About the Author
Josh Linkner is a five-time tech entrepreneur, New York Times bestselling author, and globally recognized innovation expert. He has built five tech companies that sold for a combined value of over $200 million, and is co-founder and Managing Partner of Muditā Venture Partners, an early-stage venture capital firm. As Chairman of Platypus Labs, Josh helps organizations across industries build cultures of innovation and creative problem-solving.
In This Article
- Five macro shifts reshaping the industry, from AI-powered fraud detection to autonomous financial agents that trade, lend, and advise with minimal human intervention
- Real examples of AI at work: JPMorgan’s contract intelligence platform, Morgan Stanley’s GPT-powered wealth advisors, Mastercard’s generative AI fraud systems, and more
- Three developments to watch over the next 12-36 months
- Why human creativity and judgment still matter more than any algorithm
- A 90-day action plan for finance and fintech leaders ready to move
The financial services industry has weathered market crashes, regulatory overhauls, and the arrival of digital banking. It will weather artificial intelligence too, but the industry on the other side will be fundamentally different from the one we know today.
AI has moved well beyond back-office experiments and into the core operations of banks, asset managers, insurance companies, payment processors, and fintech startups. According to Caspian One’s 2025 AI in Financial Services report, over 70% of financial institutions are now utilizing AI at scale, up from just 30% in 2023. McKinsey’s Global AI Survey found that 58% of financial institutions directly attribute revenue growth to AI through enhanced trading performance, predictive risk management, and automation of operational processes. The global AI-in-fintech market surpassed $17 billion in 2025 and is projected to exceed $50 billion by 2030, according to Mordor Intelligence.
But the headline numbers only tell part of the story. The deeper shift is structural: AI is rewriting the rules of how money moves, how risk is assessed, how fraud is caught, and how financial advice is delivered. It is compressing decision cycles that used to take days into seconds and opening financial services to populations that traditional models couldn’t profitably reach. The winners in the finance and fintech space will be the firms that move quickly and responsibly.
Here’s what you need to know.
Five Ways AI Is Changing Finance & Fintech
1. Fraud Detection Has Become an Arms Race
Financial fraud is no longer a static problem, and AI is the reason on both sides of the equation. Criminals are using generative AI to create synthetic identities, deepfake voice clones, and hyper-targeted phishing attacks at a scale that was impossible even two years ago. Synthetic identity theft has emerged as the fastest-growing form of financial crime in 2025. At the same time, the institutions fighting back are deploying AI systems that are orders of magnitude more capable than the rule-based filters they replace.
The numbers are striking. According to Mastercard’s 2026 fraud research, embedding generative AI across its detection systems delivered up to a 300% improvement in fraud detection rates. Organizations that have invested in AI-powered fraud systems for more than five years report average savings of $4.3 million in recovered revenue, nearly double the $2.2 million reported by newer adopters. Roughly 87% of global financial institutions have implemented AI-powered fraud detection as of 2025, up from 72% in early 2024.
For fintechs and finance firms, falling behind in this arms race means absorbing losses that AI-equipped competitors are avoiding.
2. Lending and Credit Decisions Are Being Rebuilt from the Ground Up
Traditional credit scoring relies on a narrow set of data points: credit history, income, debt-to-income ratio. The result is a system that works reasonably well for people who already have credit and badly for the roughly 1.3 billion adults worldwide who lack access to formal financial services. AI is dismantling that limitation.
AI-driven credit models can incorporate alternative data sources, including transaction behavior, utility payment patterns, employment stability signals, and dozens of other indicators, to assess creditworthiness in ways that conventional models simply cannot. The World Economic Forum’s research on AI credit scoring found that responsibly deployed models show clear benefits for thin-file borrowers, meaning people with little or no traditional credit history.
The opportunity is enormous, but so is the risk. When lending data reflects historical inequalities, AI models trained on that data can learn and perpetuate discriminatory patterns. The institutions that will lead in this space are the ones building fairness and inclusion metrics directly into their model objectives, not as an afterthought for regulatory audits but as a core design principle.
