The fraud landscape has always been in a state of constant flux, and traditional rules-based solutions will not be enough to tackle the advanced threats. On the other hand, the organizations need to detect the risks proactively even if they are seeing such an attack for the first time. The weakest link in the chain is always the end-user and forcefully making them install software or using old strategies that affect UX is not an option. Many vendors today address these problems in an isolated manner which in turn generates blind spots through which attackers can bypass anti-fraud solutions. Set against such a backdrop is buguroo which offers the most comprehensive solutionfor real-time threat intelligence to prevent the fraud. "buguroo's holistic view includes behavioral biometrics, malware detection capabilities, and RBA which helps to achieve the real fraud hunting by leveraging deep learning,” says Pablo de la Riva, CEO and Founder of buguroo.
“Behind each session, there is a user,” says Riva. buguroo's online fraud solution, bugFraud, leverages deep learning and user cognitive analytics to detect and prevent emerging threats to digital banking users. bugFraud profiles each session to identify indicators that provide warnings on user impersonation (ATO) or user manipulation (RAT, malware, phishing). Cognitive analytics is leveraged to create a digital fingerprint of the user which includes how the user types, moves the mouse, commonly used devices, and so on. The solution then evaluates device risk, geolocation data, user’s patterns, and fraud intelligence data to detect risk to the user.
buguroo's holistic view includes behavioral biometrics, malware detection capabilities, and RBA which helps to achieve the real fraud hunting by leveraging deep learning
“bugFraud, thanks to its holistic view and the use of deep learning focuses on the fraudsters' goal, whether they use RAT, phishing scam, or whatever,” adds Riva.
bugFraud leverages a proprietary pattern similarity-based mechanism that detects new malware campaigns regardless of whether it matches with a known malware signature or not. This helps them distinguish between users and not just communities because good and bad behaviors can be relative. Therefore, this approach goes beyond known signatures and blacklist to analyze suspicious activity by fraudsters. Additionally, buguroo bugFraud Defense classifies grey anomalies to avoid false positives and false negatives. This provides extended visibility to prevent account takeovers and other attacks effectively. “Our hybrid engine combines real-time anomaly detection with machine learning models to protect your company against emerging campaigns,” adds Riva.
The solution’s advanced fraud detection capabilities leverage an agentless approach to make sure that user’s processes are smooth and frictionless. This completely transparent system secures the user’s banking transactions without alarming users with unnecessary alerts.
In a real-life scenario, a Spanish top 10 financial group wanted to detect infected devices that were targeted by malware attacks. They also did not want to install any additional software or impact their UX. buguroo implemented bugFraud Defense in the transactional pages of the bank. It helped identify all content manipulations and provide real-time alerts. Thereby, the bank increased the detection of customers with risks and minimized the impact of the attacks in time, reducing online banking fraud.
“Targets, attacks, and techniques change, but fraudsters just move,” quips Riva. Leveraging the digital fingerprint to identify fraudsters who traverse between different companies, helping their customers prevent cyber attacks, and research all the details regarding a fraud form the key elements of the company's plans. He also envisions expanding bugFraud’s mobile capabilities to allow digital enrolment in a frictionless way, prevent New-Account-Fraud, and aid companies in their digital transformation. "Much more than just clustering bad and good, bugFraud will give the option to catch the one,” concludes Riva.