ThreatMark vs. IBM Safer Payments

ThreatMark is at the forefront of combating fraud by utilizing behavioral intelligence, enabling financial institutions to safeguard their customers from various scams and social engineering tactics. The company proactively identifies threats to digital channels, preventing fraudulent activities before they occur, unlike traditional fraud detection methods, which often fall short. In addition to assisting financial institutions and fintech partners in addressing fraud, ThreatMark significantly minimizes false positives associated with current fraud prevention measures, reduces operational expenses, and aids global partners in customer retention and revenue growth. Conventional fraud detection methods typically rely on historical transaction data, while authentication practices primarily focus on what a user knows or possesses; however, these strategies prove inadequate against scams where legitimate customers authorize fraudulent payments using their credentials, devices, and locations. By integrating its Behavioral Intelligence solution, ThreatMark enhances an institution’s fraud prevention capabilities through the analysis of largely overlooked data regarding digital interactions and user-device behavior, allowing for the identification of transactions that occur under duress or manipulation by fraudsters, ultimately strengthening the overall security framework of financial institutions.

IBM Safer Payments enables organizations to create personalized and user-friendly decision models that facilitate rapid responses to emerging threats while improving fraud detection accuracy and speed, without the reliance on external vendors or data scientists. This innovative solution significantly accelerates the modeling optimization process by providing essential analytics and simulation tools that support continual monitoring of business performance and adjustments to changing fraud patterns. Clients benefit from remarkable detection rates and a reduction in false positives upon integrating this system into their workflows. Users have the capability to build, assess, validate, and deploy machine-learning models within days rather than months, thereby eliminating vendor dependency. The platform is capable of handling thousands of transactions per second, delivering an enterprise-level solution with an impressive 99.999% uptime and outstanding throughput. Its open architecture allows for seamless importation of detection models, model components, and proprietary insights, all while offering a comprehensive interface for the development of new models. Furthermore, it accommodates a diverse array of data science, machine learning, and artificial intelligence techniques, making it an adaptable resource for any organization seeking to bolster their payment security. The inherent flexibility of this platform ensures that businesses can proactively address potential fraud threats more efficiently than ever, ultimately leading to a more secure financial environment. As organizations continue to evolve, the ability to swiftly adapt to new security challenges becomes increasingly critical.