Senior Full Stack Java Developer at a financial services firm with 10,001+ employees
Real User
Top 20
2025-10-07T15:16:03Z
Oct 7, 2025
We experienced some stability issues including API latency, SDK initialization failures, and session ID correlation. We mitigated these by synchronous SDK loading, monitoring API performance, ensuring fallbacks for unsupported devices, and regular session validation. Load testing and error logging also help maintain reliability at scale. Currently, I do not have anything to say on present features of BioCatch because we use it frequently but have not explored it completely. As a Java developer, I work on both front-end and back-end. If something could be developed in BioCatch, I see potential in how users interact with devices, such as typing patterns. Also, integration-friendly aspects, such as the lightweight SDK for web, native, and iOS and Android SDKs, along with continuous authentication, real-time risk scoring, and multiple fraud detection models such as account takeover and bot detection, would be beneficial. It could work across web and mobile platforms while maintaining privacy and compliance.
Technical Business Analyst at a financial services firm with 10,001+ employees
Real User
2020-12-02T11:13:33Z
Dec 2, 2020
BioCatch is one of the fraud detection tools which also has machine learning capabilities and it has what is called a machine learning model feature. It is run in the background. The consequence of those machine models is it is complex to perform data functions and the activity and programming techniques. The decision-making for determining what's happening within those models is a little bit complex and not at all transparent. It's not easy for businesses to understand how the model is using the data of the bank customers in order to come to the assumption it does. All of these things are background technologies and the business may not understand what's happening in the background. The customer will never know what tools are being used to monitor the fraud at all, however, the business manager should certainly be interested in knowing how this model is working. People in banks are very particular when it comes to approving these models, as they have to be accountable to the regulators on the other side. They need to understand and explain what customer data is being consumed, why it's being consumed and if it's consumption is endangering any privacy rights. There needs to be clarity in terms of how much anonymization of the data happens before BioCatch comes in. I might have a gap in knowledge, and the solution may have been updated since I used it in December of last year.
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...
We experienced some stability issues including API latency, SDK initialization failures, and session ID correlation. We mitigated these by synchronous SDK loading, monitoring API performance, ensuring fallbacks for unsupported devices, and regular session validation. Load testing and error logging also help maintain reliability at scale. Currently, I do not have anything to say on present features of BioCatch because we use it frequently but have not explored it completely. As a Java developer, I work on both front-end and back-end. If something could be developed in BioCatch, I see potential in how users interact with devices, such as typing patterns. Also, integration-friendly aspects, such as the lightweight SDK for web, native, and iOS and Android SDKs, along with continuous authentication, real-time risk scoring, and multiple fraud detection models such as account takeover and bot detection, would be beneficial. It could work across web and mobile platforms while maintaining privacy and compliance.
BioCatch is one of the fraud detection tools which also has machine learning capabilities and it has what is called a machine learning model feature. It is run in the background. The consequence of those machine models is it is complex to perform data functions and the activity and programming techniques. The decision-making for determining what's happening within those models is a little bit complex and not at all transparent. It's not easy for businesses to understand how the model is using the data of the bank customers in order to come to the assumption it does. All of these things are background technologies and the business may not understand what's happening in the background. The customer will never know what tools are being used to monitor the fraud at all, however, the business manager should certainly be interested in knowing how this model is working. People in banks are very particular when it comes to approving these models, as they have to be accountable to the regulators on the other side. They need to understand and explain what customer data is being consumed, why it's being consumed and if it's consumption is endangering any privacy rights. There needs to be clarity in terms of how much anonymization of the data happens before BioCatch comes in. I might have a gap in knowledge, and the solution may have been updated since I used it in December of last year.