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.
Feedzai has three primary deployment steps:
1) It evaluates data sets and models
2) It evaluates data sources
3) It connects to case management systems
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. That said, Splunk has immense power in the way it handles machine data. It can collect and tag these data from a variety of sources and it can enable a wide variety of alerts on the data. Even machine learning tools are available but the logic for fraud detection may need to be customized. Feedzai specializes in fraud detection.
#codingexercise
Find the weighted mean of elements with duplicates in a contiguous sorted sequence
Solution:
1. For each element in a contiguous sequence
2. Insert the element, count of repetitions in a dictionary
3. for each key-value pair in the dictionary
sum the value of element times the count
also sum the counts
4. divide the sums for the weighted mean.
#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-
Feedzai has three primary deployment steps:
1) It evaluates data sets and models
2) It evaluates data sources
3) It connects to case management systems
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. That said, Splunk has immense power in the way it handles machine data. It can collect and tag these data from a variety of sources and it can enable a wide variety of alerts on the data. Even machine learning tools are available but the logic for fraud detection may need to be customized. Feedzai specializes in fraud detection.
#codingexercise
Find the weighted mean of elements with duplicates in a contiguous sorted sequence
Solution:
1. For each element in a contiguous sequence
2. Insert the element, count of repetitions in a dictionary
3. for each key-value pair in the dictionary
sum the value of element times the count
also sum the counts
4. divide the sums for the weighted mean.
#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-
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