Globally, the Artificial Intelligence (AI) in banking market was estimated to be USD 34.58 billion in 2025.1 This is expected to increase to USD 45.59 billion in 2026 and then grow to approximately USD 451.50 billion by 2035, expanding at a CAGR of 29.30% from 2026 to 2035. As the market continues to grow, the way banks are leveraging this technology is also undergoing sea change. AI in banking is moving from experiments and pilot projects to core operations with a strong focus on value and Return on Investment (ROI). It is being used to transform legacy processes, leverage data, and drive banking efficiency, security, and personalization.
92 percent of global banks are using AI in at least one core function while 86 percent of European banks are leveraging it for improving compliance and fraud detection.2 And 70 percent of North American banks are using gen AI powered chatbots to handle Tier 1 customer queries. The most valuable AI use cases are those that sit at the intersection of risk, revenue, and relationships. They don’t operate in silos, instead, they connect transactions to behavior, pricing to risk, and customer activity to long-term profitability. As we move into 2026, here are some key use cases for AI in banking:
- Fraud Detection and Prevention
Traditional rule-based systems cannot keep pace with the modern, constantly evolving fraud landscape. They miss new or evolving fraud patterns, and they generate high false positives, increasing manual reviews and customer friction. AI models can analyze thousands of behavioral signals in real time to assess risk instantly. These include transaction patterns, device fingerprints, geolocation, login behavior, velocity anomalies, and historical usage trends. High-risk transactions can be paused, verified, or blocked before settlement. Banks are already leveraging AI for fraud detection.3 HSBC’s AI system processes 1.35 billion monthly transactions, boosting detection via behavioral analysis. DBS Bank cuts false positives by 90% and improved accuracy by 60% with AI monitoring 1.8 million hourly transactions.
- Risk Management and Credit Scoring
Traditional models relied mainly on static credit histories, but AI models can evaluate and correlate multiple factors like transaction behavior and cash-flow patterns, income stability, and employment signals. They can assess spending volatility and lifestyle indicators as well as historical repayment behavior across products. This enables faster loan approvals, accurate risk assessment, expansion of credit to underserved customers, and dynamic adjustment of pricing and limits over time. In fact, machine learning models have helped mid-size banks approve loans 34 percent more accurately.4
- Personalized Customer Services
Modern customers want 24/7 customer service availability, and interactions that solve their problems and address their needs. Generative AI powers hyper-personalized experiences, like tailored loan offers and dynamic rewards based on behavior. Chatbots and virtual assistants boost engagement across channels, supporting native languages for global reach. AI-powered virtual agents and chatbots can handle account servicing and transaction queries, card and limit management, loan eligibility checks, personalized insights into spending and balances, and even extend contextual product recommendations. And unlike scripted bots, NLP-driven systems understand intent, learn from interactions, and adapt responses over time, delivering an improved experience and boosting customer satisfaction and loyalty.
The long-term implications for the bank are significant. These models can engage customers continuously, not just at transactional moments. AI-driven insights can trigger contextual offers, bundles, or pricing adjustments and digital engagement. The emergence of agentic AI will further transform the customer service function. AI agents can autonomously analyze the customer’s behavior patterns and relationship history to proactively recommend products, or services or offer financial advice. To capitalize on this, banks need pricing and billing systems that can respond in real time to adjust fees, incentives, and offers dynamically.
- AI-Powered Customer Onboarding
Customer onboarding is one of the most important processes in the banking lifecycle. Delays lead to drop-offs and weak controls lead to regulatory lapses. AI-powered KYC and KYB now ensure automated document scanning and authenticity checks and biometric and facial recognition for remote onboarding. They enable real-time sanctions, watchlist, and PEP screening and can carry out dynamic customer risk profiling. AI-powered processes can significantly reduce onboarding time without hampering compliance efforts. Faster onboarding means faster activation of accounts and services while lower abandonment improves acquisition ROI. And risk aware onboarding ensures that pricing, limits, and services align with customer risk from day one.
- Corporate Banking, Treasury, and Revenue Intelligence
AI adoption in corporate and transaction banking is accelerating rapidly. Banks are using AI for a wide range of functions such as liquidity forecasting and cash-flow prediction, portfolio and balance-sheet optimization, trade finance and counterparty risk scoring, intelligent fee and earnings analysis, and relationship-level profitability modelling. AI models can help banks understand which clients, products, and behaviors truly drive value and deliver a more contextual and effective experience.
The future of AI in banking is not isolated use cases. It is end-to-end orchestration and deep integration across systems and processes. It is important to note that AI is a powerful driver of efficiency, accuracy, automation, and productivity, and it will help eliminate manual repetition and fragmented decision-making. But it cannot replace human judgement and oversight. In fact, keeping a human-in-the-loop is a critical governance guardrail as AI models can be susceptible to bias and hallucinations. Leveraged strategically, AI in banking can go beyond being a mere technology initiative to become a robust business transformation engine.
