As fraudulent activities become increasingly sophisticated, traditional rule-based methods, while still useful, can no longer be the silver bullet to protect customers and institutions.
This is where machine learning technology comes into play, which is revolutionizing fraud detection by enabling financial institutions (FIs) to stay one step ahead of fraudsters.
Understanding the ensemble machine learning approach
Machine learning is a subset of artificial intelligence that allows systems to learn and improve from experience. While there are multiple approaches, it’s important to understand the difference between the two primary types of machine learning relevant to financial fraud detection: unsupervised machine learning (UML) and supervised machine learning (SML).
Unsupervised machine learning involves feeding data into the system without any preexisting labels or classifications. The system then identifies patterns and structures within the data, helping institutions detect suspicious activities that may indicate potential fraud. This approach is highly valuable in identifying emerging financial fraud patterns that might not be apparent through traditional methods.
On the other hand, supervised machine learning relies on labeled data, where historical fraud cases are used to train the model. The system learns from past examples and can classify new transactions as either fraudulent or legitimate. Supervised machine learning is particularly effective when dealing with known fraud patterns and is capable of providing accurate predictions.
Selecting the appropriate machine learning approach for the problem at hand is critical for maximizing the effectiveness of fraud detection in banking. For institutions dealing with a constant influx of transaction data with no historical knowledge of fraud patterns, unsupervised machine learning can be highly advantageous. It can detect unusual patterns in real time, flagging potential fraud cases that may otherwise have gone unnoticed.
Supervised learning becomes a highly effective approach when there is a clear understanding of fraud patterns that demand a more automated solution beyond self-adaptation methods. For complete, holistic protection, a combined or ensemble approach provides complete coverage against known and unknown threats.
Evaluating the advantages and limitations of UML and SML
Understanding the benefits and drawbacks of UML and SML is crucial, as not all machine learning approaches effectively combat all types of fraud. Financial institutions must consider their budgets and expertise while comprehending the use cases and limitations of each technology to implement the most efficient fraud prevention strategies.
Supervised machine learning offers a high level of accuracy when used with substantial datasets to learn patterns, and its accuracy improves over time with frequent model tuning as more labeled data becomes available. SML models also boast fast processing speeds, enabling quick predictions on large data volumes.
However, SML is more reactive, protecting against known fraud types but not the unknown. It requires a large amount of labeled data to train effectively, and the models can become outdated quickly, requiring frequent retuning and training efforts.
Alternatively, UML takes a proactive and real-time approach to detect new fraud patterns without relying heavily on historical, labeled data. UML models have longer lifespans as they are not as dependent on data labels, which can quickly become outdated in the ever-changing world of fraud. The limitations of traditional UML models include that it’s only suitable for specific use cases, it can be computationally intensive and it often lacks explainability or does not address model governance concerns.
Newer and more sophisticated UML techniques have the capability to overcome limitations by providing a higher level of detail to identify hidden connections, using all types of information to retain more data and offering real-time results with minimal delay. Some will also feature built-in support for reviewing fraud to further enhance fraud detection.
Choosing the right machine learning approach for banking
There’s no one-size-fits-all approach when it comes to fraud prevention. Thus, careful consideration of the strengths and weaknesses of UML and SML is essential for FIs to implement the most effective and efficient fraud prevention measures.
An effective ensemble approach should leverage SML and sophisticated UML to capture new attack patterns in real time, support the detection of multiple types of fraud, empower organizations with no labels to detect fraud from the start and utilize all digital signals, even for new values.
Here are the top questions to ask when considering what fraud prevention strategy or provider to leverage:
Does the fraud and risk platform operate in real time?
Does the platform offer both SML and UML capabilities?
What is the detection accuracy rate?
Can the platform account for missing data and fix data quality issues?
Does the platform provide human-readable explanations to support your investigators?
Banks and credit unions must embrace the power of machine learning to combat the ever-evolving threat of fraud. By harnessing the capabilities of both unsupervised and supervised machine learning, financial institutions can enhance fraud detection, minimize losses and foster trust and confidence among their customers.
Jeremy Chen is the senior director of product management at DataVisor.
Checks remain a primary means of fulfilling financial obligations. Technological advances coupled with increased availability at decreased costs have enabled criminals to engage in illegal and/or deceptive practices that include signature forgery, counterfeit checks, and physical alteration of paper checks....