This article is written by our partner, Finastra

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.

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.

Also Read

• Bank Statement Monitoring at Scale: Why 3,800 Statements a Day Can Become a Treasury Risk

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Treasury Masterminds is a community of professionals working in treasury management or those interested in learning more about various topics related to treasury management, including cash management, foreign exchange management, and payments. To register and connect with Treasury professionals, click the button below.

By Finastra, with insights from Treasury Masterminds Board Members

Centralising bank connectivity through SWIFT has become standard practice for multinational treasury teams. The idea is straightforward: bring banking communication into a single channel, streamline processes, and create better visibility over global cash.

In theory, centralisation reduces complexity.

In practice, it often shifts complexity somewhere else.

One multinational generating less than 100 billion in annual revenue receives roughly 3,800 bank statements every day, arriving from banks across multiple countries and formats. At that scale, even small inconsistencies can create significant operational pressure. A missing file, a duplicate statement, or a timing mismatch can quickly trigger investigations across dozens of accounts.

What begins as a simple operational task quietly becomes something more important. Bank statements stop being routine administration and start becoming a core element of treasury’s control framework.

When Scale Meets Reality

The organisation had centralised its bank connectivity through a SWIFT Service Bureau and integrated statement data directly into its treasury management system. On the surface, this setup looked efficient. In reality, managing statement reception across a global banking landscape proved more complicated.

Different banks deliver statements at different frequencies. Some send files only when account activity occurs. Others follow strict delivery windows. File formats vary by region and bank, and acquisitions continually expand the number of accounts that must be monitored.

For years, monitoring relied largely on manual processes and Excel tracking. It worked reasonably well, but visibility into expected versus actual statement delivery was limited. When something went wrong, teams often discovered the problem only after downstream processes started failing.

At a volume of several thousand statements per day, even a one-percent error rate can translate into dozens of investigations.

Data First, Automation Later

Before introducing structured monitoring, the organisation first addressed the underlying data environment. This step proved critical.

Inactive accounts were still sending statements. Some active accounts were missing entirely from monitoring lists. Reconciliation backlogs had grown to more than forty thousand unresolved lines. Until these issues were identified and corrected, implementing automation would have solved little.

“This article gets something right that often gets skipped in treasury transformation conversations: data quality is a prerequisite, not an afterthought.

The point about cleaning up before automating is the one that matters most here. The monitoring capabilities described are only as good as the expectations set behind them. Knowing what should arrive, from which accounts, and when, that’s the real control framework. The technology just enforces it.

One question I’d add to the list: who owns the exception? Visibility without accountability is just a prettier dashboard.”

His point highlights a broader truth about treasury transformation. Technology can automate monitoring, but it cannot define the rules that monitoring relies on. Those rules must already exist.

Building a Monitoring Framework

Once the data landscape was stabilised, the organisation implemented structured monitoring capabilities. Instead of simply receiving files, the system began defining expected behaviour and identifying deviations.

Treasury could now monitor statement reception in real time, compare incoming files against expected schedules, and quickly identify missing or incorrect statements. Dashboards provided a clear view of received, pending, and problematic files, while structured workflows allowed exceptions to be investigated and resolved systematically.

The goal was not simply automation. It was predictability.

Treasury teams could see immediately when something was wrong instead of discovering the issue hours or days later through reconciliation problems.

“Without proper analysis and remediation actions, organisations expose themselves to significant control risks. In environments where large volumes of bank statements are processed daily, small issues can quickly become bigger operational problems.

As shown in the case presented by Finastra, problems such as missing statements, inactive accounts, or reconciliation backlogs can weaken the control framework if they are not properly addressed.

Automation alone is not enough. If the underlying data and processes are not properly analysed and remediated first, automation can actually amplify existing issues rather than solve them.

Strong analysis, data cleanup, and clearly defined monitoring controls are essential to reduce risk and ensure reliable treasury operations.”

Lorena also notes that when entering an organisation facing these challenges, the priority should be restoring data integrity and strengthening monitoring processes before moving forward with broader transformation initiatives.

“It might delay part of the roadmap,” she says, “but it will be worth it.”

Why Monitoring Matters More Today

Treasury environments are becoming increasingly complex. Organisations operate across more banks, more currencies, and more legal entities than ever before. At the same time, treasury teams are often expected to manage these environments with leaner resources and tighter control requirements.

In this context, the integrity of bank statements becomes foundational.

Accurate statements support cash positioning, liquidity forecasting, hedge accounting, and investment decisions. When statement delivery becomes unreliable, confidence in these downstream processes quickly erodes.

“In my day-to-day work in a global organisation with many bank accounts, a few things immediately stand out. Data quality issues must be addressed first, otherwise automation simply amplifies underlying problems.

Another key concern is statement reliability. If the integrity of statement reception is compromised, downstream processes like cash positioning, liquidity forecasting, and hedge accounting are exposed to risk.

Manual workarounds are also a warning sign. When critical answers live in inboxes or spreadsheets, the organisation carries a real scale risk.

Finally, audit expectations continue to increase. Treasury teams must be able to demonstrate clear controls and transparent processes, which becomes difficult when monitoring and exception handling are not structured.”

A Lesson Beyond Technology

The case ultimately highlights a broader lesson for treasury teams undergoing digital transformation.

Technology can enable monitoring and provide visibility, but it cannot replace disciplined process design or clean data foundations. Sustainable treasury operations rely on clearly defined expectations, reliable data structures, and transparent exception management.

When these elements come together, automation becomes powerful. Without them, it simply accelerates existing problems.

In the end, bank statement monitoring is not a minor operational detail. At scale, it is an essential component of treasury’s control architecture.

Also Read

• AI in banking and financial services: Trends for 2026

Join our Treasury Community

Treasury Masterminds is a community of professionals working in treasury management or those interested in learning more about various topics related to treasury management, including cash management, foreign exchange management, and payments. To register and connect with Treasury professionals, click the button below.