In 2023, digital channels account for most incidents of fraud worldwide. The casualties of a cyberattack can be enormous, and direct losses (e.g., money poured into fake charities or gift card scams) can even be eclipsed by indirect losses (e.g., erosion of customer trust and wasted employee productivity). To avoid falling prey to a cyberattack, companies should consider a multifaceted fraud detection solution.
The best fraud detection solution will be twofold: It should prevent fraud before it happens, and it should also detect fraud when it occurs. Fraud prevention methods will examine data related to consumers, user transactions, and devices. They will look closely at who is sending and receiving information. Fraud detection, conversely, will examine patterns. A fraud detection solution will notice outliers in the data ― for example, payments made in an unusual location, or at an unusual frequency ― and escalate the concern to the appropriate parties.
Artificial intelligence (AI) tools can aid fraud detection through a combination of unsupervised learning and supervised learning. An unsupervised learning model will employ a self-learning system to identify hidden patterns in transactions, documents, or data. This model is a self-learning system, and it learns exclusively from unlabeled data. A supervised learning model uses a trained (“supervised”) algorithm, and it relies on some level of human intervention. Semi-supervised learning refers to a mixture of unsupervised and supervised learning.
With the help of these tools, fraud detection can be enhanced and simplified. Through image and pattern recognition, an unsupervised learning model can spot discrepancies faster and more accurately than the human eye.
A fraud detection and prevention approach might involve a combination of AI tools and manual processes, consistent auditing and monitoring, and fraud awareness training for employees.
With Virtusa’s expertise in AI development and implementation, we offer a range of tailored solutions designed to align business-specific needs and goals with a range of AI-tailored solutions.