We continue reading "Modern data Fraud Prevention at Big Data Scale". Feedzai enables companies to move from broad segment based scoring of transactions to individual oriented scoring with machine learning based techniques. Feedzai claims to use a new technology on a new platform. They claim to have highest fraud detection rates with lowest false positives. Feedzai uses real-time behavioral profiling as well as historical profiling that has been proven to detect 61% more fraud. They have true real time processing. They say they have true machine learning capabilities. Feedzai relies on Big Data and therefore runs on commodity hardware. The historical data goes as far back as three years. In addition, Feedzai processes realtime data in 25 milli seconds against vast amounts of data at 99th percentile.  This enables fraud to be detected almost as early as when it is committed. Moreover the monitoring and alerting components of Feedzai can work independently from its inflight transactions. Therefore for those purposes, Feedzai can work independently and in a non-intrusive manner. It is also deployed quickly as an appliance that can be trained and activated.
Feedzai involves an in-memory analytics engine which can compute multi-dimensional fraud scores based on 250,000 KPI in the same second every second.  This provides a new industry standard for real-time fraud protection. It also comes in useful to augment machine learning capabilities. For example, the individual transactions being scored are also used train the models.  Moreover the scoring and flagging are intuitive which helps comprehension and reduces manual intervention.
The ability to process 100,000 events per second enables them to detect risk and fraud patterns that would have otherwise gone undetected. The actions taken by Feedzai are configurable from merely reporting to blocking. As such, it is a non-intrusive system. Approximately, ninety percent of all Feedzai customers connect the solution to message queuing but it comes with a variety of connectors that can take the feed from other sources. As opposed to a rules based engine where the deployment and refinement of rules may take time, Feedzai can install its analytic engine and connectors within a day.
If we compare Splunk with its connectors, machine learning abilities and use of Big Data, commodity machines and clusters for analytics on machine data in a time series database, it seems the primary difference is the customer orientation of data and analytics,

#codingexercise
We discussed an exercise yesterday involving topological sort. Let's revisit it:

topological sorting DFS ( V, E)

For each vertex v in V

       V.color=white

       V.d = nil

  Time = 0

 For each vertex v in V:

       If v.color == white:

              DFS-Visit (V, E)

     

 DFS-VISIT (V,E, u)

  time = time + 1

   u.d = time

   u.color = gray

   foreach  vertex v adjacent to u

        If v.color == white

           DFS-VISIT(V,E,v)

         Else

               If v.d  <= u.d < u.f <= v.f  throw back edge exception.

 u.color = black

time = time + 1

 u.f = time

#As we read about fraud detection, I'm going to see if delegated identity can help alleviate fraud detection: https://1drv.ms/w/s!Ashlm-Nw-wnWsE3BHcaes2F7Lsoi