How Banks Can Catch Up to Fintechs on Artificial Intelligence

The latest banking AI trends tell a clear story: artificial intelligence (AI), and agentic AI in particular, is reshaping how financial institutions make money and hold onto it. But the shift isn't happening evenly. We examined over 600 AI projects across roughly 30 idea clusters to see where things actually stand. We tracked initiatives launched after ChatGPT's release in late 2022 through mid-2025, comparing what fintechs were building against what incumbent banks and payment companies were shipping. The gap is real. Fintechs aren't experimenting faster. They're deploying at scale while many traditional banks are still stuck in pilot mode. According to McKinsey's analysis, the AI in banking market is projected to reach $315.5 billion by 2033, growing at a 31.83% annual rate. That's a massive opportunity. Banks that don't move now risk missing it entirely. We'll break down what's working, what isn't, and what banks need to do to close the gap with their fintech competitors.

Incumbent banks are still far behind fintechs when it comes to AI implementation with measurable business impact. The picture from our dataset makes this obvious: we looked at approximately 4,000 of the world's largest fintechs by revenue and valuation. Even though fintechs make up only 40% of the dataset, they account for nearly 70% of all AI initiatives. Striking, but not surprising if you've watched how these two groups operate. Fintechs move fast because they can. They don't carry decades of technical debt. Their teams are smaller, flatter, built around shipping products rather than managing committees. When a fintech wants to test an AI-powered credit scoring model, the time from decision to deployment is measured in weeks. At a large bank, the same decision can take months just to clear internal approval.

Why Banks Fall Behind on Banking AI

Banks face a triple constraint that fintechs largely avoid:

• Regulatory overhead. Every AI model that touches customer data needs compliance review, explainability documentation, and audit trails. The EU AI Act, which takes full effect for high-risk banking systems in August 2026, adds another layer. Credit scoring, AML monitoring, and automated lending tools must now meet strict requirements around transparency and human oversight, with penalties reaching up to 7% of global annual turnover.
• Fragmented tech stacks. Most large banks run on a patchwork of legacy systems, some dating back to the 1990s or earlier. Integrating machine learning models into COBOL-based core banking platforms is difficult and expensive. Digital transformation requires middleware, API layers, and often a full cloud migration before any AI model can access production data.
• Organizational inertia. Banks are hierarchical by design. Getting alignment across risk, compliance, IT, and business units on a single AI initiative requires political capital that most project sponsors simply don't have.
• Workforce readiness. Most bank employees were hired for process execution, not AI oversight. The shift toward agentic systems means retraining staff to supervise algorithms and interpret model outputs. That skills gap doesn't close overnight. Fintechs have been quick to deploy AI in analytics, trading, and portfolio management. Meanwhile, plenty of banks are still trying out interesting ideas and struggling to move them from pilot to production.

After a period of intense experimentation, the overall rate of AI adoption in financial services is flattening out. Many applications have become table stakes. AI-powered conversational assistants, financial close automation, and basic fraud screening tools are now standard features, deployed at roughly the same pace as products that don't specifically target AI. The interesting growth is happening at the edges.

Where Banking AI Growth Is Concentrated

The applications with the fastest adoption rates are primarily agentic and focused on revenue:

• AI-based multi-asset trading platforms that make algorithmic decisions in real time
• Predictive analytics and decision management systems that apply machine learning and natural language processing to detect trends and assess risk
• Autonomous customer service agents that handle complete workflows without human handoff, using NLP to understand context and intent
• Open banking integrations where AI models sit on top of shared API ecosystems, pulling data from multiple providers to build richer customer profiles Fintechs dominate these high-growth categories. In our view, that gap comes from a reluctance among banks to ship AI products directly to customers. Our dataset on AI-related launches includes roughly 600 product launches. That's about 3% of all product and service launches in the same period (2022 through mid-2025). Three percent. That number should worry bank executives. It means the vast majority of digital banking innovation is still happening without AI at its core. Fintechs, meanwhile, are building entire business models around it.

