Fraud (including different types of check fraud) is a growing issue with far-reaching consequences for the banking industry, in terms of financial losses and loss of credibility. According to PwC’s Global Economic Crime and Fraud Survey 2020, respondents reported losses of a whopping $42 billion over the past 24 months. What’s worse is that out of all the financial institutions surveyed, only 56% said that they investigated their worst fraud incident. On top of that, barely one-third of them reported the fraudulent behavior to their board.
There is a clear need for better fraud detection models and robust fraud management systems in banking as most transactions are now digital; the emergence of digital-only and online banking and payments systems in recent years has led to an exponential increase in the number of transactions. And fraudsters have become smarter and more savvy as well, adopting innovative fraudulent behavior to avoid revealing suspicious activity online.
These disturbing financial fraud trends are a cause for concern, and they call for better fraud prevention strategies in banks and other businesses. The traditional bank fraud detection system might not be sufficient enough to combat sophisticated fraudulent behavior. A solution? Fraud detection machine learning and other such predictive algorithms might be greatly beneficial.
We explain why using a machine learning algorithm is useful for detecting fraud attempts and how a machine learning focused management framework around fraud would work.
Traditional fraud detection techniques in banks
Most banks use rules-based systems with manual evaluation for fraud detection. Until recently, these systems were doing a decent job. But with fraudsters increasing in sophistication, the results traditional systems provide are becoming inconsistent.
Fraud patterns are changing and evolving faster than the rules-based systems can handle. This creates several issues: False positives (blocking genuine customers), and frauds going undetected because of the sheer volume of data to be processed. Fortunately, these challenges and shortcomings can be overcome with the use of machine learning in fraud management systems.
How does machine learning apply to fraud detection in banks?
Machine learning is the science of designing algorithms that automatically find improvements based on previous experiences. It analyzes huge sets of data using complex algorithms to identify patterns. This type of deep learning can help machines predict and respond to situations, even if they have not been explicitly programmed in those ways.
There are several use cases, including predictive analytics, product recommendations, market research, and more. But one of the most critical applications of machine learning is detecting fraud. Click here to learn how financial services use digital identity verification.
The idea behind using machine learning is that fraudulent transactions show certain patterns that differentiate them from genuine ones. Machine learning algorithms recognize these patterns and are able to differentiate those between fraudsters and legitimate clients. These algorithms can detect fraudulent activity much faster and with more accuracy than traditional rules-based systems because they can make use of larger sets of data.
While humans and rules-based programmed systems can ignore or unknowingly overlook pieces of information, machine learning algorithms can be trained to analyze even the most seemingly-unrelated information in order to find a pattern.
How machine learning helps fraud detection
Fraud prevention and risk management programs using machine learning start by gathering and categorizing as much previously recorded data as possible. This includes information about legitimate transactions and fraudulent transactions that is labeled as good (legitimate transactions or customers) or bad (fraudulent transactions or customers).
This data is then used to “teach” the machine learning program how to predict whether a certain client or transaction is fraudulent or not. For this fraud detection system to be successful, it’s good to have as much data as possible with fraud patterns so that it gives the algorithm a lot of examples to learn from. Once the machine learning algorithm is trained, the program becomes specific to the business, and can be considered ready to use in a bank’s fraud management framework.
The algorithm needs to be updated from time to time, of course, as it is not infallible. But it certainly offers several benefits as a fraud detection solution.
Benefits of machine learning in fraud detection
Even modern analytics tools and systems are largely dependent on humans to analyze data and detect suspicious transactions and fraudulent activity. This dependence is prone to issues like slow speed and human error. The use of machine learning can solve some of these issues. Benefits of machine learning for banks include:
- Speed - Machine learning algorithms can evaluate enormous amounts of data in a very short amount of time. They have the ability to continuously collect and analyze new data in real-time. Speed is increasingly important as the velocity and volume of eCommerce increases.
- Efficiency - Machine learning algorithms can perform repetitive tasks and detect subtle changes in patterns across large amounts of data. This is critical to detecting fraud in a much shorter amount of time than what humans can perform. Algorithms can analyze hundreds of thousands of payments per second, which is more work than several human analysts can do in the same amount of time. This reduces costs as well as time taken to analyze transactions, thus making the process more efficient.
- Scalability - As the number of transactions increases for banks, the pressure on a rules-based system and human analysis increases. This means a rise in costs and time, and a reduction in accuracy. With a machine learning algorithm, it’s just the opposite. The more data, the better. The program improves as more data comes in, enabling it to detect fraud faster and with more accuracy.
- Accuracy - Machine learning algorithms can be trained to analyze and detect patterns across seemingly insignificant data. They can identify subtle or non-intuitive patterns which would be difficult, or maybe even impossible, for humans to catch. This increases the accuracy of fraud detection, meaning that there will be fewer false positives and frauds that go undetected.
Machine learning is the future for fraud detection in banks
With banking scams resulting in more and more fraud losses to customers and banks every year, it is more important than ever to pay attention to fraud risk management and anomaly detection. The traditional rules-based fraud detection systems are not sufficient anymore. Using artificial intelligence and machine learning is faster, more efficient, and more accurate than rules-based systems, and saves a lot in human labor. Machine learning programs are now the future for those who want to remain competitive, and importantly, fraud-free.