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Use AI and ML for Real-time Fraud Detection: Part-2

There are two types of machine learning approaches that are commonly used in anti-fraud systems, unsupervised and supervised machine learning. They can be used independently or combined to build more sophisticated anomaly detection algorithms. Supervised learning involves training an algorithm using labeled historical data. In this case, existing datasets already have target variables marked, and the goal of training is to make the system predict these variables in future data. Unsupervised learning models process unlabelled data and classify it into different clusters detecting hidden relations between variables in data items.

To get more information on types of Internet Fraud and how to prevent them please read Part 1.

Real-time Fraud Detection with IoT AI and Machine Learning

Types of Machine Learning Data

IoT unsupervised learning data
IoT Supervised learning data

Algorithms Used for Fraud Detection

  • Logistic Regression: Logistic Regression can become more sophisticated when applied to fraud detection due to the number of variables and size of the data sets. In this technique, the authentic transactions are compared with the fraudulent ones to create an algorithm. This model (algorithm) will predict whether a new transaction is fraudulent or not.
  • Decision Tree: Decision Tree algorithms can be used for classification or regression predictive modeling problems. They are essentially a set of rules which are trained using examples of fraud that clients are facing. This gives a probability score of fraud based on earlier scenarios.
  • Random Forest: Random Forest (or an ensemble of Decision Trees) is an algorithm that builds decision trees to classify the data objects. The model selects a variable that enables the best splitting of records and repeats the splitting process multiple times. Besides their simplicity and speed, random forests can be used with different types of data, including credit card numbers, dates, IP addresses, postal codes, etc. They are considered precise predictors that can work even with datasets that have missing records.
  • Support Vector Machine (SVM): A Support Vector Machine (SVM) is a supervised machine learning model that uses a non-probabilistic binary linear classifier to group records in a dataset. SVM are particularly good at working with complex multidimensional systems. It also allows for avoiding the overfitting problem that random forests may experience. It is a very common method in credit card fraud detection.
  • K-Nearest Neighbors: It is an algorithm that classifies records by similarity based on the distance in multidimensional space. The record is assigned to the class of the nearest neighbors. The record of each cluster is voting for each new record using the distance parameter. This method is insensitive to missing and noisy data, which allows for configuring larger datasets with less preparation. It is also considered highly accurate and does not require much engineering effort to modify models.
  • Neural networks and Deep Neural Networks: Neural networks and especially deep neural networks are powerful at finding non-linear and very complex relations in large datasets. This works both for transactional data and for text and image analysis, which may be used in insurance cases. They usually provide high accuracy, which makes neural networks a necessary part of a modern fraud detection group.

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