From what we've seen, fraud detection is the area where banking AI has the most proven track record. And the need keeps growing. Global credit card fraud alone is projected to reach $43 billion by the end of 2026, according to industry estimates. Traditional rule-based systems can't keep up with the volume or sophistication of modern fraud patterns.

How AI Changes Fraud Detection

Machine learning models analyze transaction patterns in real time, flagging anomalies that rule-based systems miss. The difference is significant.

• Real-time scoring. ML models evaluate thousands of variables per transaction in milliseconds, catching synthetic identity fraud and account takeovers that static rules can't detect.
• Adaptive learning. Unlike rule-based systems, AI models continuously retrain on new fraud patterns. This shrinks the window between a new attack vector and the system's ability to catch it.
• False positive reduction. This is where the financial impact gets real. Traditional systems generate enormous volumes of false positives, each requiring manual review. Banks using AI-powered fraud screening report 50-70% reductions in false positive rates.

Credit Scoring and Risk Assessment

AI-driven credit scoring is another area where banks are starting to catch up. Traditional credit models rely on a narrow set of variables: payment history, outstanding debt, length of credit history. Machine learning models can pull in thousands of alternative data points, from utility payments to spending patterns, and build a more accurate risk profile. For banks serving populations with thin credit files, this matters a lot. It opens entirely new customer segments that were previously unscoreable.

If there's one area where the ROI of banking AI is hard to argue with, it's KYC and AML compliance. The numbers speak for themselves: banks typically assign 10-15% of their full-time employees to KYC and AML activities. Despite increasing compliance spending by up to 10% annually, the financial industry detects only about 2% of global financial crime flows. That mismatch between investment and outcome is extraordinary.

The False Positive Problem

In traditional anti-money laundering workflows, up to 95% of alerts are false positives. Building a single Suspicious Activity Report (SAR) can take four or more days. Multiply that across the thousands of alerts a large bank processes monthly, and you've got a compliance function that consumes enormous resources while catching very little actual crime. AI changes the economics of this entirely. Accenture's research on agentic AI in banking documented a 99% reduction in KYC ingestion time for institutions that deployed AI-powered document analysis and identity verification.

What AI-Powered Compliance Looks Like

• Perpetual KYC. Instead of periodic reviews, AI enables continuous risk reassessment throughout the customer lifecycle. It catches changes in behavior patterns that point-in-time reviews miss.
• Intelligent document processing. ML models extract and validate information from identity documents and corporate filings at speeds no human team can match.
• Network analysis. Graph-based AI models map transaction networks to identify money laundering patterns spanning multiple accounts and jurisdictions. For enterprise technology leaders evaluating where to start with AI, compliance automation offers the clearest path to measurable returns.

There's a lot of confusion in the market about what these terms actually mean, and the distinction matters for banking AI strategy. Generative AI produces content. It drafts emails, summarizes documents, and generates code. In banking, it powers coding co-pilots, chat-your-data interfaces, and personalized marketing. Useful, yes, but it's fundamentally a productivity tool. You ask, it answers. Agentic AI produces outcomes. It goes beyond generating a response: it perceives a situation, makes a decision, and takes action. An agentic AI system can validate a loan application, route it for processing, run compliance checks, and notify the customer without human intervention.

The Banking Implications

Consider a practical example. A generative AI tool might draft a personalized loan approval letter. An agentic AI system processes the entire loan: application intake, underwriting, approval, disbursement. The shift from generative to agentic is where the real banking AI transformation happens. According to BCG, approximately 70% of financial services firms are now exploring agentic AI, but only 14% have deployed it at full scale. The 14% figure is where the opportunity sits. Banks that move to production-grade agentic systems now will have a real edge over those still running generative AI experiments.

Where Each Type Fits

Customer experience is where the fintech advantage is most visible to end users. Fintechs have set the digital banking bar: instant approvals, proactive spending insights, real-time notifications. Customers now expect that same experience from their bank.

