AI in banking and financial services: Trends for 2026
Artificial Intelligence (AI) is redefining the financial services landscape, shifting from backend automation to a catalyst for resilience and competitive differentiation. 2026 is poised to be a pivotal year for the widespread deployment of AI in financial services. It is expected to move from experimentation to enterprise-wide deployment.
Here, we will discuss how AI has been used in finance to modernize operations, identify the trends predicted to surface in 2026, and how this will reshape the industry going forward.
The current state of AI in financial services
AI in banking and finance is being implemented to transform infrastructure across payments, wealth management, and fintech. With AI’s rapid expansion in the industry, the market is projected to grow from USD 38.36 billion in 2024 to USD 190.33 billion by 2030.1
Globally, AI has become a prominent area of investment, with venture capital accelerating and early-stage startups demonstrating the ability to achieve more with less capital due to generative AI. Generative AI itself has already driven a profound transformation in the industry, but it still sparks industry curiosity as to what will happen next. From upgrading service delivery and customer satisfaction to streamlining data research and financial modelling, the benefits are becoming obvious.
Key trends: The future of finance in AI in 2026
Trend 1: Hyper-personalization in AI banking
Many organizations are increasingly leveraging machine learning in finance to streamline operations, reduce manual tasks and enhance efficiency across core processes. However, hyper-personalized banking and investment experiences are becoming a key differentiator, enabling banks to tailor products and services to individual customer needs.
In the near future, we could see AI managing even more complex queries. Through advanced algorithms that analyse spending patterns and life events, generative AI in finance may start to offer personalized options to customers before they arise. With this level of tailored decision-making and human understanding, AI in banking has the potential to coexist permanently with traditional customer service by delivering faster, consistently high-quality support at scale.
With the integration of such complex machine learning, the need for stricter governance is more vital than ever. Regarding the deployment of generative AI in banking, the enforcement of ethical AI is crucial to prevent bias, protect customer privacy, ensure accountability and maintain transparency. This includes investing in AI training for all employees to provide a baseline understanding of systems and data processing. Without this responsible approach, banks will lose their competitive edge and customer loyalty despite boasting the latest AI innovation.
Trend 2: Generative AI as a game-changer
Generative AI is transforming the financial sector by driving innovation in document generation, reporting and advisory services. It has been a game-changer across the global banking sector, particularly in content creation and information retrieval. Plus, GenAI tools support underwriting, risk modelling and loan servicing by rapidly interpreting large volumes of data and contracts.
As research by McKinsey highlights, “generative AI could add $200 billion and $340 billion in value annually across the global banking sector.” Generative AI also influences market sentiment analysis, using machine learning to interpret the emotional tone of text, such as reviews, feedback and social media posts. We have predicted that the use of generative AI in finance could deliver between $2.6 trillion and $4.4 trillion in economic benefits.
It is important that financial institutions (FIs) consider the risks associated with generative AI use in financial services, including data privacy vulnerabilities, regulatory uncertainty and explainability challenges. A particularly interesting phenomenon that has arisen is AI hallucinations, in which LLMs perceive patterns or objects that do not exist. Serious ethical complications can arise from this, the most detrimental being the spread of misinformation.
By partnering with fintechs and technology partners, like Finastra, regulators can prioritise governance frameworks, including secure guardrails, robust monitoring, human oversight, and clear accountability, to ensure the responsible and ethical adoption of AI.
Trend 3: Agentic AI in banking
The shift toward agentic AI in banking and financial services represents a significant evolution from traditional, reactive AI chatbots and rules-based robo-advisors to autonomous systems capable of making real-time decisions, executing complex workflows and continuously learning from data. These AI agents can monitor transactions, detect fraud, streamline operations and adjust actions dynamically.
Looking ahead to 2026, agentic AI use in finance is poised to deliver significant short-term gains for banks while enabling deeper operational transformation over time. By serving as an “always-on” relationship manager, agentic AI agents will negotiate personalized products in real time, balancing customer preferences with bank risk and regulatory constraints. Our research indicates that agentic AI will drive a 20% increase in operational efficiency, and banks that leverage AI earn a 15% greater share of the market.