3. Wealth Management Is Becoming Radically Personalized
The wealth management industry has already been reshaped once by robo-advisors. Betterment now manages over $45 billion for more than a million clients. Wealthfront surpassed $75 billion in assets under management and filed for an IPO in 2025. Robo-advisors collectively manage over $2 trillion globally.
But the next wave goes considerably further. Morgan Stanley deployed its GPT-4-powered AI Assistant across its entire wealth management division, and 98% of advisor teams now actively use it for research retrieval, client preparation, and meeting follow-up. The tool draws on more than 100,000 internal research reports to give advisors instant, conversational access to the firm’s collective intelligence. A companion tool, AI @ Morgan Stanley Debrief, transcribes client meetings, extracts action items, and drafts follow-up communications (with client consent).
The shift happening now is from static robo-advisors that rebalance portfolios on a fixed schedule to what the industry is calling “agentic wealth agents”: AI systems that proactively adjust positions based on market events, tax situations, and life changes in real time. Financial advice is becoming continuous rather than episodic.
4. Compliance and Regulatory Operations Are Being Automated
Regulatory compliance has long been one of the most expensive, labor-intensive functions in financial services. The volume of regulation financial firms must track is staggering, and the penalties for getting it wrong are severe. AI is turning what has traditionally been a cost center into something closer to a strategic capability.
JPMorgan’s COiN platform offers the clearest illustration. The system uses machine learning to analyze commercial loan agreements, extracting and categorizing data across approximately 150 different contract attributes. It can review 12,000 documents in seconds, work that previously consumed 360,000 hours of lawyer and loan officer time annually. Compliance-related errors dropped by approximately 80%.
More broadly, BCG’s research found that institutions adopting AI with specialist teams are seeing up to 60% efficiency gains and 40% cost reductions in areas like onboarding, compliance, and settlement. As regulators themselves adopt AI-informed oversight, with the EU AI Act entering full enforcement for high-risk financial AI systems in August 2026, the gap between AI-equipped compliance operations and manual ones will only widen.
5. The Shift from AI Assistance to AI Agency
Perhaps the most consequential trend in financial services AI is the move from tools that assist human workers to systems that act with a degree of autonomy. The industry is calling this the “agentic era,” and it represents a qualitative shift in what AI does inside financial institutions.
According to Wolters Kluwer, 44% of finance teams will use agentic AI in 2026, an increase of over 600% from the prior year. Goldman Sachs is developing autonomous agents powered by Anthropic’s Claude model to handle trade accounting and client onboarding. Lloyds Banking Group expects agentic AI deployments to add £100 million in value this year by automating fraud investigations and complex complaint resolution. The agentic AI market in financial services is projected to reach $45 billion by 2030.
The World Economic Forum’s 2026 Future of Jobs report acknowledged that agentic AI will automate a meaningful proportion of current white-collar tasks, particularly in legal, accounting, and administrative roles within financial services. This isn’t replacing humans wholesale. It’s redefining which tasks require human involvement and which don’t.
How AI Is Actually Being Used Today
JPMorgan’s Contract Intelligence Platform
JPMorgan Chase’s COiN system remains one of the most relevant examples of AI delivering measurable operational impact in finance. The platform uses unsupervised learning to identify and categorize repeated clauses across commercial loan agreements, processing 12,000 documents in seconds. The bank reports saving over 360,000 work hours annually. Beyond speed, the system provides gains in accuracy: compliance-related errors dropped by roughly 80%, substantially reducing both regulatory risk and the cost of remediation. JPMorgan has since expanded to over 450 AI proofs of concept across the firm, with machine learning models monitoring trading books for early warning signals.
