Financial institutions often use multiple siloed systems to handle online fraud detection, one or more systems for varied data storage, another system for pattern recognition or learning, and yet another system for decision making. This is an IT and operations nightmare.
Online fraud detection must be real-time, every extra second taken to respond means degraded user experience and friction for trusted users. Unfortunately, traditional NoSQL or RDBMS are not capable of instant decision making on large amount of varied data.
Too Much Data
Fraud detection requires analyzing against rich variety of data and large amounts of data. This poses a double challenge to traditional fraud detection and prevention system, which’s based on traditional RDBMS or NoSQL systems that can’t handle such data needs.
Speed and latency are two critically important factors in the realm of anti-fraud. Comparing to Spark architecture, Ultipa Graph is at least 100 times faster, thanks to Ultipa’s hyper-parallel and lightning fast in-ram computing architecture.
Many of today’s anti-fraud solution systems are complicated and very much black-box to the system owners. Ultipa Graph offers white-box solutions with high visualization – owners can look under the hood with crystal clear visual cues (look to the ⬅️).
Ease of Use
Building a bespoke anti-fraud system is a laborious process; besides, the system can be very difficult to use or optimize. Ultipa Graph loves simplistic design and the sheer joy of empowering developers or analysts with easy to use toolchains to accelerate their time-to-value/market.
Lightning fast connected data processing and decision making.
Able to handle QPS in the range of 100,000s to millions.
Graph is the most natural way of connected and deep data analysis, thus ideal for real-time anti-fraud.