This shift highlights the need for modern, composable core banking systems that can support autonomous decision-making at scale, laying the foundation for the next generation of AI in finance. However, banks must be cautious as agentic AI’s continuous learning demands massive data storage and strict compliance with complex regulatory and ethical requirements. As with large-scale use of generative AI in financial services, this poses significant risks if not properly governed.
Trend 4: AI-driven fraud detection and cybersecurity
In 2026, AI in finance and banking will increasingly focus on embedded tools for anti-money laundering (AML), Know Your Customer (KYC) and Know Your Business (KYB) systems. Moving from basic automation to adaptive, real-time intelligence, FIs will improve onboarding accuracy and strengthen risk management.
As Keyrus notes, two key components that will influence optimized cybersecurity and fraud detection in 2026 are quantum-enhanced detection and multimodal threat detection. The former introduces a hybrid system that fuses quantum-enhanced computing with AI to analyze vast amounts of data and identify fraud patterns that extend across multiple institutions and jurisdictions. The latter combines behavioural biometrics for authentication with document verification and deepfake detection to identify suspicious activity across a range of accounts.
These represent a shift from traditional, manual checks and static rule-based compliance, enabling faster, more accurate onboarding and proactive risk management. As these trends advance, FIs will rely on AI to enhance regulatory compliance, reduce fraud and streamline verification processes, making it a central component of modern banking and fintech operations.
Trend 5: AI use and sustainability in FIs
Another trend predicted for 2026 is the use of machine learning in finance to measure carbon footprint. AI tools have the potential to bridge the gap between banking and sustainability, creating personalised sustainable investment recommendations, automated carbon tracking for clients and greater transparency in ESG data.
According to research by Forbes, AI-augmented tools have the ability to provide greater transparency to customers by enabling real-time insights and auditing, thereby making it simpler for companies to substantiate their credentials and protect against claims of greenwashing. While greenwashing has previously eroded confidence in sustainable finance, AI offers a practical way forward. By verifying ESG data, AI enhances transparency, improves risk management and enables stakeholders to make better-informed decisions
Additionally, recent trends show a significant increase in the issuance of green bonds, which are likely to become increasingly prominent across wealth platforms in the near future. To address the ongoing threats to the integrity of GSSS bonds, AI can play a critical role. In particular, Natural Language Processing (NLP) can be used to assess potential risks and anticipated impacts, while flagging areas that require closer review. Driven by stricter sustainability regulations, AI implementation could help the financial industry move toward a greener future.
Trend 6: Open banking continues to grow
Open banking is expected to expand from BaaS platforms to orchestrated ecosystems, sharing not only account data but also savings, investments and insurance through secure APIs. This allows AI to offer personalized services, real-time insights, and smarter financial decisions. APIs enable banks to connect their services, such as payments, lending and credit scoring, to fintech partners and other applications. At the same time, embedded finance brings these services directly into everyday platforms, marking a shift toward a more integrated and seamless experience for financial institutions.
Finastra’s Core Banking Solutions are central to this transformation, offering the secure, scalable API infrastructure banks need to participate in open ecosystems, support embedded finance use cases and ultimately power the future of AI in finance and banking.
Preparing for an AI-focused 2026
As institutions prepare for 2026, the use of AI in banking and other financial services has become the strategic backbone of future-ready transformation. Leading organisations are adopting phased AI implementation and are strengthening human-AI collaboration. The opportunities ahead are significant, ranging from hyper-personalised banking and agent-based automation to AI in financial forecasting and intelligent fraud detection. However, institutions must also balance these with risks such as explainability gaps, regulatory complexity, cybersecurity threats and the potential for AI-generated errors.
To succeed, FIs must responsibly embrace the future of AI in finance and build ecosystem partnerships that accelerate innovation.
Finastra Essence, our next-generation, cloud-first core banking solution, delivers a modern, secure, compliant, and composable foundation for sustainable AI adoption. With a modular architecture designed for 24/7 operations, powerful built-in analytics and AI-driven insights, Finastra Essence enables hyper-personalized customer journeys and superior operational efficiency. Partner with Finastra to accelerate transformation and unlock long-term, sustainable growth through intelligent, data-driven banking.
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