How are emerging technologies shaping my industry? What is the current rate of adoption and which use cases show the most promise? Who are the key players leading investments in new solutions? This Insight aims to address these frequently raised questions for the Financial Services sector, encompassing banking, payments, and wealth management.

Our analysis examined product launches, partnerships, and use cases from the past 12 months across multiple technologies such as AI, metaverse, blockchain, IoT, quantum computing, robotics, biometrics, and genomics. The following discussion spotlights key technologies—AI, blockchain, and biometrics—that demonstrated significant potential in use cases and adoption for financial services.

Key takeaways

• Classical AI (predominantly machine learning [ML] and natural language processing [NLP]) has found its footing in backend operations, with 57% of banks having deployed it in fraud detection and 48% in risk assessment. Notable developments included Wells Fargo and Lloyds' AI integrations in trade finance and significant funding rounds like Abound's USD 1 billion raise for international AI lending platform expansion.

• GenAI is gaining momentum across customer-facing operations: Major initiatives included Wells Fargo's Fargo assistant (handling 100 million annual interactions) and NatWest's Cora+ having practical applications of GenAI in this space. Notably, Temenos' launch of “Responsible Generative AI” solutions for core banking operations aimed to address regulatory compliance and data security challenges in financial operations.

• Blockchain and AI-enhanced biometrics appear as active trends across payments: Blockchain is making substantial progress in backend and customer-facing payment applications, with Worldpay reporting a 50% reduction in payment processing time through its stablecoin system and 29% of retail investors using crypto for transactions (up from 23% in 2022). Biometric adoption in financial services has reached 60%–75%—among the highest across sectors—with solutions like Mastercard's Scam Protect combining identity verification and behavioral biometrics.

• AI-driven personalization supports retail investment strategies and back-office automation for wealth managers: Product updates such as Revolut's robo-advisory services and EarnUp's AI Advisor are examples of hyper-personalized investment products. GenAI adoption in wealth management is particularly strong in customer service (69% of firms) and investment research (50%), with BCG estimating potential time savings of up to 30% in areas like customer boarding.

• Open banking data sharing receives big regulatory boost: Relaxation of regulations surrounding open banking has enabled banks to seamlessly share data among themselves, resulting in the emergence of new payment models, such as account-to-account (A2A) and multi-currency payments. Notable developments included HSBC’s launch of Zing, a multi-currency payments app, in collaboration with Visa.

• Asset tokenization regains traction for institutional wealth management: The value of tokenized assets reached USD 15 billion as of September 2024 (excluding stablecoins). Major financial institutions, including BlackRock, Fidelity, and JPMorgan, actively implemented blockchain technology for tokenization, players such as State Street introduced tokenization platforms, and partnerships between companies like AlphaPoint, Polymesh, Archax, and Assetera aimed to enhance and facilitate asset tokenization.

• Quantum computing also showed promising use cases, particularly in portfolio optimization, simulations, and credit analysis. Key players such as Goldman Sachs, JPMorgan, and HSBC were among the major institutions frontlining research into quantum computing in financial services. However, most real-world implementations are still in experimental phases, with 60% of regulators expecting a significant impact within as late as seven years. Notably, D-Wave Systems, a maker of quantum annealing systems, is exploring applications in financial services, a hybrid approach that combines classical CPUs/GPUs with their quantum annealing hardware.

Banking and lending: GenAI gains momentum among banks after late start

Summary of current tech trends

In 2024, the banking sector saw notable developments across three key domains: Traditional AI, GenAI, and the open banking ecosystem. Traditional AI—mainly ML—continues to dominate backend operations, with over 60% of banks utilizing AI in lending and 57% for fraud detection. Notable developments included Wells Fargo and Lloyds' AI integrations in trade finance as well as significant funding rounds like Abound's USD 1 billion raise for international AI lending platform expansion.

GenAI, on the other hand, is disrupting customer-facing banking operations, with the sector poised to achieve up to 30% productivity gains through AI-driven automation according to Accenture. Industry leaders are already making significant strides: NatWest enhanced its Cora+ platform, Wells Fargo's digital assistant manages 100 million customer interactions annually, and Temenos introduced enterprise-grade Responsible Generative AI solutions for core banking functions. The transformation is gaining momentum, with 40% of banking executives expecting GenAI to handle between 6% and 20% of daily operations by year-end, while 65% consider it fundamental to their long-term innovation strategy. Yet obstacles remain, including regulatory compliance requirements, a critical talent shortage (cited by 55% of banks), and the constraints of legacy systems (noted by 38%).

