AI in Finance and Fintech: 2026 Statistics and Trends Backed by Verified Data

The FBI recorded $893 million in losses tied to AI-related cybercrime complaints in 2025, the first year its annual report tracked AI as a separate category. Deloitte forecasts that generative AI could drive US fraud losses to $40 billion by 2027. Both figures are credible, but only the first reflects losses that have already occurred.

This distinction matters because statistics on AI in finance vary widely in quality. Some come from regulators, official reports, and bank filings. Others are from vendor surveys and market forecasts, then spread from article to article until the original source is difficult to trace.

We at Relevant build fintech software for banks, lenders, and payment companies, so our focus is on figures that can be verified. This guide brings together reliable statistics on AI in fintech and banking for 2026, with a clear reporting period and a link to the original source for each one. It covers the role of AI across lending, payments, fraud prevention, compliance, customer service, internal banking operations, and trading. It also covers regulation, employment, and which sources you can trust.

TL;DR

• 75% of UK financial firms used AI in 2024, up from 58% in 2022. However, only 34% said they fully understood the AI systems they use.
• In 2026, 65% of financial services professionals surveyed by NVIDIA said their companies actively use AI, up from 45% a year earlier.
• In an independent 2025 test, machine learning credit models approved nearly 4% more applicants while accepting about 9% fewer borrowers who later defaulted.
• Visa reported blocking more than $40 billion in attempted fraud in both FY2023 and FY2024.
• 23% of US consumers trust generative AI to make payments on their behalf, while agentic commerce still accounts for less than 1% of e-commerce.
• The FBI recorded $893 million in losses from AI-related cybercrime in 2025. Deloitte, meanwhile, forecasts that AI-enabled fraud losses in the US could reach $40 billion by 2027.
• HSBC detects 2 to 4 times more suspicious activity and generates over 60% fewer AML alerts after replacing rule-based systems with machine learning.
• Bank of America’s Erica reached 24.6 million active users in Q2 2026. In contrast, Klarna resumed hiring human customer service agents in May 2025.
• Agentic systems accounted for 31% of new AI use cases announced by the world’s 50 largest banks in Q1 2026, up from 15% in the previous quarter.
• EU rules for high-risk AI used in credit scoring will apply from 2 December 2027, 16 months later than originally planned.

Where AI in Finance Stands in 2026

Most financial firms already use AI, but far fewer fully understand how their systems work or produce results. The strongest evidence on adoption comes from regulators.

The Bank of England and the FCA surveyed 118 UK financial firms, including banks, insurers, asset managers, and non-bank lenders. In 2024, 75% of respondents were already using AI, up from 58% in 2022. Another 10% planned to adopt it within 3 years.

The findings also show that AI adoption remains limited in scope and relies heavily on external providers:

• Firms classified 62% of their AI use cases as low materiality and only 16% as high materiality.
• 34% of firms said they had a complete understanding of the AI systems they use, while 46% reported only a partial understanding.
• Third-party implementations accounted for one-third of all use cases, up from 17% in 2022.
• The three largest providers supplied 73% of cloud services, 44% of models, and 33% of data services.

In July 2026, HM Treasury confirmed that the FCA and the Bank of England were conducting another survey. Its results should provide a more recent benchmark for the 75% adoption figure.

Industry surveys tell a similar story but report different adoption rates. NVIDIA’s 6th annual survey covered more than 800 financial services professionals and ran from August to December 2025. It found that 65% of respondents worked for companies actively using AI, up from 45% a year earlier. It also reported that 89% had seen AI increase revenue and reduce costs.

These findings are useful, but the source matters. NVIDIA sells the hardware that powers many AI systems, so its commercial position matters when evaluating the reported financial benefits.

Who Spends the Money

Banking spends more on AI than any other industry tracked by IDC. Banks invested about $31.3 billion in technology in 2024, and IDC expects financial services to generate more than 20% of global AI spending from 2024 to 2028.

At the largest banks, this investment forms part of technology budgets comparable to the annual revenue of a midsize company:

ROI reporting hasn’t kept pace with AI spending. Of the 50 banks tracked by Evident, only 15 disclose their overall return on AI investment. In the payments sector, no company has reported realized or projected returns across its entire AI portfolio.