Morgan Stanley’s AI-Powered Wealth Advisory
Morgan Stanley’s partnership with OpenAI has produced what may be the most comprehensive AI deployment in wealth management. The AI @ Morgan Stanley Assistant gives financial advisors conversational, natural-language access to more than 100,000 research reports, investment analyses, and internal documents. The companion Debrief tool, launched in 2024, transcribes advisor-client meetings, generates notes, drafts follow-up emails, and inputs action items into the firm’s CRM. By late 2025, 98% of wealth management advisor teams were actively using the system, with nearly half of all Morgan Stanley employees accessing generative AI tools. The firm also launched AskResearchGPT for its institutional securities division, synthesizing insights across 70,000 proprietary research reports annually.
Mastercard’s Generative AI Fraud Prevention & Stripe’s Radar System
Mastercard’s integration of generative AI into its fraud detection infrastructure has produced some of the most quantifiable results in the industry. The system uses a combination of generative AI and graph technology to predict compromised card numbers from partial data, doubling detection speed. In its 2026 research, Mastercard reported that 42% of issuers and 26% of acquirers have saved more than $5 million each in fraud losses over the past two years. Organizations leveraging AI for fraud triage, transaction pattern recognition, and real-time detection report that 83% have seen faster investigation and case resolution. Stripe’s Radar system processes over $1.4 trillion in annual payments, scoring every transaction using machine learning trained across millions of global businesses and reducing fraud by 38% on average.
Klarna’s AI Customer Service (and Its Course Correction)
Klarna’s AI assistant, launched globally in early 2024, offered a compelling case study in both the promise and the limits of AI in financial services. Within its first month, the system handled 2.3 million conversations, representing two-thirds of all customer service interactions and doing the equivalent work of 700 full-time agents. Resolution times dropped from 11 minutes to under two. Repeat inquiries fell by 25%. Klarna projected a $40 million profit improvement from the system in 2024. But the story didn’t end there. By 2025, CEO Sebastian Siemiatkowski acknowledged that the company had prioritized cost reduction too aggressively and began rehiring human agents to complement the AI. “Cost unfortunately seems to have been a too predominant evaluation factor,” he said. This case is evidence that AI can deliver staggering efficiency gains, but institutions that optimize purely for cost risk degrading the service quality that customers actually value.
What’s Coming Next: Three Moves to Watch (12-36 Months)
1. Autonomous Financial Agents Will Move from Labs to Production
The concept of agentic AI, systems that don’t just answer questions but independently execute multi-step financial tasks, is moving rapidly from research prototypes to production deployments. SAS reports that 2026 will mark the dawn of agentic AI in banking, with semiautonomous systems beginning to take on meaningful work across the enterprise. These aren’t chatbots with better scripts. They’re systems capable of settling routine trades, managing compliance checks, processing loan applications, and triaging fraud cases with minimal human oversight.
The implications for workforce structure are significant. The World Economic Forum’s analysis suggests that financial services will be among the first sectors to see meaningful task reallocation between humans and AI agents. But the operative word is “reallocation,” not “replacement.” The institutions deploying these systems are discovering that the humans freed from routine execution become more valuable when redirected toward judgment-intensive work: complex client relationships, novel risk assessment, and strategic decision-making that AI can inform but not perform.
2. Explainable AI Will Become a Competitive Requirement
The regulatory pressure on AI transparency in financial services is intensifying on multiple fronts. The EU AI Act classifies most financial AI applications as high-risk, requiring formal explainability, bias auditing, and human oversight. U.S. and Canadian regulators are formalizing AI governance guidelines addressing model transparency, auditability, training-data provenance, and explainability. The Consumer Financial Protection Bureau has moved from accepting generic explanations of AI-driven lending decisions to demanding behavioral specificity.
Financial leaders that invest early in explainable AI infrastructure will find themselves with a structural advantage, they’ll be able to deploy new AI applications faster because the governance scaffolding is already in place. The firms that treat explainability as an afterthought will find themselves in a cycle of remediation and delay. Dataiku’s 2026 financial services AI report identifies “closing the production value gap,” the distance between AI prototypes and governed, production-grade deployments, as the defining challenge for the industry.