Virtual Assistants and Chatbots

Bank of America's Erica is the most cited example, and for good reason. The AI assistant has handled over 676 million client interactions since launch. Erica answers questions, sends alerts, helps with budgeting, and routes complex issues to human advisors. But Erica is the exception. Most bank chatbots are glorified FAQ pages that frustrate customers more than they help. The gap between what's possible and what most banks actually deliver is enormous.

Personalization at Scale

AI-driven personalization goes beyond product recommendations. Modern systems analyze spending patterns and income flows to deliver timely, relevant financial guidance. Discovery Bank in South Africa offers a financial wellness tool through WhatsApp, where customers ask things like "How much did I spend on coffee last month?" and get instant, personalized answers. That kind of contextual, conversational interaction is where customer experience meets real business value. Banks that implement it well see measurable improvements in engagement and retention.

The Personalization Gap

The problem for most banks isn't the AI technology. It's the data. Customer data is typically spread across dozens of siloed systems: accounts, cards, mortgages, investments. Building a unified customer view that feeds a personalization engine requires significant data engineering work, and usually a custom software investment that most banks have been reluctant to make.

There's no shortage of optimistic projections about AI's potential value in banking. McKinsey estimates $200-340 billion in annual potential for global banking from AI adoption. But what are institutions actually seeing today?

Early Adopter Returns

The data from firms that have moved past pilots is encouraging. According to research compiled by Neurons Lab, early adopters of agentic AI in financial services are reporting 2.3x returns on their AI investments within 13 months. A fast payback cycle for enterprise technology, by any measure. A few specific case studies put more color on these numbers:

• JPMorgan Chase allocates roughly $2 billion to AI from an $18 billion technology budget. Over 150,000 employees use large language models weekly. The bank's LAW system handles legal document review with 92.9% accuracy, and its EVEE assistant has freed hundreds of call center agents for proactive client outreach.
• Klarna's AI assistant handles two-thirds of all customer service chats within the first month of launch, doing the equivalent work of 700 full-time agents. Estimated annual savings: $40 million.
• Bank of America has committed $4 billion to AI and related initiatives, rolling out Salesforce's Agentforce to 1,000 financial advisors.

Where ROI Is Strongest

The highest returns aren't coming from flashy customer-facing features. They're coming from operations:

• 30-50% reduction in manual workloads for tasks like document processing and compliance review
• 50-70% fewer false positives in fraud detection, directly cutting investigation costs
• 99% faster KYC document ingestion at institutions using AI-powered processing For banks evaluating their AI strategy, the lesson is clear: start where the operational pain is greatest, not where the marketing story is shiniest.

Understanding the banking AI trends is one thing. Actually implementing AI in a large financial institution is another. The barriers are real, and ignoring them is why so many bank AI projects stall after the pilot phase.

Legacy System Modernization

Most large banks run core banking operations on mainframe systems that are 20-40 years old. These systems work. They process millions of transactions daily. But they were never designed to integrate with modern AI models. Getting real-time data out of a COBOL-based system and into a machine learning pipeline requires middleware, APIs, and often a cloud migration strategy. The cost of digital transformation is substantial. But the cost of not modernizing is growing faster, because every year the gap between what AI can do and what legacy infrastructure allows gets wider.

Data Quality and Data Governance

AI models are only as good as the data they're trained on. In banking, data quality problems are common:

• Inconsistent formats across acquired systems. Most large banks have completed multiple mergers, each bringing its own data schema.
• Missing or incomplete records in customer databases.
• Siloed storage, where customer information for accounts, cards, and loans lives in separate, disconnected systems. Building a reliable data foundation and a clear data governance framework isn't glamorous work, but it's the prerequisite for every AI initiative that follows. Without centralized data governance, even the best models produce unreliable outputs.

Governance and Explainability

Regulators expect banks to explain how AI models make decisions, especially for credit and fraud determinations. That means black-box models aren't acceptable in many banking contexts. Banks need explainable AI frameworks and model risk management processes, plus ongoing monitoring. According to industry surveys, 48% of financial institutions cite governance as their top barrier to AI adoption, and 30% flag data privacy concerns. These aren't problems you solve once. They require ongoing investment in people and processes, plus the right technology.