AI in Lending and Credit Scoring

Credit scoring offers some of the strongest evidence on how AI affects loan approvals, but the measured gains are smaller than many articles suggest. Vendor case studies often report approval increases of 40% or more. The strongest independent study so far found an improvement closer to 4%.

The evidence comes from a FinRegLab study published in July 2025 and funded by JPMorgan Chase and Capital One. FinRegLab is a nonprofit research organization. Its researchers trained models using credit bureau and bank account data from about 424,500 consumers, then tested the results against the performance of accounts opened in 2018 and 2019.

Compared with traditional logistic regression, machine learning produced several improvements:

• Predictive accuracy increased by about 2% on the ROC-AUC measure across all data types.
• At a 3% loss cutoff, which is typical for prime lenders, the approval rate rose from 65.18% to 67.66%. The number of approved applicants who later defaulted fell by about 9%.
• At a 7% cutoff, which is more typical for subprime lenders, the increase in approvals nearly disappeared. However, approvals of applicants who later defaulted fell by about 15%.
• Adding bank account cash flow data to credit data increased approvals by another 0.6% to 1.6% at the prime lending cutoff.

A 4% increase may sound modest, but it becomes significant at market scale. The study’s authors noted that similar cutoffs in 2023 saw about 55 million new credit card accounts and 3.8 million mortgages opened. A 4% increase at those volumes would equal roughly two million credit card accounts and 152,000 mortgages.

AI may have the greatest value for consumers whom traditional credit models struggle to assess. According to CFPB estimates updated in 2025, 2.7% of US adults had no credit file and 9.8% had a file too limited to generate a score as of December 2020. In 2022, Equifax identified 62 million consumers with thin credit files.

However, the FinRegLab sample included too few consumers without conventional credit histories to measure the effect reliably. This remains one of the biggest gaps in the available evidence.

For teams developing AI-driven financial forecasting and underwriting tools, the practical lesson is that the model and its data work together. The hybrid machine learning model, which combined credit and cash flow data, ranked first for accuracy and approvals across nearly every subgroup measured. This included consumers with low-to-moderate incomes and those with recent delinquencies.

AI in Payments

Payments bring together some of the oldest and newest uses of AI in finance. Card networks have used neural networks to assess transactions for three decades. By contrast, AI agents that can shop and pay for consumers have emerged only in the past two years, and trust in them remains low.

Fraud Scoring at Network Scale

Visa began using neural networks to assess transaction risk in 1993. Today, Visa Advanced Authorization scores every VisaNet transaction in about one millisecond. In a September 2025 letter to US regulators, Visa said it had blocked more than $40 billion in attempted fraud in both FY2023 and FY2024 while processing over 300 billion transactions annually.

Human investigators still handle threats that automated models miss. Visa formalized its scam disruption team in March 2025 after it prevented more than $350 million in attempted fraud in 2024. In a separate operation, it also shut down nearly 12,000 fraudulent merchants.

Further improvements come from updating the models within existing payment systems. One bank tracked by Evident rebuilt a fraud detection model for a specific payment type. The new model detected 64% more fraud by dollar value than its predecessor without increasing operating costs.

Agentic Commerce: Built Before It Is Wanted

Agentic commerce uses software agents to search, make purchasing decisions, and complete payments without human approval at every step. The payment infrastructure is ready, but consumer trust remains low.

The Harris Poll surveyed 2,065 US adults from 26 to 28 May 2026. Average x402 volume was about 14 cents per transaction, showing what these payment rails currently support: small machine-to-machine transfers rather than everyday consumer purchases.

Visa, through Intelligent Commerce, and Mastercard, through Agent Pay, are developing identity and permission systems for AI agents. For teams building mobile payment software, the key question for 2027 isn’t whether the technology works. It’s who will be liable when an agent makes the wrong purchase.

AI-Enabled Fraud: Measured Losses vs Forecasts

The most widely cited figure for AI-enabled fraud is a forecast. The first reported figure based on actual complaints arrived in April 2026 and was roughly 2% of that projection.

