The FinTech Guys - Payment Transaction Monitoring with AI

Payment Transaction Monitoring with AI

Previously in "Payment Fraud and AI's three lines of defense" I discussed payment fraud, transaction monitoring, machine learning, and multifactor authentication and the role of each in tackling payment fraud. In this paper, I want to expand the first line of defence transaction monitoring. Through the technique of transaction monitoring, AI systems have emerged as a robust first line of defence against fraudulent activities.

Transaction monitoring has three key facets:

1. Continuous Assessment of Transactions:

AI's strength lies in its ability to continuously assess incoming transactions in real-time, comparing them against historical data. This dynamic process allows AI to instantly flag potentially fraudulent activities, even before they escalate. For instance, imagine you receive an alert on your smartphone: your credit card was just used for a high-value purchase in a foreign country, but you're physically at home. This is where AI steps in.

2. Identifying Anomalies and Triggering Alerts:

AI algorithms excel at recognizing anomalies. In the scenario mentioned above, AI recognizes the sudden international transaction as an aberration compared to your regular spending habits. It triggers an immediate alert to notify you about the suspicious activity. This alert serves as a crucial early warning system, allowing you and your financial institution time to take action.

3. Human Intervention and Confirmation:

Once an alert is triggered, the AI system doesn't work in isolation. It typically involves a human element, such as a fraud analyst, who can review the flagged transaction. This human-AI collaboration ensures that legitimate transactions are not needlessly blocked. The analyst may contact you to confirm the transaction's legitimacy, providing an additional layer of security.

AI can give us:

Real-time Detection: AI can detect fraud as it happens, minimizing potential losses.

Reduced False Positives: AI's ability to analyze complex patterns reduces the chances of legitimate transactions being mistakenly flagged as fraud.

Scalability: AI can handle vast amounts of transaction data, making it suitable for both small and large financial institutions.

Adaptability: AI continuously learns and evolves, adapting to new fraud techniques and improving its detection capabilities over time.

On the negative side, how effective it is depends on how well the models can be trained to identify fraudulent activity. There is nothing worse than having numerous false alarms - people will abandon carts, and transactions and leave. Therefore minimising false positives is key to a successful model and implementation.

To summarise AI's role in combatting payment fraud through transaction monitoring cannot be overstated. Its ability to swiftly identify anomalies and trigger alerts empowers individuals and organizations to stay one step ahead of fraudsters, safeguarding their financial assets and enhancing overall security in an increasingly digital world. But its' implementation has to be very carefully managed if one is to avoid losing customers along the journey.