In the wake of the Covid-19 pandemic, global consumers reported $3.3 billion in fraud losses between 2020 and 2021. This represents an increase of nearly 50% from just a year earlier.
These figures certainly highlight the aggression and technical proficiency that cybercriminals and their ilk are able to leverage against consumers and merchants. Thus, fraud becomes more complex and costly, year over year.
While technology has helped create this problem, it may also provide the solution. In many cases, machine learning technology has proven very effective at halting fraudulent transactions before they occur.
What is Machine Learning?
A machine learning model for fraud detection is a technological tool that contrasts incoming verification details with historical data prior to approving a transaction. This automated, “self-learning” fraud tool uses sophisticated algorithms to swiftly analyze and respond to irregular data.
By testing incoming information to either confirm or contradict an existing algorithm, the program “learns” from each use and adjusts verification data accordingly. ML models are programmed to discern good and bad transactions and deliver their results in real time. The system gets better at doing so the more examples it is provided.
How Can Machine Learning Help Detect Credit Card Fraud?
In fraud detection, time is of the essence. Machine learning works faster and more efficiently than human counterparts are able to, and can respond with its findings almost instantaneously. Using the algorithmic technology mentioned above, each transaction is assigned a ‘fraud score’ that is compiled from several data points at once.
This score calculates a general profile compiled from the cardholder and the card being used to make a determination. It can advise the merchant to accept, reject, or flag the transaction for manual review. Decisioning typically takes less than a second, and the customer is not alerted or disturbed during the process.
Again, the more transactions that are processed, the better the information. This improves the decsioning process over time. As one might imagine, the lightning-fast action and response time ML models bring to the table can save users considerable time and money.
Advantages of Using Machine Learning to Detect Credit Card Fraud
Machine learning fraud detection offers advantages over traditional, rules-based fraud solutions. In fact, there are quite a few perks to promote about ML. It is:
- Faster: No matter how swiftly a human reads and cognates, they are rarely able to do so in under one second.
- More Accurate: Over time and use, ML models are able to digest, quantify, and assign data much more effectively than their human counterparts, free of error or bias.
- Self-Improving: The more information the model is given, the more it improves. In this way, ML models grow with your business.
- Proactive: Once calibrated to optimal performance levels, the ML model’s algorithms are capable of fully preventing a fraudulent transaction before it is processed.
Not to mention, machine learning also saves money for merchants. One computer can run more data checks in just a few seconds than an entire room full of human analysts in a single day. This is an obvious boon to any establishment seeking to save money.
Machine learning is brilliant and incredibly promising as a means of fraud detection and prevention. However, it should be noted that the system works best as one part of a greater fraud prevention strategy rather than its sole arbiter.
Disadvantages of Using Machine Learning to Detect Credit Card Fraud
When utilized efficiently, machine learning is an incredibly effective and customizable means of detecting fraud. This isn’t to say that the technology is infallible, however.
Firstly, bad actors are hardly asleep at the wheel. Cybercriminals work tirelessly to come up with new ways to subvert fraud detection and prevention systems. Thus, they have found several means of mimicking typical customer behaviors that ML models specifically search for. Fraudsters are adept at spoofing fraud indicators, allowing them to slip by unnoticed.
Machines are only as good as the input they are given. Bad or manipulated data can also be fed back into the machine, causing further problems down the line.
As an example, if a case of friendly fraud is misread as a case of criminal fraud, the skewed data will increase inaccuracies across the board. Aside from this, ML models tend to be hypersensitive to incoming data, which can lead to false positives that cost merchants nearly $443 billion each year. This is ten times more than losses resulting from credit card fraud.
How Can Banks Help Merchants Take Advantage of Machine Learning?
Machine learning is best utilized in combination with a greater fraud prevention strategy, but it remains the most promising and comprehensive fraud tool to date. It can be extremely effective against criminal fraud and friendly fraud alike.
To get the most of the software, it should be paired with additional fraud tools that can help provide a safeguard against false positives and help the model discern between good and bad data. The faster it is implemented, the more accurate its data will become.
Banks should encourage their clients to seriously consider adding machine learning technology to their anti-fraud efforts. It’s difficult to argue against technology that has the capacity to solve so many problems at once, and so swiftly.