Deloitte’s Center for Financial Services estimates that generative AI could push US fraud losses from $12.3 billion in 2023 to $40 billion by 2027. That is its most aggressive scenario. Its conservative estimate is closer to $22 billion. Deloitte developed the forecast by assessing 26 fraud categories from FBI reports for their exposure to generative AI and projecting their growth under three adoption scenarios.

The FBI’s 2025 Internet Crime Report was the first edition to track AI as a separate category:

• Total reported losses reached $20.9 billion across 1,008,597 complaints, up 26% from $16.6 billion in 2024.
• The FBI received 22,364 AI-related complaints, with reported losses of $893 million.
• Business email compromise caused $3.05 billion in losses, including more than $30 million from cases with a confirmed AI component.
• Investment fraud was the largest category, with $8.65 billion in reported losses.

The FBI and Deloitte figures aren’t directly comparable. The FBI counts complaints in which victims or investigators identified an AI component, so it will miss cases where AI use went undetected or unreported. Deloitte models how large future losses could become under different adoption scenarios.

The evidence supports a more measured conclusion. AI-enabled fraud is real and now trackable, but reported losses remain small compared with fraud that doesn’t require AI.

The FBI data also points to where AI gives attackers the clearest advantage: email and voice impersonation. Traditional transaction-monitoring systems often struggle to detect these scams because the victim appears to authorize the payment.

AI in AML and Compliance

Anti-money laundering offers one of the clearest examples of AI solving a measurable problem. Rules-based monitoring flags so much legitimate activity that, during the initial review, more than 95% of alerts are false positives. About 98% never result in a suspicious activity report.

Starting in 2019, HSBC worked with Google Cloud to replace parts of its rules-based system with machine learning risk scores. The bank reports that the new system detects 2 to 4 times more suspicious activity while generating over 60% fewer alerts. According to HSBC’s head of financial crime compliance, processing billions of transactions across millions of accounts now takes a few days instead of several weeks.

AI has also produced measurable results in other compliance-heavy processes:

• ABN AMRO said its AI tool reduced corporate loan processing time by 40%.
• ING attributed part of a 2% reduction in operating costs to generative AI.

Banks are also looking beyond the largest cloud providers for specialized tools. Vendors outside the hyperscalers now account for 68% of bank AI deployments, with much of that activity concentrated in credit, AML, and treasury operations.

HSBC’s results shouldn’t be treated as the industry average. They come from a vendor and client case study involving a bank with years of data science investment, so they’re closer to an upper benchmark.

For most financial institutions, the main challenge is explaining the model’s decisions to regulators. Teams developing AI agents for compliance or audit automation should therefore build clear audit trails into the product from the first sprint.

AI in Customer Service

Bank of America and Klarna illustrate two very different approaches. Bank of America developed its assistant gradually and expanded it over time. Klarna moved quickly, reported a major reduction in staffing needs, and later resumed hiring people.

Bank of America: 8 Years of Erica

Bank of America launched Erica in 2018. By the end of 2025, the assistant had handled more than 3.2 billion interactions and served over 20 million clients, according to the bank’s annual report.

Growth continued in 2026. In the second quarter, active users increased 23% to 24.6 million, while interactions rose about 15% to 200 million. Digitally enabled sales accounted for 70% of the bank’s total sales.

Bank of America has also adapted the technology for its employees. More than 18,000 service representatives use EricaAssist, which provides guidance during live calls in under 3 seconds. A separate internal version of Erica is used by more than 90% of employees and has reduced calls to the IT service desk by 50%.

Klarna: The Correction

Klarna launched its AI assistant in February 2024. During its first month, it handled 2.3 million conversations, or two-thirds of all customer service chats. Klarna said this workload was equivalent to that of 700 full-time agents. The estimate later increased to 853 agents and $60 million in savings.

In May 2025, Klarna’s CEO said customers should always have the option to speak with a person, and the company resumed hiring human agents. The quarterly report citing $60 million in AI savings also showed customer service and operations costs rose from $42 million to $50 million year over year.