3. AI-Native Fintech Will Pressure Incumbent Business Models
A new class of fintech companies is being built with AI at the architectural level, not as an add-on to existing processes, but as the foundational logic of the business. These firms have no legacy systems to integrate, no decades of technical debt, and no organizational muscle memory pulling them toward manual workflows. JPMorgan analysts have projected that Stripe alone could tap a $350 billion market by 2030 through AI-driven commerce and digital payments. Zocks, an AI-powered assistant platform for financial advisors, raised $45 million in Series B funding, with advisors reporting that the platform saves them over 10 hours per week by automating client onboarding, meeting preparation, and document processing.
The competitive pressure this creates for incumbents is real. Traditional banks and financial services firms carry enormous advantages in scale, trust, and regulatory relationships. But those advantages erode when a startup can deliver equivalent service quality at a fraction of the cost by building on AI infrastructure from day one. The next 12 to 36 months will determine which incumbents successfully bridge this gap and which find themselves defending margins against AI-native competitors.
The Human Factor: Why Creativity Still Wins
With all of this technological momentum, it’s tempting to conclude that the future of finance belongs to whoever has the best model. It doesn’t. It belongs to whoever combines the best tools with the most creative, adaptable human thinking.
In my work with leaders across industries, I’ve seen a consistent pattern: the organizations that thrive in periods of disruption aren’t necessarily the ones with the biggest technology budgets. They’re the ones that cultivate what I call a Find A Way™ mindset, an organization-wide commitment to creative problem-solving that prioritizes agility over brute force and improvisation over perfect planning.
In finance and fintech, this matters enormously. AI can score a borrower’s creditworthiness using hundreds of alternative data points in milliseconds, but it takes a human to recognize that a particular lending product could open an entirely new market segment. AI can detect fraudulent transactions with 300% greater accuracy than legacy systems, but it takes a human leader to build the organizational trust required for customers to share the data that makes those systems work. AI can process 12,000 loan agreements in seconds, but it takes a human to negotiate the relationship that wins the client’s business in the first place.
Klarna’s experience is instructive here. The company achieved remarkable AI efficiency gains, then discovered that optimizing purely for cost had degraded something harder to measure but equally important: the quality of the customer relationship. That course correction required human judgment, not better algorithms.
Start small and start now. Maybe it’s your compliance team running a 90-day pilot on AI-assisted document review for one contract type. Maybe it’s your fraud operations group benchmarking an AI detection tool against your current system on last quarter’s data. Maybe it’s a cross-functional workshop where risk managers, product leads, and technologists sit together to identify the three AI use cases most likely to move the needle in the next 12 months. The breakthroughs accumulate, but only if you start accumulating them.
A 90-Day AI Action Plan for Finance Leaders
1. Pick One High-Friction Problem and Solve It Well
Don’t attempt to “do AI” across your entire organization at once. The firms converting AI investment into operational results are the ones going deep on specific use cases rather than spreading resources thin. BCG found that institutions adopting AI with focused, specialist teams see up to 60% efficiency gains, while those taking a diffuse approach struggle to move beyond pilot stage. Choose one workflow, whether it’s fraud triage, document review, customer onboarding, or credit decisioning. Define success metrics before you start. Prove value, then scale.
2. Audit Your Data and Governance Infrastructure
AI is only as good as the data it runs on and the governance framework surrounding it. In financial services, this is particularly acute: legacy systems often have poor data integration across business lines, and regulatory requirements for model transparency are tightening rapidly. Before purchasing another AI platform, invest in getting your data foundation right. With the EU AI Act classifying most financial AI as high-risk and U.S. regulators formalizing explainability requirements, the institutions that build governance infrastructure now will deploy new applications faster later. The ones that wait will spend their time in remediation.
3. Train for Judgment, Not Just Tools
The biggest mistake financial leaders make with AI adoption is treating it as a technology initiative rather than a people initiative. Your team needs training not just on which AI tools to use, but on when to trust AI outputs, when to apply human override, and how to communicate AI-driven decisions to regulators, clients, and boards who will ask hard questions. Morgan Stanley didn’t achieve 98% advisor adoption by mandating tool usage. They achieved it by building AI into the advisor’s natural workflow and demonstrating clear value. Build a change management framework alongside your technology rollout. It will outlast any individual tool.
