Company name: Amdocs

Project title: Discovering Fraud cases in Mobile Financial Services (MFS) world.

Supervisor: Leon Malalel (5648), Phone: 0544-896886, Email: leon.malalel@amdocs.com

Project Category : Big Data , AnalyticS , BI , Fraud , Mobile Financial Services, Machine Learning, Predictive Modeling, Rule based system

Project Description:The goal is to auto-discover, using Machine Learning algorithms and techniques or rule based system (or both), cases of Fraud in MFS transactions and alert them in real-time.

Processing: The solution needs to scan every MFS transaction and calculate a score which is the propensity of the transaction to be legal or fraudulent, simulation data structure will be provided by the mentor. The score calculation mechanism will be built based on past cases of legal / fraudulent cases. High score means high propensity to be Fraud case. Transaction which have high score (= Fraudulent) will be alerted to a decision maker, so he can decided to take action, in real-time, while the transaction is still “alive”.

Output:1. List of all the transactions analyzed with their propensity to be fraud score. This will later

be used for further analysis and discovery of new patterns and trends, for model tuning purposes. The data should be saved in a standard data base and presented on UI

2. Alerts, in real-time, for this transactions above a given threshold. The solution should include a dedicated UI for the alerts.

Recommended background: Analytics and Machine learning, statistics

Under the scope of this work Mobile Financial Services is focusing on a service that gives banking functionality to the unbanked society, through the mobile devices