Using AI-powered transaction scoring to detect fraud
When an AI system learns what "normal" activity looks like for each user, it starts looking for anything out of the ordinary. If, for example, a customer who typically makes small local transactions suddenly begins making large international transfers, it will flag this as suspicious behavior. This allows the bank to investigate quickly and block suspicious activities, reducing fraud losses and costs.
This self-improving capability gives AI a major advantage over traditional rule-based systems. Using AI-powered transaction scoring to detect fraud more data it processes, the better it becomes at detecting anomalies and distinguishing true from false transactions. It also reduces the number of false positives, resulting in fewer manual reviews and improved productivity.
Prevent Chargebacks with Transaction Risk Scoring: Minimize Financial Losses
As digital transactions become more common, the risk of fraud is increasing. To meet this challenge, many financial institutions are deploying artificial intelligence (AI) to analyze data and identify suspicious patterns. By analyzing data points including customer behavior, device information and location, AI can spot red flags in real-time and preemptively take action.
For instance, an AI model can automatically notify employees of suspicious activity and recommend additional authentication steps for a user whose login pattern has changed suddenly, such as accessing their account from a new device or location. This enables the institution to preemptively stop fraudulent activity before it happens and to restore trust with customers.
An AI model can also assess new credit applications by comparing the applicant's features to a database of genuine and fraudulent profiles. This helps the lender evaluate new applications more rapidly and effectively, enabling them to offer faster approvals or disbursements and improve the customer experience.