Metrics to Watch
As you execute, track operational efficiency improvements in the specific workflow you’ve targeted, and track AI adoption confidence among your team. If your people are using the tools but not trusting them, you have a governance and training problem. If they’re trusting them without questioning outputs, you have a different but equally serious problem. The goal is informed, confident, human-led AI integration.
The Bottom Line
The financial services industry isn’t being replaced by AI. It’s being reorganized around it. The leaders, firms, and professionals who strategically and creatively engage with this shift will define the next era of the industry. Finance sits at a unique inflection point: it is the sector where AI adoption is most advanced, the regulatory scrutiny is most intense, and the stakes of getting it wrong, whether through complacency or recklessness, are highest.
The best time to start was yesterday. The second-best time is today. Find a way.
Frequently Asked Questions
How is AI currently being used in financial services?
AI is being deployed across the full financial services value chain: real-time fraud detection and prevention, automated credit scoring using alternative data, AI-powered wealth advisory tools, regulatory compliance automation, and autonomous trade settlement. According to Caspian One’s 2025 report, over 70% of financial institutions are now utilizing AI at scale, and McKinsey found that 58% directly attribute revenue growth to their AI initiatives.
How is AI changing fraud detection in banking?
AI has transformed fraud detection from reactive, rule-based filtering to real-time, predictive pattern recognition. Mastercard’s generative AI systems have delivered up to a 300% improvement in detection rates, and roughly 87% of global financial institutions have implemented AI-powered fraud detection as of 2025. The challenge is that criminals are also using AI to create synthetic identities and deepfake attacks, making this an ongoing arms race rather than a problem to be solved once.
What are autonomous financial agents, and why do they matter?
Autonomous financial agents are AI systems that independently execute multi-step financial tasks, such as settling trades, processing loan applications, triaging fraud cases, and managing compliance checks, with minimal human oversight. Unlike chatbots or simple automation, these agents can make decisions within defined parameters. Wolters Kluwer projects that 44% of finance teams will use agentic AI in 2026, and Goldman Sachs is already developing autonomous agents for trade accounting and client onboarding.
How is AI affecting lending and credit access?
AI-driven credit models use alternative data sources beyond traditional credit scores, including transaction behavior, utility payments, and employment signals, to assess creditworthiness. This approach is expanding access to credit for the roughly 1.3 billion adults worldwide who lack access to formal financial services. The World Economic Forum found that responsibly deployed AI credit scoring models show clear benefits for thin-file borrowers. However, if training data reflects historical bias, AI models can perpetuate discrimination, making fairness auditing a critical component of responsible deployment.
What regulations are shaping AI in finance?
The EU AI Act classifies most financial AI applications as high-risk, requiring formal explainability, bias auditing, and human oversight, with full enforcement beginning August 2026. U.S. and Canadian regulators are formalizing guidelines on model transparency, auditability, and training-data provenance. The Consumer Financial Protection Bureau is demanding increasingly specific explanations for AI-driven lending decisions. Financial leaders should treat compliance infrastructure as a competitive investment, not a cost to minimize.
Where should finance and fintech leaders start with AI?
Pick one high-friction, measurable workflow and deploy an AI solution with clear success criteria. Simultaneously, audit your data infrastructure to ensure you have clean, integrated, and well-governed data across business lines. Then invest in training your team not just on tools, but on the judgment required to use AI responsibly and effectively. The firms seeing the strongest results are the ones combining focused use cases, strong data foundations, and deliberate change management.
Learn more about Josh Linkner’s keynote speaking in Finance and Fintech.
Josh Linkner speaks to financial services organizations and fintech leaders around the world about innovation, navigating disruption, and building cultures that thrive in an era of rapid change. To explore how Josh can energize your next event, schedule a call today.