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.Traditional approaches such as those based on SAS suffered from the limitation that they have become old and difficult to maintain. Second, they are inflexible and unable to keep up with dynamic requirements.Feedzai claims to use a new technology on a new platform. They claim to have highest fraud detection rates with lowest false positives. They have true real time processing. They have true machine learning capabilities. They run on commodity hardware. They are non-intrusive and they are easily deployed.
The difference comes from the approach taken by traditional versus Feedzai techniques. The earlier models used to score transactions based on global perspectives. Feedzai uses real-time behavioral profiling as well as historical profiling that has been proven to detect 61% more fraud. It also reduced the false alarms significantly. 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.
Although the machine learning techniques are not enumerated, we will pend reviewing this for later. The takeaway is that Feedzai relies on Big Data and therefore runs on commodity hardware.
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.
#codingexercise
Given a sorted dictionary of an alien language, find the order of characters
The solution includes the following:
1) Create a graph G with the number of vertices as the number of distinct alphabets in the alien language
2) For every word pair in sequence, find the first mismatching character pair between the words and draw an edge between them in G
3) do a topological sort of the graph and print the characters encountered.
#As we read about fraud detection, I'm going to see if federated identity can help alleviate fraud detection: https://1drv.ms/w/s!Ashlm-
The difference comes from the approach taken by traditional versus Feedzai techniques. The earlier models used to score transactions based on global perspectives. Feedzai uses real-time behavioral profiling as well as historical profiling that has been proven to detect 61% more fraud. It also reduced the false alarms significantly. 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.
Although the machine learning techniques are not enumerated, we will pend reviewing this for later. The takeaway is that Feedzai relies on Big Data and therefore runs on commodity hardware.
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.
#codingexercise
Given a sorted dictionary of an alien language, find the order of characters
The solution includes the following:
1) Create a graph G with the number of vertices as the number of distinct alphabets in the alien language
2) For every word pair in sequence, find the first mismatching character pair between the words and draw an edge between them in G
3) do a topological sort of the graph and print the characters encountered.
#As we read about fraud detection, I'm going to see if federated identity can help alleviate fraud detection: https://1drv.ms/w/s!Ashlm-
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