- Resampling Imbalanced Data and Its Limits - Dec 22, 2020.
Can resampling tackle the problem of too few fraudulent transactions in credit card fraud detection?
Balancing Classes, Bootstrap sampling, Fraud Detection, Knime, Sampling, Unbalanced
- Fraud through the eyes of a machine - Nov 24, 2020.
Data structured as a network of relationships can be modeled as a graph, which can then help extract insights into the data through machine learning and rule-based approaches. While these graph representations provide a natural interface to transactional data for humans to appreciate, caution and context must be applied when leveraging machine-based interpretations of these connections.
Fraud, Fraud Detection, Graph Analytics, Machine Learning
- DIY Election Fraud Analysis Using Benford’s Law - Sep 15, 2020.
In this article, we will talk about a Do-It-Yourself approach towards election analysis and coming to a conclusion whether the elections were conducted fairly or not.
Benford's Law, Elections, Fraud Detection, India, Politics, USA
- A Comprehensive Data Repository for Fake Health News Detection - Mar 19, 2020.
We introduce the FakeHealth, a new data repository for fake health news detection. Following a preliminary analysis to demonstrate its features, we consider additional potential directions for better identifying fake news.
Bots, Fake News, Fraud Detection, Health, NLP
- AI and Machine Learning In Our Every Day Life - Feb 7, 2020.
The curiosity and buzz around the most talked-about technology -- Artificial Intelligence -- have experts and technophiles busy decoding its exciting future applications. Of course, the use of AI and machine learning is already pervasive in our daily lives, as we review many of these popular features in this article.
AI, Fraud Detection, Gmail, Machine Learning, Search, Social Media, Travel
- How Data Analytics Can Assist in Fraud Detection - Nov 11, 2019.
A primary advantage of data analytics tools is that they can handle massive quantities of information at once. These solutions typically learn what's normal within a collection of information and how to spot anomalies.
Analytics, Fraud, Fraud Detection
What is Benford’s Law and why is it important for data science? - Aug 7, 2019.
Benford’s law is a little-known gem for data analytics. Learn about how this can be used for anomaly or fraud detection in scientific or technical publications.
Anomaly Detection, Benford's Law, Fraud Detection
- Machine Learning and Deep Link Graph Analytics: A Powerful Combination - Apr 23, 2019.
We investigate how graphs can help machine learning and how they are related to deep link graph analytics for Big Data.
Fraud Detection, Graph Analytics, Graph Databases, Machine Learning, XAI
- Introduction to Fraud Detection Systems - Aug 17, 2018.
Using the Python gradient boosting library LightGBM, this article introduces fraud detection systems, with code samples included to help you get started.
Fraud Detection, Gradient Boosting, Python
- Using GRAKN.AI to Detect Patterns in Credit Fraud Data - Aug 30, 2017.
The term Horn Clause Mining, similar to Rule Based Machine Learning or Inductive Logic Programming, is used to describe the inverse of this functionality. Given a large enough knowledge base, can we infer rules that describe the data accurately?
Fraud, Fraud Detection, GRAKN.AI
- Stay ahead of cyberattacks and fraud with predictive analytics - Jun 6, 2017.
Even as cyber criminals and swindlers step up their game, companies can use predictive analytics to stay ahead. Discover the full scope of IBM SPSS predictive analytics capabilities.
Fraud analytics, Fraud Detection, IBM, Insurance, Predictive Analytics
- Cartoon: Taxes, Artificial Intelligence, and Humans - Apr 15, 2017.
In honor of Tax Day, new KDnuggets Cartoon looks at an unexpected white-collar job that may resist automation and Machine Learning.
AI, Artificial Intelligence, Cartoon, Fraud Detection, Humans, Taxes
- New e-learning course: Fraud Analytics using Descriptive, Predictive and Social Network Analytics - Jan 31, 2017.
This online course teaches how to find fraud patterns from historical data using descriptive analytics, and social network learning.
Bart Baesens, Fraud analytics, Fraud Detection, SAS, Social Media Analytics
- How to combat financial fraud by using big data? - Mar 25, 2016.
Financial fraud methods are becoming more sophisticated and the techniques to combat such attacks also need to evolve. Big data has brought with it novel fraud detection and prevention techniques such as behavioral analysis and real-time detection to give fraud fighting techniques a new perspective.
Alibaba, Banking, Big Data, Fraud, Fraud Detection, Fraud Prevention
- Big Data and Data Science for Security and Fraud Detection - Dec 11, 2015.
We review big data analytics tools and technologies that combine text mining, machine learning and network analysis for security threat prediction, detection and prevention at an early stage.
Big Data, DeZyre, Fraud Detection, Security
- Detecting In-App Purchase Fraud with Machine Learning - Nov 25, 2015.
Hacking applications allow users to make in-app purchases for free. With help from a few big games in the GROW data network we were able to build a model that classifies each purchase as real or fraud, with a very high level of accuracy.
Fraud Detection, Machine Learning, Online Games
- New Hybrid Rare-Event Sampling Technique for Fraud Detection - Apr 26, 2015.
Proposed hybrid sampling methodology may prove useful when building and validating machine learning models for applications where target event is rare, such as fraud detection.
Bootstrap sampling, Fraud Detection, Sampling