Klarna didn’t abandon its assistant. It continues to handle routine requests, while the CEO now describes access to human support as a premium service. He has also reported that revenue per employee increased from $300,000 to $1.3 million since 2022, while the company roughly halved its headcount through attrition.

The lesson for conversational AI in banking is to focus on routing rather than full replacement. Define which requests the assistant should handle, when it should transfer customers to a person, and how success will be measured across both channels.

AI Inside the Bank: Generative and Agentic AI

The fastest shift in 2026 is happening behind the scenes. Bank employees are using AI to draft documents, write code, conduct research, and manage routine processes. At the same time, banks are moving beyond chat assistants toward agents that can complete multi-step tasks.

Generative AI for Employees

JPMorgan’s LLM Suite gives employees controlled access to models from several providers without exposing bank data to public AI tools. In a February 2026 investor update, Jamie Dimon said about 150,000 employees use the platform each week and estimated it saves them around 4 hours.

However, the bank doesn’t include those saved hours in its net present value calculations or treat them as evidence of reduced staffing needs. More than 90% of JPMorgan’s engineers also use AI coding assistants.

That caution is important. Banking’s largest AI spender can measure adoption but isn’t yet recording employee time savings as a financial return. This reflects a broader shift toward improving existing tools rather than adding more of them. In NVIDIA’s survey, 41% of respondents said they planned to invest in optimizing AI workflows already in use.

Agentic AI in Banking

AI agents began moving beyond pilot programs in Q1 2026. Evident tracks publicly announced AI use cases from the world’s 50 largest banks and found that:

• Agentic systems accounted for a record 31% of new use cases in Q1 2026, up from 15% in Q4 2025.
• Most use cases focused on product and service operations as banks shifted from company-wide copilots to tools designed for specific workflows.
• Anthropic was the most frequently referenced vendor during the quarter.
• 38% of the announced use cases included reported outcomes.

NVIDIA’s survey provides a similar view from inside financial institutions. It found that 21% of respondents had deployed AI agents, while 42% were using or evaluating agentic AI.

Bank of Singapore offers one practical example. Its multi-agent system runs KYC research and onboarding checks in parallel, helping the bank process complex clients more quickly.

AI agent development requires stronger controls because they take action rather than simply make suggestions. Each action needs appropriate permissions, a complete activity log, and a human review point that reflects the level of risk.

AI in Trading and Capital Markets

Trading attracts more attention than almost any other use of AI in finance, yet reliable adoption data remains scarce. Regulators tend to focus on potential risks rather than AI’s share of market activity.

In evidence submitted to Parliament, the Bank of England reported that 11% of surveyed UK financial firms used AI for algorithmic trading. Another 9% planned to adopt it within three years. By comparison, 16% used AI for credit risk assessment and 41% used it to improve internal processes.

The IMF dedicated a chapter of its October 2024 Global Financial Stability Report to AI in capital markets. Patent filings and employment data suggest that adoption could rise significantly. The IMF also found that pricing patterns in some markets already show changes consistent with AI use.

The report identifies several specific risks and potential benefits:

• During market stress, AI models may converge on similar decisions, creating herd behavior and intensifying sell-offs.
• Under normal conditions, the same technology may support more diverse trading strategies and improve market resilience.
• Market-making and investment activity may shift further toward hedge funds and other nonbank firms, where regulators have less visibility.

No public source currently provides a reliable estimate of how much trading volume is driven by generative or agentic AI rather than older algorithmic systems. Claims that assign an exact market share should be treated with caution.

AI Regulation in Finance: The Dates That Matter

The EU AI Act classifies systems used to assess individual creditworthiness or calculate credit scores as high-risk. The same classification applies to AI used to price life and health insurance. Fraud detection systems are explicitly excluded.

