost and customer friction.

Consortium data improves detection. A pilot led by Pay.UK in May 2024 with Visa, Featurespace and Synectics Solutions reported a 40% uplift in fraud detection across three partners and over GBP 112 million of fraud detectable in a year, using historic data. The conclusion is that data from several banks reveals scam patterns that a single bank cannot see, and network operators are well placed to supply it.

Criminals are also using AI. The BioCatch and Nasdaq Verafin foreword to the UK Finance report states that criminals use AI to scale and refine attacks, including deepfakes during account opening. The conclusion is that identity checks and behavioural analytics must be upgraded together, and detection vendors that cover onboarding and payments have an advantage.

Payee checks and warnings support the models. The reimbursement scheme has increased the incentive of banks to invest in prevention, and the regulator notes that outcomes remain inconsistent because some losses fall outside its scope or limits. The conclusion is that rules set the demand for detection, and the scope of rules decides how much banks spend.

The limits are the cost of integration with bank systems, false positives and customer friction, and limits on data privacy and sharing. The brief tests each limit against the cost trajectory to 2033.

The scam detection for banks market, covering AI-based transaction monitoring and risk scoring, behavioural and device analytics, scam detection and customer warning services, identity and deepfake detection, and consortium data and case management sold to banks and payment firms, and excluding card chargeback services, anti-money laundering screening and general cybersecurity, is valued at USD 8.0–11.0 billion in 2026 and is projected to reach USD 20.02–27.52 billion by 2033, at a CAGR of 14%.

The 2026 base rests on AI transaction monitoring and risk scoring, behavioural and device analytics, scam detection and warning services, identity and deepfake detection and consortium data and case management sold in Europe, North America, Asia-Pacific and Latin America. Growth then follows the volume of payments screened, the share of banks using scam-specific models and the revenue earned per payment.

The forecast is sensitive to reimbursement rules in new countries, the pace of data sharing between banks and the growth of scams that use AI.

The base case assumes reimbursement rules spread to several major markets and network scores for transfers are widely sold, bringing the inflection to about 2028 and a 2033 value near the middle of the range. An accelerated case assumes rapid rule making and open data sharing, bringing the inflection to 2027 and lifting the 2033 value to the top of the range. A delayed case assumes limited rule making and privacy limits on data sharing, moving the inflection to 2031 and holding growth at the bottom of the range.

Growth drivers

• Reimbursement and liability rules.
• Rising scam and fraud losses.
• Growth of instant payments.
• Use of AI by criminals.

Enablers

• Model and data sharing advances.
• Network and consortium data.
• Loss reduction evidence.
• Central bank and regulator programmes.

Restraints and barriers

• Cost of integration with bank systems.
• False positives and customer friction.
• Data privacy and sharing limits.
• Skills and model oversight.

The direction of AI fraud detection is set by reimbursement and liability rules, bank and vendor investment, the growth of scams and AI-enabled attacks, advances in models and data sharing, and the readiness of banks and customers to accept holds and warnings.

The key participants in scam and fraud detection include Visa Inc., Mastercard Incorporated, BioCatch Ltd, Feedzai, Inc., Nasdaq, Inc., Experian plc, RELX PLC, Fair Isaac Corporation, ACI Worldwide, Inc., NICE Ltd., SAS Institute Inc., Socure Inc., Pay.UK Limited, Synectics Solutions Limited, Lloyds Banking Group plc, Barclays PLC, NatWest Group plc, HSBC Holdings plc, JPMorgan Chase & Co., Banco Santander, S.A., Bank of America Corporation, Wells Fargo & Company, Citigroup Inc., Capital One Financial Corporation, Fiserv, Inc., FIS, Worldline SA, Nexi S.p.A., Thales S.A., IDEMIA Group, Jumio Corporation, Sardine, Alloy, Inc., Darktrace plc, Palantir Technologies Inc., Oracle Corporation, Microsoft Corporation, Google LLC, Amazon.com, Inc., Stripe, Inc., PayPal Holdings, Inc., Adyen N.V., Revolut Group Holdings Ltd, Monzo Bank Limited, Starling Bank Limited, Nationwide Building Society, UK Finance, the Payment Systems Regulator, the Financial Conduct Authority, the European Commission and the Federal Reserve.

The brief profiles representative participants in each archetype and assesses which are positioned to lead as reimbursement rules spread.

Feedzai, BioCatch, FICO and NICE lead among fraud detection software providers, Visa and Mastercard lead among card networks and data providers, and Lloyds, Barclays and NatWest lead among UK banks. Competitive intensity is rising as card networks, bureaus and specialist vendors each offer scam detection for transfers.

Where value migrates.

In 2026 value sits with machine learning scoring at large banks and with early scam-specific products. By 2028 it moves toward network scores for bank transfers, consortium data and identity checks that cover onboarding and payments. By 2033 it settles with AI detection as a standard control in payment security, where network data, bank integration and proven loss reduction carry the margin. Margins hold for providers that can show lower losses and low false positives, and narrow for providers of rule-based systems without AI models.

UK scam losses up 19% in 2025, a 21% fall in Faster Payments scam losses after reimbursement and a card network launch of transfer scoring support growth, while integration cost, false positives and privacy limits hold back the pace, and vendor claims remain to be tested at banks.

Narrow rules, privacy limits on data sharing and growth of AI-enabled scams could slow progress. The brief therefore tracks four indicators: scam losses and reimbursement rates, detection rates and false positives, banks sharing data in consortia and countries with reimbursement rules.

Machine learning scoring at all large banks first, then scam detection and consortium data, then standard control status is the expected order of change.