Within the financial industry, detecting fraud is a major priority, or certainly ought to be. Not only does fraud present the risk of a monetary loss, but there is also a tremendous brand and reputational risk. However, it can be a challenge for companies to understand and stay ahead of ever-evolving fraud risks, especially when it takes quite a bit of effort to navigate and understand the data analytics technologies that could assist in the process. Despite those daunting roadblocks, the return on investment for effective fraud detection solutions can be transformative for a business. Implementing fraud analytics solutions, or outsourcing to the right consulting company, to yield faster, more effective fraud detection and prevention, can result in immediate savings.
Fraud analytics spans many technologies and uses. Primarily, those technologies can be grouped into business intelligence and data science. The latter is the more nuanced and sophisticated, and where some of the newest and most beneficial fraud analytics solutions lie. One of the most exciting examples of new fraud analytics for financial companies would be artificial intelligence (AI) and within that field, machine learning.
AI can be defined as the computer implementation of human thought processes in a computerized and efficient fashion, and machine learning is a subset of AI that relates to the science of algorithms. More specifically, it’s a set of numerous algorithmic techniques that can be used to extract complex relationships in data which a human would not be able to find.
There are also a few different types of machine learning:
- Supervised learning focuses on making inferences from labeled historical data with known outcomes, for example, past transactions that were found to be fraudulent or genuine.
- Unsupervised learning is used to make inferences from datasets consisting of instances without labeled responses.
- Semi-supervised learning is a hybrid approach that can make use of both labeled and unlabeled data.
Machine learning’s potential uses continue to grow with each passing day, and it is only getting better at identifying possible cases of fraud across many different fields. It is being used to fight money laundering, precisely distinguish between legitimate and fraudulent transactions on the buyer and seller sides, effectively identify irregular credit card behavior patterns, simulate the growing open banking environment, and more.
Another exciting new development within artificial intelligence is the concept of neural networks, which are a new generation of algorithms based on the way people think. Significant potential lies in the ability of neural networks to learn relationships from modeled data, and, as Forbes notes, implementing this type of solution to curb cyber crime, for example, will reduce the economic losses drastically.
As you can see, the variety and effectiveness of fraud analytics for financial companies only continues to grow. Machine learning will increasingly enable companies to detect and prevent fraud. While the battle between new means of fraud and the methods of detecting and preventing fraud will continue to rage, it’s no argument that reacting after the fact is too late and will yield negative impacts on banks’ bottom lines, not only from the monetary cost but also from customer experience and perception.