Fraud detection solution components involve creating models that have intelligence capability to properly classify transactions as either legit or fraudulent, based on transaction details such as location, transaction channel, amount, time, amount etc.
In our project https://www.researchgate.net/project/Detecting-Multi-Channel-Financial-Fraud-Using-Machine-Learning we are working on Multi Channel Financial Fraud Detection. However we would like to hear suggestions and alternative recommendations from the researchers who are working on social media analytics. What are the software packages, approaches, algorithms and tools do you recommend for Multi Channel Financial Fraud Detection? How do you relate your solution to machine learning or deep learning frameworks?
refer this links
https://www.g2.com/categories/fraud-detection
https://www.onespan.com/blog/fraud-detection
I think you should use Big data tools such as Apache Spark, which can detect anomalies in real time streaming. Spark comes with a nice ecosystem like MLlib, which provide some machine learning algorithms for detection.
Thank you
https://www.researchgate.net/project/Research-topic-map/update/5d2eedf4cfe4a7968db642b3
Dear Deger, it is necessary to discriminate between two kinds of environments. If you need real-time detection, tools such as Apache Storm, Apache Spark, Flink, and SAMOA (among others) could be an option. However, if you need to process the data under a big data environment, the answer is different because you are oriented to a batch processing. In such an environment the Hadoop environment could be an option. In this sense, it is important to highlight that the tool's right choice depends on your project's requirements.
Preprint GENERAL KNOWLEDGE MACHINE PROJECT. INTELLECT MODELING KIT AU...
I did some work on credit card fraud detection last year. One of the main issues is that the fraud is ( thankfully) less, around 2-3%. When training a ML model this unbalanced dataset becomes a serious issue. Also traditional accuracy scores might not be the best measure of effectiveness.
While anomaly detection is good and is suggested, an Attacker could ensure that the fraud characteristics does not deviate from 'normal'.
Generally a fraud has to have some deviation ( else it will not be a fraud!) and if sufficient parameters, I am thinking multi channel here, is available there is the likelihood that detection rates can be significantly increased.
I would like to here more on this....
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