The Federal Trade Commission reported that 2020 saw 2.2 million reports of consumer fraud in the US. Consumer losses to fraud came in at around $3.3 billion, making it more than twice as expensive as the previous year. However, these losses are not inevitable. Fraud detection using machine learning closes the gap between perpetrators of fraud and businesses that suffer its consequences. The latest advance in fraud detection, deep learning, enables consumers and businesses alike to protect their data from fraud with ever-increasing confidence.

The Alternative: Rules-Based Fraud Detection

The most common alternative to machine learning in fraud detection is a system familiar to anybody who has set up an email filter: rules hierarchies. A simple Rules-based fraud detection system uses lists of conditions that are easy for a machine of low complexity to interpret. Rules-based systems are much simpler than machine learning methods. This simplicity lends itself to a few advantages, almost all of which have to do with the ease of developing models for the rules being used. 

However, this simplicity unfortunately also has its downsides. Fraudsters frequently change their tactics, and rules-based methods don’t adapt on their own. This creates opportunities for bad actors to exploit vulnerabilities that remain until a human analyst gets around to fixing them. 

The Four Greatest Challenges 

We can narrow down the many reasons why AI is the perfect tool for fraud detection into four primary challenges:

1. Lots of Data

The sheer volume of data that is generated by a business is enormous and is only growing. For example, if we just look at the number of bank transactions processed globally, the number is over a billion per day. For any other business’s operations, from payroll to phone directory, the amount of data passing through a company or stored on its servers is in the tens of billions of bytes. Finding the fraud in the data is like finding a specific needle in a stack of needles. There is simply no way for a human being to keep up. 

2. It Happens Fast

Not only does the data pile up, but it also builds up quickly. An exponential increase in the speed at which data is created brings with it rapid change in what that data says and the kinds of insights it can give. The metrics you cared about yesterday and the metrics you care about today can look completely different. 

3. The Arms Race 

As the tools to stop fraud evolve and mature, those who commit fraud change their tools, too. An exploit patched today doesn’t prevent a threat from bad actors in the future, who constantly change their methods. There is a perpetual war between those who commit fraud and the business they target, forcing one side to develop strategies to match the other.  

4. It Doesn’t Always Look Like Fraud

One of the problems with fraud is that it doesn’t always look like fraud. A bad actor intent on committing fraud often hides in plain sight, invisible to the human eye. Moreover, with such challenges as insider threat, the wolves are all wearing sheep’s clothing, and some of the sheep are working for the wolves. 

Why Use Machine Learning in Fraud Detection?

Machine learning is different from artificial intelligence, but they are related. Machine learning is the method by which artificial intelligence networks can increase their own abilities. It is an automated process that AI uses to collect and analyze data. Using machine learning, an AI can teach itself based on loose parameters defined by human analysts. It’s the difference between looking at every line of code on a webpage for a specific word and simply using the browser’s Find function. 

Machine Learning Adapts Quickly 

Just as computers have become more powerful, so has their capacity to analyze large amounts of data extremely quickly. AI can analyze billions of transactions, account openings, and other financial events, in real-time. This is where the learning part of machine learning comes in, as the system automatically adapts its approach to data based on what that data tells it. This adaptation happens instantly and constantly to match the endless flow of information.

AI Never Takes a Sick Day

Real-time data analysis is only possible when the systems analyzing that data can keep pace with the speed of intake. With minimal data about a transaction, an AI can greatly increase the quantity and quality of the data, automatically test thousands of algorithms and parameter combinations. This constant process generates an optimized model, helping to make better decisions more quickly. 

The Newest Development in Fraud Detection: Deep Learning

The term “deep learning” comes from the use of neural networks with multiple overlapping layers that enable the system to learn on its own. These neural networks possess multiple nodes that cooperate in the decision-making process. These systems are tuned by attributing more or less weight to specific nodes. This creates a system that can process variable input. More familiar examples of deep learning and neural networks might include speech recognition and real-time translations. uses deep learning in its products to create robust, reliable, and always-on fraud detection solutions.