Abstract: In this blog post we will discuss how data mining and machine learning can improve fraud detection in any industry. We also categorize solutions in two main parts which have their own specific patterns for fraud detection.
Fraud detection is a topic applicable to many industries including banking and financial sectors. Fraud attempts have seen a drastic increase in recent years, making fraud detection more important than ever.
Generally, data mining (sometimes called data or knowledge discovery) is the process of analyzing data from different perspectives and summarizing it into useful information. Data mining and statistics help to anticipate and quickly detect fraud and take immediate action to minimize costs. Through the use of sophisticated data mining tools, millions of transactions can be searched to spot patterns and detect fraudulent transactions.
The machine learning and artificial intelligence solutions may be classified into two categories: ‘supervised’ and ‘unsupervised’ learning.
In supervised learning, a random sub-sample of all records is taken and manually classified as either ‘fraudulent’ or ‘non-fraudulent’. Relatively rare events such as fraud may need to be over sampled to get a big enough sample size.These manually classified records are then used to train a supervised machine learning algorithm. After building a model using this training data, the algorithm should be able to classify new records as either fraudulent or non-fraudulent.