Like many industries, financial services stands at a pivotal moment in the agentic AI revolution. While digital transformation and FinTech introduced AI, ML, and blockchain technology to the industry, agentic AI is offering banks, FinTech companies, and other financial institutions the opportunity to transform operations from the ground up. 

Let’s dive in. 

How Agentic AI Will Transform Financial Services

Across the US and Europe, some banks have been in operation since the 1600s, which can make adoption of new technologies like agentic AI challenging. So why make the case?

Agentic AI is helping banks and financial services institutions move from reactive to proactive. Unlike automation systems that wait for human commands and streamline simple tasks, agentic AI can independently analyze situations, make decisions, and execute complex workflows across systems, all while maintaining human oversight and audit trails.

The transformation is already occurring with marquee brands like Wells Fargo, 173 year old bank that is leveraging Google Cloud’s Agentspace to streamline operations.  Where traditional AI might flag a suspicious transaction for human review (tools like Quantum Metric and Contentsquare are great examples), agentic AI can investigate the alert, analyze transaction patterns, document findings, and prepare regulatory reports. In the near future, agents will be able to autonomously handle the entire workflow from detection to compliance filing.

According to analysis from Deloitte, the real benefit of Agentic AI is that the solution works with existing systems, leading to immediate ROI when implemented correctly: “This approach could help banks unlock near-term productivity gains without large-scale system replacements by targeting high-impact workflows that could deliver clear, measurable benefits—especially tasks characterized by repetitiveness, complexity, large data volumes, or lower risk”

AI Agents for Financial Services: Beyond Single-Task Automation

As we’ve explored in blog posts on moving from legacy BI to agentic AI, the opportunity to drive ROI from agentic AI lies in the technology’s ability to orchestrate complex, multi-step workflows while adapting to regulatory requirements around AML (anti-money laundering), consumer protection, and operational resilience.

The Multi-Agent Advantage

Modern agentic AI systems deploy specialized agents working in coordinated "squads," each handling specific aspects of complex processes:

• RAG (Retrieval-Augmented Generation) agents retrieve information from knowledge bases and documents, identifying key owners and controllers from financial statements

• Data pipeline agents monitor and orchestrate data quality checks and perform entity resolution

• Research and analysis agents gather information from multiple sources (e.g, CRM and data sets), synthesizing findings and tracking emerging trends

• Critic or validation agents review outputs and suggest improvements

This multi-system approach enables continuous processes like Know Your Customer (KYC) maintenance. One agent might pull public-source data from Databricks, another scores risks from an internal database, and another checks in on regulatory updates – all with minimal hand-off.

Agentic AI Use Cases in Financial Services

Agentic AI isn't confined to IT, data science, RevOps, or any other single department. In financial services, Agentic AI reshaping workflows across front, middle, and back-office operations, embedding intelligent decision-making throughout the banking and FinTech ecosystem.

Front-Office Applications: Enhancing Customer Engagement

• Personalized Financial Planning and Advisory Services. Agentic AI evaluates customers' financial goals and transaction history to deliver tailored recommendations in real-time. Whether adjusting a savings plan to secure a mortgage downpayment or suggesting a new small business loan product, these systems proactively anticipate customer needs before they're even articulated.

• Customer Experience & Security Enhancement. Moving beyond reactive support, agentic AI proactively reaches out by detecting unusual account activity (potential fraud), suggesting ways to avoid fees, or identifying opportunities to optimize financial outcomes. 

• Conversational Banking Beyond Traditional Chatbots. Unlike first generation ChatBots like Bank of America’s Erika, Advanced conversational AI interfaces understand complex questions and take action across multiple systems. These agents can resolve issues like opening new accounts or disputing transactions without requiring handoffs to human representatives, delivering seamless end-to-end resolution.

Middle-Office Applications: Risk Management and Compliance

• Credit Scoring and Loan Processing Automation. Agentic AI evaluates creditworthiness using diverse data points (financial behavior, market signals, and alternative data sources) to automate approvals and funding decisions. This reduces turnaround time from days to minutes while minimizing bias and illegal redlining through consistent, data-driven evaluation.

• Enhanced Risk Assessment and Compliance Monitoring. These systems track real-time regulatory updates and transactional anomalies, automatically adapting policies and triggering alerts as new data becomes available. Compliance protocols remain perpetually current without manual intervention.

• Anti-Money Laundering (AML): A Transformative Use Case. Consider a multi-agent AML investigation system: Agent A reviews alerts to understand rule violations. Agent B analyzes current and historical transactions, including transactions with bad actors from government watch or terrorists lists. Agent C documents findings and recommends actions. A human validates the report, then tasks another agent to autonomously file suspicious activity reports with regulatory bodies.

Back-Office Applications: Operational Excellence

• Internal Reporting and Documentation Automation. Instead of manual report building, agentic AI can generate audit-ready documentation on demand, ensuring internal stakeholders and regulators have access to accurate information with minimal human effort.

• Dynamic Resource Allocation and Optimization. From IT infrastructure to staffing schedules, agentic AI adjusts resources based on usage patterns, forecasted demand, and emerging bottlenecks. 

• Data Processing and Analysis at Scale. With access to vast volumes of structured and unstructured data, agentic AI identifies patterns and generates insights for faster, informed decisions, automatically turning raw data into actionable intelligence.

Agentic AI Financial Services: The Competitive Landscape Shift

The implications of agentic AI in financial services, FinTech, and banking extend far beyond operational efficiency. Agentic AI is fundamentally disrupting the economics of banking, particularly in retail and SME markets.

