In my current role at TD Bank, I work for banking clients, where we worked on integrating BioCatch behavior biometrics, enhancing fraud detection during high-risk user sessions. We use BioCatch SDK on the front-end side. Currently, I work as a full-stack developer. On this project, we use BioCatch SDK on the front-end side to capture behavioral signals such as typing rhythm and navigation habits, and we send this data to BioCatch platform, which generates a behavioral risk score in real-time. When it comes to the back-end side, we use Java Spring Boot and Node.js. We built APIs to consume their scores and feed them into our risk engine based on a risk level, where the system triggers additional verification such as MFA, multi-factor authentication, step-up authentication. I have worked with the behavioral authentication feature in BioCatch, where the user interaction is based on how they interact. Instead of relying only on passwords such as OTPs or biometrics such as fingerprints, BioCatch authentication uses behavioral signals collected from user sessions, including movement, touch gestures, scrolling, and hesitations. We also use this authentication decision where in the back-end risk engine, it consumes the scores and makes decisions for MFA or blocking the same user coming multiple times. Terrestrial steps involve keystroke dynamics, checking typing speed, rhythm, pressure, and holding intervals. Next, we utilize pointer movement to check mouse cursor speed, acceleration, and patterns. We also analyze touch and gesture patterns on mobile devices, checking swipe speed, direction, and pressure. As the next step, we perform navigation checks, where we order the pages visited and the time spent on those pages. Lastly, we examine session levels, including session duration, idle time, and page transition timing, covering the complete login, transaction, and profile updates.
Technical Business Analyst at a financial services firm with 10,001+ employees
Real User
Dec 2, 2020
The solution is primarily used for the account opening fraud journeys in retail banking and it's for the client account side. The use case was for catching any account opening fraud as the client, a bank, was losing a large amount of money previously. They had been opening accounts and suffered from a lot of financial crime. The bank wanted to catch bad behavior from potentially shady customers. Specifically, the solution was used to catch the fraudulent behavior of all kinds.
Fraud Detection and Prevention solutions help businesses identify and mitigate fraudulent activities. They provide robust mechanisms to detect anomalies and prevent financial losses, safeguarding assets and reputation.Advanced technologies in Fraud Detection and Prevention leverage AI and machine learning to analyze vast datasets, spotting patterns indicative of fraud. These solutions utilize real-time analysis, evolving with new fraud tactics, reducing false positives, and enhancing...
In my current role at TD Bank, I work for banking clients, where we worked on integrating BioCatch behavior biometrics, enhancing fraud detection during high-risk user sessions. We use BioCatch SDK on the front-end side. Currently, I work as a full-stack developer. On this project, we use BioCatch SDK on the front-end side to capture behavioral signals such as typing rhythm and navigation habits, and we send this data to BioCatch platform, which generates a behavioral risk score in real-time. When it comes to the back-end side, we use Java Spring Boot and Node.js. We built APIs to consume their scores and feed them into our risk engine based on a risk level, where the system triggers additional verification such as MFA, multi-factor authentication, step-up authentication. I have worked with the behavioral authentication feature in BioCatch, where the user interaction is based on how they interact. Instead of relying only on passwords such as OTPs or biometrics such as fingerprints, BioCatch authentication uses behavioral signals collected from user sessions, including movement, touch gestures, scrolling, and hesitations. We also use this authentication decision where in the back-end risk engine, it consumes the scores and makes decisions for MFA or blocking the same user coming multiple times. Terrestrial steps involve keystroke dynamics, checking typing speed, rhythm, pressure, and holding intervals. Next, we utilize pointer movement to check mouse cursor speed, acceleration, and patterns. We also analyze touch and gesture patterns on mobile devices, checking swipe speed, direction, and pressure. As the next step, we perform navigation checks, where we order the pages visited and the time spent on those pages. Lastly, we examine session levels, including session duration, idle time, and page transition timing, covering the complete login, transaction, and profile updates.
The solution is primarily used for the account opening fraud journeys in retail banking and it's for the client account side. The use case was for catching any account opening fraud as the client, a bank, was losing a large amount of money previously. They had been opening accounts and suffered from a lot of financial crime. The bank wanted to catch bad behavior from potentially shady customers. Specifically, the solution was used to catch the fraudulent behavior of all kinds.