The Digital Omnibus on AI, Regulation (EU) 2026/1744, moved the main compliance deadline back by 16 months. The updated timeline is:

• 1 August 2024: The AI Act entered into force.
• 2 February 2025: AI literacy obligations took effect.
• 2 August 2026: Transparency requirements under Article 50 took effect as scheduled.
• 2 December 2026: Watermarking and synthetic content disclosure requirements apply.
• 2 December 2027: High-risk requirements apply to standalone systems, including those used for credit scoring and insurance pricing.
• 2 August 2028: High-risk requirements apply to AI integrated into regulated products.

The delay changed the deadlines, not the scope of the rules. Credit models must still be documented, bias-tested, and subject to human oversight. Existing requirements for automated credit decisions also remain in force.

UK financial firms report different regulatory concerns. In the Bank of England and FCA survey, respondents identified data protection and privacy as the biggest constraints on AI adoption. Operational resilience, cybersecurity, and third-party requirements followed, along with the FCA’s Consumer Duty. The survey also found that 84% of firms have assigned a named person to oversee their AI framework.

In the US, fair lending laws apply to AI models without exception. Regulators also expect lenders to consider less discriminatory alternatives. However, FinRegLab notes that the administration has announced plans to eliminate disparate impact liability, making the US regulatory direction less settled than the EU’s.

Teams preparing for these requirements typically begin with an inventory of their AI systems and a compliance gap assessment, often supported by AI compliance services.

AI and Finance Jobs

Forecasts predict substantial job cuts, but headcount at the most AI-advanced banks has increased so far.

In January 2025, Bloomberg Intelligence surveyed 93 chief information and technology officers at major banks. On average, they expected AI to reduce their workforces by a net 3% over the next 3-5 years. Bloomberg Intelligence estimated that this could affect up to 200,000 jobs worldwide. Nearly 1 in 4 respondents expected cuts of between 5% and 10%, with back-office, middle-office, and operations roles considered the most exposed.

The same report projected that AI could increase banks’ combined pretax profits by 12% to 17% in 2027, adding as much as $180 billion.

Early headcount data from leading banks doesn’t yet support the job-cut forecasts. The five highest-ranked banks in the Evident AI Index added about 11,000 employees over the past year, according to their latest earnings reports. JPMorgan also doesn’t treat time saved through AI as a planned headcount reduction.

Klarna provides the clearest example of AI affecting staffing levels, but the reduction came through attrition and a hiring freeze rather than AI-related layoffs. The company’s later decision to resume hiring human agents also shows the limits of reducing staff in customer-facing roles.

Workforce composition is changing faster than total headcount. Banks continue to hire engineers, data scientists, and model risk specialists while reducing routine processing roles. This shift helps explain why many institutions use AI engineers on demand rather than building every capability in-house.

Which AI in Finance Statistics to Trust

Most AI in fintech statistics that appear at the top of search results come from a small group of sources, but they aren’t equally reliable. Before citing a figure, check who produced it, what it measures, and how the data was collected.

3 simple checks will catch most unreliable statistics. Does the figure include a clear reporting period? Does it link to the organization that produced the data rather than another article? Does it describe an actual result or a forecast? Every statistic in this article includes a reporting period and a link to the original source, and we clearly label all forecasts.

What the 2026 Numbers Add Up To

AI in finance now falls into 2 broad groups. The first is mature, measurable, and already funded. It includes fraud scoring, AML monitoring, credit models, and service assistants backed by years of production data. The second is newer and still measured mainly through announcements. It includes agents used inside banks, agentic payments, and generative AI tools whose productivity gains even JPMorgan isn’t ready to record as financial returns.

The difference appears throughout the data. Visa blocks more than $40 billion in attempted fraud each year, while agentic commerce accounts for less than 1% of e-commerce. FinRegLab measured an approval increase of about 4%, while some vendor presentations claim improvements ten times larger. The FBI recorded $893 million in AI-related fraud losses, while the most widely cited forecast projects up to $40 billion.

For financial companies setting their next technology budget, the strongest opportunities lie where these two groups meet. That means applying newer models and stronger controls to proven workflows such as underwriting, AML, and customer service. Our AI software development for fintech focuses on this work, from model risk documentation to production monitoring.

We update this page as regulators, banks, and researchers release new evidence. Every figure includes its reporting period and original source, so you can verify the data before citing it.

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