Welcome to the era of agentic AI, the next generation of AI that’s a step above simply automation. Agentic AI involves the creation of intelligent systems that can analyze, decide, and act autonomously. This evolution marks a significant shift from traditional AI, which often requires human oversight, toward systems that can learn from interactions and adapt to changing environments without supervision.

In this new era, the boundaries between technology and finance are blurring at an unprecedented pace. Our latest research reveals 13 groundbreaking areas where agentic AI is transforming financial services. Read on for a preview of some of the most exciting developments that are changing the game.

Rise of agentic AI: Context and trends

Globally and industrially, the financial services sector is emerging as a leader in adopting AI. Banks and FinTechs are at the forefront of agentic AI adoption due to their need for efficiency, cost reduction, and enhanced customer experiences. In 2024, 58% of banking CIOs reported deploying or planning AI initiatives—a figure expected to rise to 77% by 2025. These institutions leverage agentic AI for internal operations like risk management and fraud detection, as well as customer-facing services such as personalized financial advice.

Based on where we were and where we’re headed, it’s safe to assume that the financial services industry is at a pivotal moment. By 2027, the AI-driven finance market is projected to reach USD 130 billion. This unprecedented growth is fueled by advancements in machine learning (ML), natural language processing (NLP), and generative AI (GenAI) technologies.

Agentic AI in action: Four use cases

While there are a plethora of use cases in this intersection of agentic AI and financial services, we’re going to focus on just a handful below.

1. Fraud detection: The guardian of transactions

Fraud detection is undergoing a seismic shift thanks to agentic AI. Financial institutions lose billions annually to sophisticated fraud schemes, but autonomous AI systems are stepping in as relentless guardians. By analyzing millions of transactions in real time, these systems detect subtle anomalies—like irregular spending patterns or geographic mismatches—that humans might miss. For instance, a customer’s card being used in two countries within hours could trigger an instant fraud alert, freezing the transaction and notifying the user. Take Mastercard's technology: It analyzes transaction data across multiple cards and merchants, identifying complex fraud patterns. This results in a doubled detection rate of compromised cards, a reduction of false positives by up to 200%, and a 300% increase in speed for identifying at-risk merchants.

2. Automated trading: The speed of light

Agentic AI is rewriting the rules of speed and precision within automated trading. Traditional trading floors, once dominated by human intuition, now compete with AI bots that process terabytes of market data (news sentiment, price fluctuations, geopolitical events) in milliseconds. Hedge funds like QuantEdge deploy these systems to execute trades at mathematically optimal moments, capitalizing on microtrends invisible to the human eye. The result? Portfolios that adapt dynamically to volatility, outperforming manual strategies by margins as high as 15% annually.

3. Personalized financial advice: Tailored solutions

When it comes to personalized financial advice, agentic AI is democratizing wealth management. Gone are the days of one-size-fits-all investment plans. Today’s robo-advisors, powered by autonomous systems, analyze a user’s income, risk tolerance, and life goals to craft hyper-personalized strategies. A young professional saving for a home might receive automated recommendations for low-risk ETFs, while a retiree could get rebalancing alerts for their dividend portfolio—all without human intervention. This shift is transformative as much as it is convenient, boosting client satisfaction rates by over 60% for firms like WealthAI.

4. Regulatory Compliance: The watchdog

Often a labyrinth of paperwork and manual checks, regulatory compliance, too, is being streamlined by agentic AI. Financial institutions spend an estimated USD 200 billion yearly navigating regulations like AML and GDPR, but autonomous systems are slashing these costs. For example, AI agents at compliance-focused FinTechs like ReguTech scan millions of transactions daily, flagging potential violations, such as suspicious cross-border transfers, with 98% accuracy. This reduces false positives by 70%, freeing compliance teams to focus on high-risk cases while avoiding multi-million-dollar fines. Additionally, HSBC employs agentic AI in its anti-money laundering monitoring systems. These AI agents autonomously analyze transaction patterns across global operations, identifying anomalies that could indicate money laundering or other financial crimes. 

The future of finance?

As we stand at the threshold of a new era in financial services, agentic AI is poised to redefine the very fabric of the industry. The horizon holds promises of AI-driven investment decisions, where autonomous systems continuously refine strategies based on real-time market insights, optimizing returns while mitigating risks. Autonomous risk management will become the norm, with AI agents dynamically assessing and adjusting portfolios to navigate volatile markets with unprecedented agility.

But this is just the beginning. The full potential of agentic AI in finance is vast and multifaceted, touching every aspect of the financial ecosystem. To explore all 13 areas where agentic AI is driving change, download our comprehensive report: Agentic AI in Financial Services. Delve into the future of finance today and discover how agentic AI is shaping the industry’s tomorrow. 

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