However, despite the banking sector's high potential for AI-driven automation, many institutions have yet to begin their AI journey. This hesitancy is particularly striking compared with the more aggressive AI adoption rates among FinTechs and Financial Services sectors, such as insurance, suggesting that traditional banks risk falling behind more technologically agile competitors.

AI progression levels between banks, FinTechs, and insurance companies

Citi’s research anticipates AI adding USD 170 billion to global banking profits by 2028, a ~9% increase from baseline estimates of over USD 1.8 trillion to nearly USD 2 trillion. However, these gains may be partially offset as AI-enabled clients and competitors drive up transaction volumes. In the longer term, AI-powered banking assistants could fundamentally reshape customer relationships, particularly in retail banking. As consumers prioritize AI capabilities over traditional banking relationships, they may choose their AI assistant first and their bank second, potentially disrupting long-standing business models.

Notable activity across banking and lending

Tech deep dives

1. AI/ML: ML-based solutions remain preferred to optimize back-office functions

AI is fundamentally transforming the banking value chain, particularly in financing decisions, risk assessment, and fraud prevention. This transformation is well underway, with over 60% of financial institutions leveraging AI in their lending operations. Banks and FinTechs have seamlessly integrated AI across their core operations, from customer service to credit evaluation and regulatory compliance, while traditional banks have made significant strides in implementing AI solutions to streamline operations and enhance customer support.

Within AI, machine learning (ML) developments have gained the most traction, particularly in enhancing areas like risk assessment and fraud detection. In fact, over 57% of banking institutions now actively use AI for these purposes, marking the highest adoption rate in the sector. This is reflected in a recent case study by DataVisor, which demonstrated how its AI-powered platform helped a global financial institution achieve a 20% increase in fraud detection while maintaining a 94% accuracy rate.

Offerings that leverage classical NLP (which do not involve GenAI) are mostly used within back-office operations. For instance, Lloyds partnered with Cleareye.ai to streamline its trade finance options through NLP and optical character recognition (OCR). Across recent developments, however, companies are increasingly turning to GenAI for similar activities. For instance, while NLP has long been the core technology behind chatbots, recent advancements now use large language models (LLMs) to power these systems.

Although GenAI captured headlines in 2024, traditional AI continued to advance in banking, which saw notable expansions in 2024. UK lender Abound secured USD 1 billion to expand its AI lending platform internationally, while Swift prepares to launch an AI-powered financial crime detection service in early 2025. Brazilian digital bank Nubank strengthened its data analytics capabilities by acquiring Hyperplane and implementing advanced deep-learning models for risk assessment and collections.

Extent of AI use across bank business areas

The future of AI in banking appears promising beyond current applications. According to an EY survey in September 2024, 58% of banking CIOs were implementing AI initiatives, with an expected 77% by 2025. Citi projects AI will increase banking sector profits by USD 170 billion by 2028, though much of this growth may come from GenAI applications.

2. GenAI: Banking sector set for most disruption with GenAI-accelerated task automation

GenAI has emerged as a powerful force in banking, with major financial institutions actively developing and implementing various applications. These solutions promise to streamline operations, enhance customer interactions, and significantly reduce costs. A recent Accenture study highlighted the US banking sector's exceptional potential for GenAI adoption, suggesting productivity improvements of up to 30% through automation and process augmentation.

Share of work time by industry with potential of GenAI disruption

GenAI is transforming banking operations across customer service, personalization, and operational efficiency. Leading banks are upgrading their traditional chatbots to GenAI-powered solutions, as demonstrated by NatWest's Cora+ and Wells Fargo's Fargo assistant, which now handles 100 million customer interactions annually.

A significant advancement in enterprise banking technology came with Temenos' launch of “Responsible Generative AI” solutions for core banking operations. This implementation addresses key challenges in AI adoption, particularly regarding regulatory compliance and data security in financial operations.

The banking sector's embrace of GenAI continues to accelerate. A KPMG survey revealed that 40% of banking leaders expect GenAI to handle between 6% and 20% of daily tasks by year-end, while 65% consider it crucial for their innovation strategy. This adoption is expected to increase as AI governance frameworks mature.

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This is only a snippet of our Edge Insight on the State of Tech in Financial Services. Read more about notable tech use cases, payments, the open banking system, wealth management, funding, and more in the article on our Edge Intelligence Platform here.

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