Written by Don Lariviere
If you find the buzzwords in AI technology confusing, you're not alone. While AI, AI Agents, and Agentic AI are often used as synonyms, they represent distinct rungs on the ladder of digital autonomy.
- Think of Artificial Intelligence (AI) as the foundational engine, the core science of creating computational intelligence.
- An AI Agent is the specific vehicle built around that engine, designed to perceive its digital environment and take action to achieve a narrow goal, such as filtering emails or scheduling a meeting.
- Agentic AI, however, is the fully autonomous, self-driving vehicle; it's a sophisticated system that doesn't just follow programmed routes but can dynamically reason, plan, and execute complex, multi-step tasks to achieve a high-level objective without human intervention.
For Fintech organizations, distinguishing between the engine, the vehicle, and the autonomous driver is the first critical step toward enabling fintechs to offer faster, fairer, and more personalized services. This focus on AI is fueling a market projected to reach a staggering $45 billion in total by 2027, as fintechs demonstrate how AI can be a powerful force for both innovation and profit. The AI market is expected to be $190 billion in the Finance sector by 2030; Agentic AI is, for now, a smaller but faster-growing sub-segment, projected to be $50 billion by then.
How Top Fintech Companies are Using AI
AI is embedded in the DNA of successful fintech companies, allowing them to innovate in areas where traditional banks have been slower to adapt.
1. Intelligent Lending and Credit Scoring
Fintechs are bypassing the limitations of traditional credit scoring models by using agentic AI to analyze a more comprehensive view of an applicant. This approach allows them to serve borrowers without well-established credit files – a challenge not just for the 'credit invisible' in the U.S. (where FICO scores dominate), but for people worldwide in countries with their own credit systems. Upstart and Affirm use AI models to evaluate thousands of data points – from job history and education to real-time transaction data – to provide instant credit decisions.
2. Proactive Payments and Security
For fintechs, the speed of transactions is paramount. Klarna and Stripe use AI agents to automate fraud detection and risk management at a speed and scale not possible with manual review. Their AI agents analyze user behavior and transaction patterns in milliseconds to block fraudulent activity in real-time, providing a seamless and secure experience for both the consumer and the merchant.
A prime example of this is Proxet's partner, Adyen, whose fraud prevention system, called Protect, leverages machine learning and AI to analyze transactions in real time. This system is trained on a massive global dataset of over a trillion dollars in payments, allowing it to recognize patterns and detect suspicious activity at a speed and scale that would be impossible for a human to manage. Adyen uses an "AI-first approach" to automate fraud control, significantly reducing the need for manual rules and reviews. Its technology provides a seamless and secure experience for both merchants and consumers by blocking fraudulent transactions in real-time, all while optimizing for higher conversion rates.
3. Automated Investing and Personal Finance
“Robo-advisors” are one of the best examples of AI in fintech. Betterment uses AI to automate the entire investment process, providing an efficient, low-cost way for everyday users to invest, democratizing a service once reserved for the wealthy. Similarly, financial apps like SoFi use AI agents to provide personalized financial insights and budgeting advice based on a user’s spending habits.
The Rise of the AI Agent in Fintech: A Deeper Dive
This is where the distinction between a basic AI tool and a true AI agent becomes critical. While a simple chatbot follows a script, an AI agent is a more sophisticated entity that can execute complex, multi-step processes. The real evolution, however, is the rise of Agentic AI. These are the systems that don’t just execute tasks but can reason, strategize, and take autonomous actions to achieve a high-level goal. The core value of these agentic systems lies in this advanced autonomy, freeing up humans to focus on strategic work that requires empathy and creativity.
Automated Compliance and Risk Monitoring
AI agents can act as a tireless compliance officer, continuously scanning millions of transactions against complex regulatory frameworks, which are based on global standards for anti-money laundering (AML) and Know Your Customer (KYC). They can instantly flag suspicious activity and even generate comprehensive reports, streamlining a process that is often a major bottleneck for human compliance teams.
Streamlined Underwriting and Loan Processing
In the lending sector, AI agents are revolutionizing how loans are approved. An agent can automatically gather an applicant's data, analyze their creditworthiness in minutes, and even issue an approval, dramatically speeding up the loan process from days to mere minutes and reducing the potential for human error.
These industry-wide examples highlight the immense potential of AI agents, but they also underscore the profound technical challenge of building these systems correctly.
Proxet is Powering Fintech Innovation
While the potential of AI is clear, building these complex, secure, and scalable systems from the ground up is a significant challenge for even the most well-funded fintech. This is where a strategic technology partner is essential. Proxet, with deep expertise in custom software development, data science, and AI solutions, is uniquely positioned to help these companies bring ambitions to life.
“Proxet has the expertise and capacity to lead the way on new agentic AI projects,” said Igor Moochnick, Head of Client Solutions. “Our work with finance, fintech, and now real estate clients is paving the way forward. We have done this work for our clients – we have a ton of expertise, a ton of experience, and a ton of knowledge on how to do it right.”
Proxet is building bespoke systems that act as intelligent assistants, automating tasks and providing key insights while keeping a person in the loop for final decisions.
- Intelligent Search and Discovery: For a major client, we've developed an AI-based search system that helps venture capitalists find and analyze institutional investors. This system uses natural language to sift through vast datasets and identify investors based on specific criteria and mandates. Similarly, we've created an agentic people and company search system that allows private equity funds to find specific individuals and firms within their network, moving beyond traditional filters to use a more intuitive, conversational interface.
- Automated Risk Analysis: We're utilizing agentic AI to underwrite and score risk for investments and existing portfolios. These systems can analyze a company's portfolio to identify potential risks, providing a detailed analysis that a human can then review and act upon. This speeds up the due diligence process and helps proactively manage portfolio health.
- Data Organization and Management: For another client, we are building agent-based solutions that automatically analyze email communications. These agents can extract key information from emails and seamlessly integrate it into the correct parts of the company's portfolio database, ensuring that critical data is organized and easily accessible.
AI Agents and Human Oversight: The Key to Responsible Fintech Innovation
Returning to our metaphor, handing the keys over to an autonomous driver brings incredible power, but also demands a new level of trust and safety. The autonomy inherent in agentic AI creates new risks, from data privacy issues to the potential for a compromised system to cause a massive data breach. An autonomous vehicle without guardrails is a liability.
A key part of Proxet’s approach is building those guardrails. We ensure that our agentic systems never make final decisions on their own. Instead, they function as a powerful co-pilot, providing comprehensive analysis and recommendations while a human expert remains in the driver’s seat, making decisions impacting the final route.
“Every step an agent takes is tracked, providing a full audit trail and allowing for complete transparency,” said Ihor Kroosh, Team Leader, Machine Learning. “This ensures that a human professional is always the final decision-maker, using the AI's insights to act with greater speed and accuracy.”
This hybrid model combines the speed of the autonomous engine with the critical judgment of human expertise, ensuring the vehicle not only reaches its destination quickly, but does so safely and responsibly.