The financial services industry has consistently led the way in embracing technological advancements, with Generative AI (GenAI) emerging as a transformative force in recent years. GenAI has contributed significantly to analysing vast datasets and enhancing customer interactions through chatbots and personalised services.
However, the emergence of Agentic AI marks a significant evolution in this landscape. Unlike GenAI, which operates within predefined parameters, Agentic AI systems possess the capability to make independent decisions, learn from real-time data, and autonomously execute complex tasks without continuous human oversight.
If applied successfully, Agentic AI could revolutionise financial services by introducing higher levels of autonomy, efficiency, and adaptability.
In this article, we cover the developments between Agentic AI in fintech and possible use cases, giving a glimpse into how financial services could look like in the near future.
What is Agentic AI?
Agentic AI refers to autonomous artificial intelligence systems capable of independently perceiving, reasoning, and acting based on real-time data. Unlike traditional AI, which follows predefined rules or requires human oversight, Agentic AI dynamically adapts to changing environments, makes complex decisions, and executes tasks without direct intervention.
These systems continuously learn from interactions, optimise their performance, and proactively solve problems in various domains.
In fintech, Agentic AI could enhance fraud prevention, risk management, trading, and customer engagement by autonomously analysing financial data, detecting anomalies, and executing decisions in real time.
It could enable self-optimising financial assistants, adaptive credit assessments, and proactive compliance monitoring, making financial services more intelligent, efficient, and inclusive.
How Agentic AI Can Transform Fintech Operations
| Potential Use Case | Without Agentic AI | With Agentic AI |
|---|---|---|
| Customer engagement | Rule-based chatbots provide scripted responses and struggle with context retention | AI-driven financial assistants adapt to user behaviour, proactively offer personalised insights, and autonomously act (e.g., optimising savings, suggesting better loan terms) |
| Fraud detection | Traditional systems use fixed rules and detect fraud after transactions occur | Real-time, self-learning AI models autonomously identify anomalies and block fraudulent transactions before they happen |
| Risk management | Credit risk assessments rely on predefined financial metrics and historical data | AI continuously integrates real-time, alternative data (e.g., transaction patterns, digital footprints) to dynamically assess and adjust creditworthiness |
| Trading | Human traders manually interpret market trends and execute trades or GenAI models conduct analysis and changes at specific times | Autonomous trading agents analyse live market signals, adjust strategies, and execute trades in seconds |
| Compliance operations | Compliance teams conduct labour-intensive KYC/AML checks, leading to delays | AI autonomously verifies identities, detects suspicious activities, and generates audit-ready compliance reports instantly |
| Financial inclusion | Traditional credit scoring models exclude individuals without a formal banking history | AI-driven alternative credit scoring uses mobile data, transaction history, and behavioural patterns to assess loan eligibility for underserved populations |
From Automation to Autonomy with the “Do It for Me” Economy
The “Do It for Me” (DIFM) economy is reshaping how consumers interact with financial services. People no longer want to just be handed tools to manage their money. They want intelligent systems that take action on their behalf. We’ve already seen this shift with robo-advisors, automated budgeting apps, and frictionless payments.
But these systems still require users to set preferences, approve transactions, or manually adjust settings. Agentic AI fits perfectly into this new landscape by stepping beyond traditional automation. Instead of simply providing insights or suggestions, it autonomously acts on behalf of the user, processing data in real-time and adapting to ever-changing financial conditions.
In fintech, this means AI systems that dynamically manage credit risk, automate trading decisions, and even preemptively block fraud, all without human intervention.
Commonwealth Bank of Australia (CBA) is making significant strides in leveraging artificial intelligence to reshape the financial services landscape, already using AI to handle up to 15,000 payment disputes daily, a volume too high for its call centres. With ambitions to explore at least 50 other AI use cases in private, the bank is embracing AI to cut costs, increase productivity, and deliver superior customer service.
CBA has invested around US$1 billion annually in technology, including AI, and continues to explore more AI use cases to further improve efficiency and customer experience.
Next, in a business angle, Stripe has developed an innovative agent toolkit designed to enhance the functionality of AI agents within the fintech space. By integrating large language models — used to power a wide range of automations — with Stripe’s financial tools, businesses gain the ability to manage finances, process payments, deliver customer support, and handle billing seamlessly and automatically.
The Regulatory Minefield as Agentic AI Picks Up Its Pace
As Agentic AI rapidly evolves in its development and application, regulators face several concerns that need to be carefully addressed to ensure its safe and ethical use within the fintech sector.
With Agentic AI systems processing vast amounts of sensitive financial data, including personal and transaction details, regulators must ensure that these systems comply with stringent data protection laws, such as GDPR or CCPA. There’s a risk that AI could inadvertently expose data through cyberattacks, algorithmic vulnerabilities, or insufficient safeguards.
Also, the autonomous nature of the AI means decision-making is often removed from human oversight. Regulators need clear guidelines on accountability, particularly in cases of erroneous or harmful AI-driven decisions, such as wrongful fraud detection or unfair credit scoring.
Ensuring AI’s transparency in decision-making processes is vital to avoid opaque outcomes that might not be easily understood or disputed by consumers.
Next, AI systems are only as good as the data they’re trained on, and if this data contains inherent biases, it can lead to discrimination, particularly in areas like credit scoring or lending. Financial regulators need to ensure that AI systems are designed to avoid amplifying existing biases and that they undergo regular audits to check for discriminatory practices.
On another point, Agentic AI is advancing far quicker than regulatory frameworks can keep up. Regulators risk being left behind if they fail to create forward-thinking policies that account for the complexities and risks of AI in financial services. There’s also a potential for jurisdictional challenges when AI systems operate across borders, raising issues around harmonising global standards.
Finally, as Agentic AI systems make increasingly significant decisions for consumers, there’s the risk that they may prioritise efficiency over human-centric concerns. Regulators will need to ensure that the development and application of Agentic AI stay grounded in ethical principles, safeguarding the well-being and interests of consumers.
What Lies Ahead?
From fraud prevention to financial inclusion, its applications are vast and impactful. However, ethical implementation and regulatory oversight remain critical to ensuring its benefits are maximised while mitigating risks.
As the fintech landscape evolves, embracing Agentic AI will not only transform how institutions operate but also empower consumers with smarter financial tools for a more inclusive future.
Source of image: Edited from Freepik