Cybersecurity and Scalability

As banks deploy more AI systems, the attack surface grows. AI models themselves can be targeted through adversarial inputs or data poisoning. Cybersecurity teams at banks now need to protect not only traditional infrastructure but also the training data and API endpoints that power AI services. Scalability is the other side of this. A fraud detection model that works well on 10,000 transactions per hour needs to perform just as reliably at 10 million. Many bank AI pilots fail at exactly this point: the model works in a controlled environment but buckles under production volumes. Building for scalability from day one separates successful banking AI programs from expensive experiments.

So what should banks actually do? The answer isn't to try to out-innovate fintechs on speed. Banks have structural advantages that fintechs lack: trust, regulatory licenses, existing customer bases, and balance sheets. The path forward is combining those advantages with AI capabilities that fintechs have pioneered.

Prioritize Revenue-Generating Applications

Too many bank AI projects focus on internal efficiency gains. Those matter, but they won't close the competitive gap. Banks need to invest in customer-facing AI that drives growth: personalized product recommendations and AI-powered financial planning tools, plus real-time decision engines for lending and insurance. The fintechs winning on banking AI aren't winning because they have better models. They're winning because they ship those models into products that customers actually use.

Build the Data and Governance Foundation

You can't scale AI without reliable data infrastructure and clear governance. That means investing in:

• Unified customer data platforms that pull information from across the organization
• Model risk management frameworks that satisfy regulators without slowing deployment to a crawl
• Cloud infrastructure that supports the compute requirements of modern ML workloads, including cloud computing platforms built for financial services
• Open banking data pipelines that let AI models pull from external sources to build richer customer profiles

Develop Agentic Capabilities

The shift from automation to autonomy is the most important banking AI trend to watch. Banks need to move beyond chatbots that answer questions toward AI agents that complete workflows. That means building or acquiring systems that can perceive context and make decisions within defined guardrails. PayPal's Agent Toolkit, launched in 2025, is a good example of where this is heading. The toolkit lets developers build agentic AI workflows embedded directly in the payments infrastructure, handling invoices and shipment tracking without human involvement. Goldman Sachs has taken a similar path, developing autonomous agents powered by large language models to handle trade accounting and client onboarding. That's the standard banks will be measured against.

Retrain the Workforce

The biggest banking AI blind spot isn't technology. It's people. As agentic systems take over routine tasks like document processing and transaction monitoring, the role of bank employees shifts from execution to oversight. Advisors and underwriters need to learn how to supervise AI workflows. Operations staff need to know when to intervene on edge cases. Banks that invest in retraining now will have teams ready to manage AI at scale. Those that don't will face a workforce that's either redundant or unable to work alongside the systems the bank just spent millions deploying.

The table below breaks down how fintechs and traditional banks compare across the main dimensions of AI adoption. The picture isn't entirely one-sided. Banks have real advantages in trust and regulatory standing. But they're losing on speed and willingness to let AI drive core business decisions.

The gap between fintechs and banks on AI isn't going to close by itself. Every quarter that banks spend in pilot mode is a quarter their fintech competitors use to build production systems and acquire customers. But the window isn't shut. Banks that commit to a clear AI strategy, invest in their data foundations, and move from generative AI experiments to agentic AI systems can still compete. They have assets that fintechs would trade a lot to have: regulatory licenses and customer trust backed by massive distribution networks. The digital transformation required isn't a single project with a finish line. It's a sustained commitment to rebuilding how the bank operates, from predictive analytics in lending to AI-driven cybersecurity monitoring. Each piece reinforces the next. Banks will adopt AI. They have to. The real question is whether they'll do it fast enough to matter. Based on the banking AI trends we're tracking, the answer depends on whether leadership treats AI as a technology project or as a business transformation. Those are very different things. In our experience working with financial institutions, the banks that understand the difference will outperform those that don't.