While each bank operates differently, their connecting link is the number of users whose personal data is regularly used on the website for financial transactions. The realization that people are usually targeted during user authentication or authorization led buguroo to develop it fraud detection solution, bugFraud, to collect data over the duration of the entire netbanking session and protect the system data. “Our solution’s user-centric vision profiles fraudsters by identity-proofing each of them,” says de la Riva. “We realize the importance of individualizing each patron instead of grouping them into clusters.” bugFraud uses AI models for each registered consumer to avoid generalizing its clientele.
bugFraud tracks each user’s interaction with the customer website, mobile app, and the network used. It looks into the usual geolocation, reputation, and anonymization patterns to recognize suspicious behavior patterns when a user is being impersonated. Behavioral biometrics is also leveraged to identify whether a patron’s profile is taken over by an imposter.
Our solution’s user-centric vision profiles fraudsters by identity-proofing each of them
buguroo’s precise Deep Learning models help its solutions in adapting faster to the ever-changing fraud landscape than Machine Learning traditional models. “While static AI or machine learning based models prevent necessary adaptability, buguroo uses hybrid models where Deep Learning facilitates precise responsiveness and keeps pace with changing user behavior,” explains de la Riva. bugFraud employs Deep Learning together with its holistic view to create a digital fingerprint to defend its customers’ systems against multiple attacks, regardless of their nature. The effective malware detection approach avoids false positives and minimizes fake negatives to detect even the most sophisticated threats. bugFraud’s transparent countermeasures automatically protect its customers against attacks like RAT, MitB, webinjects, ATO, and Phishing. Alternately, it reports the attacks to third-party systems like transaction monitors and SIEMs to mitigate the risk. With the awareness that frauds often take place across various segments of society in different regions, buguroo allows its customers to create models of millions of users from across the globe. Its robust and scalable models utilize shared intelligence networks based on the profiling of each individual digital fingerprint.
Moving ahead, buguroo aims to use the digital fingerprint to identify fraudsters moving laterally between companies to help its customers prevent malicious attacks as well as identify those involved in the fraud. “We seek to know each end-user as a biometrically unique entity to offer a secure environment to our customers without affecting their user experience in any of their devices,” de la Riva says. Besides global expansion, buguroo aims to create specific solutions for cryptocurrency, e-commerce and Fintech customers.