Fintechs Pioneer Agentic AI in Banking: A New Era of Autonomous Financial Services

The financial services landscape is undergoing a profound transformation driven by agentic AI, with over half of organizations already leveraging AI agents to varying degrees. However, a clear divergence in adoption strategies is emerging: traditional banks predominantly focus their investments on back-office efficiency, where 68% report significant returns, while agile, digital-native fintechs are aggressively integrating agentic capabilities directly into customer-facing experiences and product innovation.

This dual-track approach is creating competitive imbalances, as highlighted by a recent Oliver Wyman report. The report identifies three transformative “economies” poised to reshape the industry:

• The Assistance Economy, where AI agents deliver comprehensive customer experiences.
• Adaptive Customer Experiences, enabling real-time personalization of interfaces.
• Agentic Twins, representing customers with delegated authority across their financial relationships.

Market leadership will increasingly belong to institutions that can industrialize agentic AI across all three dimensions, treating them as foundational capabilities rather than merely adding new tools to existing workflows.

Key Insights into Agentic AI Adoption:

• More than 50% of financial services executives are currently deploying AI agents, with specific adoption rates including 57% for customer service, 48% for marketing, 43% for fraud management, and 40% for productivity and research.
• Despite 99% of banks prioritizing customer-facing AI, only 32% realize significant returns from these investments. In contrast, 68% find the most substantial value in back-office efficiency gains, revealing a notable misalignment between priorities and outcomes.
• Current AI models in finance demonstrate limitations, achieving a 30% task performance threshold where outputs are rated superior to human experts on only three-tenths of finance-related tasks. This necessitates ongoing human oversight for complex processes.

The AI Evolution Reaches a Crucial Turning Point

The current generation of generative AI tools is merely the beginning. The financial services industry is on the cusp of fundamentally reimagining service delivery as AI transitions from assistants that provide help to autonomous AI agents that perform tasks independently. This shift moves beyond isolated solutions, enabling systems to plan, decide, and execute complex workflows without continuous human guidance through every step.

This new frontier prioritizes autonomous action over advisory support. Imagine an AI mortgage agent that, from a single command, instantly compares conveyancers, identifies optimal loan rates based on a customer’s financial profile, coordinates with third parties, submits applications, and presents validated loan options for final user selection. This operating model thrives on dual agent relationships – agent-to-client and agent-to-agent collaboration – forging entirely new mechanisms for value creation.

The agentic era is set to transform not only customer experience but also fundamental operational practices. By leveraging AI agents, financial institutions can unlock underserved market opportunities, including niche customer segments and persistent advice gaps previously deemed inaccessible or unprofitable. Simultaneously, these capabilities raise the bar for acquiring, winning, and sustaining customer relationships. Much like how the gig economy emerged from the convergence of mobile technology and GPS, new economic models will materialize around agentic AI through its deep embedding within real customer journeys, rather than simply layering capabilities onto outdated systems.

Divergent Adoption Strategies Fuel Competitive Imbalances

Banks and fintechs are pursuing distinct AI adoption trajectories, each facing critical decisions between incrementally extending existing propositions and fundamentally rebuilding processes and offerings.

• Incumbent banks primarily concentrate on back-office improvements. Despite 99% prioritizing AI in customer-facing services, only 32% report significant returns from these efforts. A substantial 68% agree that the most value comes from efficiency gains in back-office operations, such as KYC (Know Your Customer) processes, compliance workflows, and marketing analytics.
• Fintechs are advancing at a much faster pace due to their digital-native architectures. By building technology stacks from the ground up, fintechs can embed agentic AI throughout their entire value chains—from operations and product design to scalable delivery. A neobank executive highlighted this advantage, stating that proprietary core banking platforms ensure consistent service patterns, allowing any engineer to work across the stack seamlessly.

Notable examples of this innovation include Stripe’s Payments Foundation Model, which processes approximately $1.4 trillion in payments annually to detect fraud, optimize flows, and predict disputes. Similarly, Arta Finance leverages investment, research, and product agent suites to create “portfolios-of-one” for thousands of clients, delivering highly personalized financial management.

