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Fraud Detection

Flag unusual transactions and behaviors that might indicate fraud attempts. Analyzing historical transaction patterns per customer/customer segment, spot anomalies. Examples of fraud include credit card fraud, loan fraud fraud, on-boarding customers fraud

Business value: Reduces Fraud Loss, Gain Customer Trust, and Improve Customer Experience.

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ROI Examples
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1. Get Inspired

Understand the Use-case under 5 minutes

2. Know More

Get to know more Business and Technical details about the use-case (15-30 minutes)

Deeper Intro

More detailed introduction covering business and technical aspects

Business Focused

Case studies, Organizational Aspects, Return on Investment examples

Tech focused

More details on the technical aspects of the use-case

3. Do

Technical resources that will help you implement the use-case (notebooks, tutorials..)

Data Sets

Data Sets you can use to build Demos, POCs, or test Algorithms

Synthetic Financial Datasets For Fraud Detection

Synthetic datasets generated by the PaySim mobile money simulator

Credit Card Fraud Detection

The dataset contains transactions made by credit cards in September 2013 by European cardholders.
This dataset presents transactions that occurred in two days, where we have 492 frauds out of 284,807 transactions

IEEE-CIS Fraud Detection

The data comes from Vesta’s real-world e-commerce transactions and contains a wide range of features from device type to product features (with “isFraud” as an attribute, for fraudlent transactions)


Off-the-Shelf Products using AI for Fraud Detection

Got a Question or a Resource to share with the Community? Please do!

  • Everything about this is SIMPLY BRILLIANT!

    1- The attention to details is stunning
    2- The whole thing is very well-organized, whether in terms order or sections
    3- The objectives are stated clearly
    4- The objectives of each section is linked to the main objective so everyone would be able to follow/keep up
    5- Building a knowledge in a top-down or a flipped pyramid manner where the business knowledge base is built (with references) so that anyone (whether business or technical specialists) who will contribute in the project would know where their role lies in the big picture
    6- The technical side is driven from a very well-documented business background, and that allows the technicians to be creative in finding solutions and serving the article’s objectives based on their deep understanding of the business side
    7- Providing a variety of references and cases shows the urge and eagerness to help people out, and the captions under each section is very descriptive and to the point
    8- The (data) sources are very rich, and being flexible with them is a huge plus
    9- The interface and UX is very appealing and user friendly
    10- The Discussion section is a way to go in trying to reach the best practice

    Can’t wait for this thing to be officially launched and go as far as it gets.

  • 10%- Fraud Losses