The Revenue Disruption

The global payments industry generates over $2.5 trillion in annual revenue, with roughly half concentrated in two areas: net interest income on deposits and consumer card economics, which have come under close scrutiny recently because of Mastercard and Visa’s blockbuster court case. While banking will always require some productive friction in the user experience (2-factor authentication, while annoying at times, keeps us from getting robbed), agentic AI can help consumers and bankers operate more effectively while maintaining security. 

According to a McKinsey Report, “The end of inertia Agentic AI’s disruption of retail and SME banking,” here’s what the Agentic AI looks like for consumers.

• Deposit Optimization. AI agents can automatically sweep cash to highest-yield accounts. In Europe alone, if just 10-20% of consumers adopt agent-driven cash sweeps, bank net-interest margins could tighten by 30-50 basis points, potentially impacting over $100 billion in revenue.

• Dynamic Payment Routing. As open banking advances, AI agents can execute account-to-account (A2A) payments at checkout, bypassing card interchange systems entirely. In North America, where interchange fees range from 1.30% to 3.25%, agents can run micro-auctions in milliseconds, selecting the least-cost payment rail and undermining traditional card economics.

• Real-Time Card Optimization. Agents can rotate credit cards to maximize rewards, leverage balance-transfer offers, and optimize every transaction, provided they navigate credit-score implications and trust barriers.

In order to compete in the agentic AI future, financial services institutions must adapt the following:

• Card Issuers should translate rewards into machine-readable formats, expose preapproved credit via APIs, and pilot experiential benefits algorithms can detect but only customers can appreciate.

• Wallet Providers must position themselves as the operating system for agents, publishing developer toolkits with transparent consent controls.

• Card Networks are responding by running instant payment rails, tokenizing credentials, and packaging premium services like fraud coverage that agents can invoke by default.

• Merchants face micro-auctions at every checkout. Large retailers can drive agent preference through API-driven dynamic coupons; SMEs can plug into marketplace-agnostic offers through acquirer partnerships.

Implementing Agentic AI in Financial Services: Three Key Steps to Success

1. Set a Clear Strategy

• Align AI initiatives with existing people, tools, and processes

• Prioritize high-value, well-defined use cases

• Build and test with measurable metrics like FinOps KPIs

• View agentic AI as an evolution of broader AI efforts, not a disconnected initiative

Deploy agentic AI for complex tasks requiring autonomous decision-making, while using traditional AI for pattern recognition and prediction (each where it adds the most value).

2. Build Trust & Ensure Safety

• Define ethics and operational standards from the outset

• Update risk controls and build a shared use case library

• Promote AI literacy across all teams

• Automate early-stage risk assessments

• Encourage responsible innovation with clear accountability

• Monitor use cases continuously

Proactively integrate compliance considerations within AI agents' operational logic, workflows, and oversight mechanisms. This requires close collaboration between compliance teams and AI development groups during both design and deployment phases.

3. Plan for Scale

• Use frameworks to scale efficiently without adding complexity

• Make tools accessible across teams

• Start small, validate quickly, and iterate

• Leverage existing platforms like Amazon Bedrock, Salesforce Agentforce, and Google Agentspace

• Consider specialized vertical agents like Anthropic's Claude for financial services or Stripe's toolkit for secure financial transactions

Navigating the Challenges of Implementing Agentic AI in Banking and Financial Services 

The path to agentic AI adoption isn't without obstacles, especially in a highly regulated industry. 

• Regulatory Complexity: The EU AI Act classifies agentic finance tools as "high risk," requiring AI explainability, human controls, and third-party audits. In the US, CFPB rulemaking could mandate standardized agent access to customer-authorized data.

• Legacy Infrastructure: Existing systems and weak data integration protocols complicate deployment. Many banking processes need major overhauls, particularly workflows with limited automation history. Thankfully, Agentic AI platforms like Oraion work with on-premise legacy technology, as well as the cloud.

• Trust Barriers: Consumers remain wary of delegating financial decisions to agents. Agents need tokenized, zero-trust architectures with minimal permissions, automatic access revocation for anomalous behavior, and multifactor reauthentication for large transactions. Some of this is made possible by existing blockchain technology, like JP Morgan Chase’s kinexys. 

• Agent Errors: Early versions will make mistakes. Without proper training, an agetnc, like a human, might pay the wrong bill or improperly shifting funds. Clear communication, real-time alerts with override capabilities, and comprehensive audit trails are essential. And it’s important to remember that humans make similar errors every minute. 

• Fraud and AML Concerns: Rapid sweeps and multi-account orchestration can trigger suspicious-activity alerts. Agents must embed velocity caps, periodic KYC revalidation, and real-time AML monitoring.

Oraion for Financial Services 

Oraion helps the agentic AI data platform that helps financial organizations such as banks, FinTech, payments providers, and others take data across systems and turn it into immediate, actionable answers – without writing SQL or code. 

With Oraion, you can understand metrics such as revenue targets, customer acquisition costs, sales, and more. 

Oraion is GDPR, HIPAA, and SOC II Compliance, with MFA security, Custom SSO/SAML Provider, and Isolated Tenants to ensure security in highly regulated industries like banking. “It’s like having a senior analyst who never sleeps. ORaion finds what matters and surfaces it fast,” says Edouard de Montmort, Partner at ART Capital. 

Are you ready to see why financial institutions like Nubank are joining the agentic AI revolution? Schedule a demo today.