Four Critical Enablers for Mass Adoption

While current technological advancements have laid the groundwork for agentic AI in financial services, widespread progress hinges on the convergence of four critical enabling factors:

• Infrastructure: Robust cloud computing platforms and integrated data pipelines are essential for seamless scalability and cross-workflow deployment of agentic AI solutions.
• Supportive Regulatory Environments: Transparent, ethical, and responsible AI guidance, coupled with initiatives like the FCA’s Supercharged Digital Sandbox, will foster trust and confidence in autonomous system deployment.
• Digital Identity Solutions: Secure, scalable identity verification is crucial to ensure AI agents act on behalf of verified users and interact with legitimate counterparts.
• Trust: Businesses and customers must trust in delegating tasks autonomously, supported by continuous improvements in AI accuracy and explainability.

However, achieving mainstream adoption requires more than just these foundational enablers. It demands breakthrough use cases that deliver clear customer value by addressing core pain points or fixing dysfunctional processes. Just as Uber revolutionized taxi services by streamlining ride-hailing and Waymo continues to advance autonomous taxi services, financial services need similar applications that demonstrate clear superiority over existing processes to drive mass adoption.

Three New Economies Driven by Agentic Transformation

The Oliver Wyman report identifies three transformative customer paradigm shifts driven by agentic AI, poised to redefine customer experiences, operating models, and competitive dynamics:

1. The Assistance Economy: AI agents will deliver entire customer experiences across various engagement models, fundamentally altering how banks and fintechs interact with clients.
2. Adaptive Customer Experiences: Institutions will be able to deliver interfaces that adjust in real-time to user preferences and context, demanding new, dynamic engagement strategies.
3. Agentic Twins: These digital entities will represent individuals with agency and trust, becoming integral components of new customer journeys.

Early banking applications already illustrate these shifts. Capital One’s Chat Concierge, for example, assists customers in seamlessly buying cars by profiling their preferences (size, brand, performance), suggesting suitable options, estimating prices, optimizing financing, and even scheduling dealership appointments. Participating dealerships reported up to 55% higher customer engagement through this service.

Adaptive Experiences Create “Segments of One”

Algorithmic personalization is now a baseline expectation, yet only 21% of banking customers report full satisfaction with the personalization they currently receive. Market leaders will pioneer next-generation adaptive customer experience interfaces where personalization emerges dynamically. Customer journeys will be shaped in real-time by agentic AI insights, drawing on contextual data like specific product searches or user behavior. This means front-end experiences will become increasingly dynamic and data-rich, assembling personalized offerings on-the-fly, blurring and potentially eliminating traditional distinctions between channels and products.

Agentic Twins Centralize Identity and Delegated Authority

Today, customer data remains fragmented and replicated across countless services and products. Despite the promise of assistance economies and agentic architectures, customers still manually provide data from multiple sources and manage credentials across numerous accounts. Each bank holds an incomplete and often outdated representation of its customers, leading to inefficiencies and missed opportunities.

The future involves Agentic Twins: digital entities owned by individuals that serve as single, protective keepers of aggregated personal information and digital identity. These twins will act only under explicit, predefined permissions granted by their owners. Over time, their authority will expand from routine tasks to complex decision-making, controlling access permissions, selecting products and services, and interfacing with all chosen providers. For service providers, this model enables richer, content-driven insights and faster interactions by replacing inefficient data chasing with permissioned collaboration with an individual’s agentic twin.

Eight Strategic Imperatives for Financial Services Leaders

To secure a competitive position in this transformative landscape, Oliver Wyman identifies eight critical actions for banks and fintechs:

• Delighting Customers, Delivering Value, and Scaling Implementations:
  • Be present in new chat-based channels by exposing and embedding existing products.
  • Pilot highly tailored customer journeys, measuring outcomes and rapidly iterating experiences.
  • Experiment with dynamic experiences, testing adaptive rollouts with controlled human validation and dynamically adjusting based on customer uptake and satisfaction.
• Anchoring Trust Foundations:
  • Embed digital identity by integrating Open Banking and digital identity solutions for secure, transparent data sharing and agent authentication, ensuring alignment with interoperability protocols.
• Building Core Infrastructure Foundations:
  • Evolve data foundations by establishing robust data pipelines that empower AI agents to anticipate and serve customer needs, addressing siloed and unstructured data, and leveraging partnerships and Open Finance for broader data access.
  • Build effective analytics layers by creating the capability to use customer and clickstream data to deliver timely, relevant experiences or propositions, while implementing feedback loops to maintain insight accuracy and relevance.

Source: thefinancialbrand.com