Fraud prevention, crucial parts of AI and machine learning
Fraudsters are getting smarter in utilizing technology and opportunities to launch their actions. The good news is that fraud prevention is very likely to be done by utilizing technology. The terms artificial intelligence (AI) and machine learning have become buzzwords in the fraud world in recent years. The two terms of the technology are predicted to be powerful weapons against fraud.
Although sometimes both terms can be used interchangeably, they are different. AI refers to software that is capable of doing tasks like humans, while machine learning is an AI component, in which its algorithm is able to study large amounts of data and diverse patterns and solve problems based on that data.
For example, an AI system might be able to monitor a consumer’s buying pattern and send a warning if it saw anything unusual found in the transaction. However, with machine learning, the system can recognize changes in habits that are wider and bring data from other sources to build their understanding of what fraudulent transactions are without being supervised by humans. This is why AI is often combined with machine learning.
What is the crucial part of a fraud detection AI model?
An AI model needs to go through a training period to form detection capabilities to match the expectation. In this training, a number of data collections (datasets) are used. Sundeep Tengur, Senior Business Solutions Manager at SAS Institute, in the article Artificial Intelligence for fraud detection: beyond the hype, mentioned that every successful AI model must have experienced many failures in detecting fraud and the main reason is data.
Like teaching a child to kick a ball, we show the child how to kick the ball several times so that he can learn it from some of these movements. Likewise, with machine learning, we input particular data that we hope the machine learns. The larger the volume and variety of datasets, the more patterns of fraud that can be recognized by the machine.
Besides data, human involvement in inputting datasets is critical, especially for supervised machine learning types which are commonly used by insurance underwriting. This type requires the involvement of humans in inputting labeled data.
Machine learning can only recognize fraud patterns input by a human. However, the machine does not think “is the input data correct?” Because the machine only learns what is input by humans. It does not matter the machine has a great algorithm if the input data is wrong, the results are not optimal, even fatal; false positive, bias, fraud not detected, and so on. Therefore, human accuracy in inputting the data cannot be underrated.
AI and machine learning are breakthroughs in the world of fraud detection. Knowing the factors that affect the accuracy of AI and machine learning’s works helps engineers and analysts develop fraud detection devices that are able to address the constant challenges.