The rapid evolution of AI technologies is revolutionizing the way financial institutions detect fraud in real time. Insights from a FinTech expert and a CEO reveal transformative strategies in this field. The article kicks off with leveraging machine learning for real-time detection and concludes with implementing AI for real-time fraud analysis, offering ‌seven expert insights. Explore how industry leaders are harnessing AI to stay ahead of fraudsters.
- Leverage Machine Learning for Real-Time Detection
- Analyze Metadata for Fraud Detection
- Flag Anomalies in Transaction Behaviors
- Monitor Patterns for Unusual Activity
- Use AI to Combat Account Takeovers
- Transform Detection with Pseudonymized Data
- Investigate and Address Suspicious Activity
Leverage Machine Learning for Real-Time Detection
AI is increasingly being used to detect fraud in real-time financial transactions by leveraging machine-learning algorithms that can analyze vast amounts of transactional data at high speeds. AI detects unusual patterns, flags suspicious activities, and differentiates between legitimate and fraudulent behavior. It does this by learning from historical data and continuously updating its models to adapt to new fraud tactics.
For example, many financial institutions use AI to monitor credit card transactions in real time. If an AI system detects a transaction that doesn’t fit a user’s normal spending patterns—such as a large purchase in a different country—it can immediately flag the transaction for review or block it. Visa, for instance, uses AI-based tools to detect and prevent payment fraud. Their system reviews each transaction and assigns a risk score based on several factors, such as location, purchase size, and device used. This allows the company to catch fraudulent transactions in real time, minimizing financial losses and protecting customers.
Sergiy Fitsak
Managing Director, Fintech Expert, Softjourn
Analyze Metadata for Fraud Detection
AI’s real-time fraud detection goes beyond basic rule-based systems by analyzing the subtleties in a transaction’s metadata—time, geolocation, device fingerprints—to assess risk. It’s able to detect fraud before it even fully unfolds by identifying patterns that a human eye could never catch in time. This real-time responsiveness is what keeps the financial system a step ahead of even the most sophisticated fraud tactics.
Visa has implemented AI that continuously monitors transactions for signs of fraud, and it once intercepted a large-scale credit card fraud operation in real-time. The AI detected a cluster of transactions from various unrelated accounts, all purchasing similar items in quick succession, something a human might’ve missed in the noise. The system blocked the fraudulent activity, saving countless customers from loss.
Alari Aho
CEO and Founder, Toggl Inc
Flag Anomalies in Transaction Behaviors
AI enhances real-time fraud detection in financial transactions by analyzing patterns and flagging anomalies. For instance, a bank employs AI algorithms to monitor transaction behaviors; if a sudden surge in transactions is detected from a single account, it raises an alert for review. This method helps reduce losses and fosters customer trust by ensuring secure transactions.
Bram Louwers
Director, BrainManager
Monitor Patterns for Unusual Activity
At Tech Advisors, we’ve seen firsthand how AI is revolutionizing fraud detection in real-time financial transactions. AI works by continuously monitoring transaction patterns, analyzing behaviors, and spotting unusual activity that could indicate fraud. For example, AI can detect when a credit card is suddenly used for a large international purchase, even though the cardholder typically makes small local transactions. This quick response is crucial because it stops the fraud before it impacts your finances.
One real-world application of AI fraud detection comes from the banking sector, where it helps catch suspicious activities like synthetic-identity fraud. AI models analyze patterns across thousands of transactions and identify unusual behaviors, such as a sudden spike in loan applications that don’t align with typical customer behavior. This is something Elmo Taddeo, CEO of Parachute, has emphasized in his own experience with managed IT security services—stopping fraud in real time gives businesses peace of mind.
For businesses, the key benefit of AI is its ability to work around the clock. AI fraud detection doesn’t take breaks, meaning it can catch fraud at any hour of the day. It’s a proactive way to safeguard your financial operations, reduce the need for manual reviews, and build trust with your customers. As we often tell clients, preventing fraud is about being one step ahead, and AI provides the speed and accuracy needed to do just that.
Konrad Martin
CEO, Tech Advisors
Use AI to Combat Account Takeovers
AI is certainly making waves in the world of fraud detection, but it’s not quite the silver bullet it’s sometimes made out to be. While it excels at analyzing massive datasets and spotting subtle patterns that humans might miss, it’s still just one tool in a larger arsenal.
Think of it like this: AI is like a highly-trained bloodhound, sniffing out clues and pointing investigators in the right direction. But it still takes human expertise to interpret those clues, connect the dots, and ultimately crack the case.
For example, at JetFuel, our influencer marketing platform, we used AI to help combat account-takeover fraud. Our system analyzed various factors, such as login location, device information, and browsing behavior, to create a unique “fingerprint” for each user. If someone tried to access an account from a new device or location, the AI would flag it as potentially suspicious and trigger additional security measures, like two-factor authentication or even blocking the login attempt altogether. This real-time monitoring helped us protect our influencers and prevent fraudulent activities on the platform.
JJ Maxwell
CEO, Double Finance
Transform Detection with Pseudonymized Data
AI is transforming fraud detection by analyzing transaction data in real-time to identify suspicious patterns and flag potential fraud instantly. This proactive approach minimizes financial losses and enhances security.
For example, SWIFT, the global financial messaging network, is launching an AI-powered service in January 2025. This service will analyze pseudonymized data from billions of transactions to detect and flag suspicious activity in real-time. This initiative aims to address the growing sophistication of financial criminals and the rising cost of fraud.
Austin Rulfs
Founder, Sme Business Investor, Property & Finance Specialist, Zanda Wealth
Investigate and Address Suspicious Activity
AI is bringing a new era for financial institutions to fight against fraudulent activities in real-time transactions through the use of algorithms and machine learning. Due to its capability of embracing and analyzing big data, AI can easily detect risks and suspect cases of embezzlement within a shorter time than conventional methods.
For instance, at Jarsy Inc., we employ AI models designed for implementing real-time analysis of transactions based on factors such as their frequency, value, and user behavior patterns. It will be considered a fraud risk, for example, if a usual buyer makes small purchases and then tries to buy something like lamps in another store of the chain.
There was, for instance, a financial institution in which we executed our AI system that realized a decrease of twenty to thirty percent of fraud transactions in their organization. They were able to investigate and address the identified activities instantly, which would, in turn, reduce the impact on their revenues and effectively safeguard customer interests. This makes security not only proactive but also increases the level of trust from the customers in the capacity of the institution to protect their business transactions.
Chunyang Shen
Finance Expert, Jarsy Inc.
