A bank’s monitoring system flags an unusual transaction. A customer who usually shops locally suddenly attempts a large international payment. In past times, such activity could have gone unnoticed or led to a belated investigation. However, today’s AI-driven anti-fraud detection tools can now identify such patterns.
Fintech is facing a rising tide of digital fraud as more payments, banking services, and customer interactions go online. Traditional systems can’t keep up with speed and sophistication. AI-driven fraud detection systems can identify suspicious activity much earlier.
This article is about AI-based fraud detection in FinTech’s.
Predictive Fraud Detection: How AI Detects Fraud Before It Occurs
The following are some of the major ways through which AI-based fraud detection helps in providing predictive capabilities.
1. Identifying Risk Through Pattern Correlation
AI can analyze multiple signals together such as transaction size, location, time of activity, and device data to detect patterns.
For instance, a payment network may observe that some newly created accounts are sending funds to a certain destination within minutes of account activation. Although individual transactions may appear legitimate on their own, AI-based fraud detection can recognize this overall pattern.
2. Predicting Emerging Fraud Tactics
Since AI models learn new data, it is easy for them to adapt to new emerging fraud tactics.
Example: If a new fraud pattern is emerging from various customer accounts, such as a series of test transactions before a large transaction occurs, it is easy for the system to detect this pattern and prevent the last transaction from taking place.
3. Supporting Faster Risk Response
Predictive AI-powered fraud detection enables risk teams to concentrate on the most important signals.
Example: The risk management team at a bank could receive a signal that indicates which accounts are exhibiting early signs of account takeover attempts.
How AI Detects Payment Fraud in Real-Time
The following are some of the major ways in which AI fraud detection helps in the real-time payment process.
1. Monitoring Multiple Risk Signals at Once
The real-time AI fraud detection process looks at multiple factors at one time rather than a single factor or condition.
For Example, if a payment attempt is made from a new device, an unknown IP address, and a high-risk category of merchant, the AI fraud detection would classify the transaction as high-risk.
2. Comparing Activity with Historical Behavior
AI learns how customers use their accounts. When a transaction significantly differs from the customer’s usual pattern, the system can identify the anomaly quickly.
Example: A corporate card that normally processes weekday business expenses may suddenly attempt several late-night online purchases. AI-powered fraud detection can recognize the unusual pattern and temporarily block the transaction.
3. Enabling Immediate Risk Response
Real-time AI fraud detection allows financial institutions to respond instantly. This may entail halting a transaction, asking for step-up authentication, or notifying the risk management team.
Example: If the system identifies indicators of a potential account takeover, it may block the transaction and ask the customer to verify their identity.
AI-Powered Fraud Detection Platforms: Build vs Buy for Banks
The following are some of the significant factor banks take into account in the build vs. buy decision for an AI fraud detection system.
1. Speed of Deployment
Buying a platform allows banks to deploy AI fraud detection capabilities much faster. Pre-trained models and workflow tools are generally provided by the vendor.
Example: A bank might use a third-party AI fraud detection platform to secure their new digital payments service.
Developing an AI solution in-house could be more time-consuming as the bank will have to develop the models and train them on their data.
2. Integration with Existing Banking Systems
Integration is an important factor that has to be taken into account while implementing AI-based fraud detection. This includes integration with payment systems, transaction monitoring, and risk management.
Example: A bank operating legacy banking system might find it convenient to acquire an AI-based platform that has integration capabilities.
3. Continuous Model Improvement
Fraud is a dynamic phenomenon, and AI models need to be periodically upgraded. This is possible if the AI-based platform has access to better models based on data from multiple clients.
Example: If an AI-based platform can identify a new pattern of fraudulent transactions from multiple payment systems, it can upgrade its models.
Banks building their own systems must rely primarily on their internal data to improve models.
Conclusion
The aim is not only to prevent financial losses but also to safeguard customer trust regarding digital banking services. As fraud schemes are becoming increasingly sophisticated, AI is likely to contribute to the development of effective fraud strategies by financial institutions.
Paramita Patra is a content writer and strategist with over five years of experience in crafting articles, social media, and thought leadership content. Before content, she spent five years across BFSI and marketing agencies, giving her a blend of industry knowledge and audience-centric storytelling.
When she’s not researching market trends , you’ll find her travelling or reading a good book with strong coffee. She believes the best insights often come from stepping out, whether that’s 10,000 kilometers away or between the pages of a novel.
