5 keys to applying machine learning in fraud detection

Key 1: The integration of supervised and unsupervised machine learning models

Cybersecurity experts in organizations such as banks constantly find themselves in combat with sophisticated threat vectors. And these keep getting better and better with the passing of time. Consequently, attempting to fight such vectors using traditional, one-size-fits-all approaches is quickly turning into a child’s play.More effective approaches are multi-faceted and comprehensive, leaving no stone unturned. Supervised models are the most common and are based on clearly labeled data sets. Unsupervised learning, on the other hand, strives to uncover discordant patterns that might be less obvious.

It thus comes in handy in identifying anomalies using untagged or poorly tagged data. This is highly effective for new, previously unknown forms of fraud. Together, supervised and unsupervised models create the optimal blend to keep fraudsters at bay.

Key 2: Making a distinction between generic and specialized behavior analytics

In behavior analytics development, there are specialized as well as general techniques. To illustrate, machine learning algorithms can spot hints suggesting that a customer is likely to churn in the next two months.But in fraud detection, it may be much harder to draw accurate conclusions. Fraud detection requires reliance on a combination of raw data and predictive traits to produce a score. These traits stand for patterns in data from which machine learning models establish patterns or relationships in a dataset.

However, for optimal model performance, data scientists need to improve this discovery process. To illustrate, when a customer does something out of the ordinary, it could read as an anomaly, but how can a model tell if it’s fraudulent or not? Generic models in themselves simply don’t suffice in creating fraud solutions and require specialization so as to be effective.

Key 3: Behavior profiling in application

The idea behind behavioral analytics is not only to understand but also to anticipate behavior during transactions. In this case, the behavior of the individual, device, merchant, and account all come into play.Machine learning models learn from these profiles and can thus predict future behavior. Profiles are by no means limited to monetary information. Rather, they consider such things as password resets, address changes and requests for duplicate cards among others.

The more comprehensive the behavioral analytics are, the more accurate their interpretation of data and prediction of future behavior becomes. Speed also comes into play, as all activities must be updated to provide a complete picture.

With such information at hand, it is much easier for financial institutions to distinguish between legitimate and illegitimate anomalies in behavior.

Key 4: Self-learning and adaptation

Cybercriminals are so advanced that they make protecting customer data extremely challenging. They like to keep things interesting and dynamic, and they always have a new trick up their sleeves.It goes without saying, therefore, that if there is any hope of keeping up with the criminal masterminds, cybersecurity officials need a dynamic and highly adaptive strategy. This particularly applies in marginal decisions.

Some transactions are extremely close to the cutoff, either slightly above it or below specified triggers. Using adaptive analytics makes it possible to attain optimal accuracy in these sensitive cases.

What makes them sensitive is the fact that the line between a false positive and a false negative is extremely thin. These could be legit events that raise eyebrows or fraudulent events that go undetected.

With self-learning and adaptive capabilities, drawing the distinction is easier thanks to enhanced threat knowledge vectors. Separating frauds from non-frauds is, therefore, less reliant on guesswork and more certain.

The models are so powerful that they keep improving performance on margin cases and thus have what it takes to stop new attacks in their tracks.

Key 5: Model training and development by leveraging large datasets

The ingenuity of an algorithm does not matter quite as much for the performance of a machine learning model as the depth of available data. Improving performance and accuracy is all about extending the dataset.In practical terms, think of how much a physician’s knowledge base expands with the number of patients they see during training. Exposure to real-life scenarios helps them acquire experience and make accurate diagnoses in their field.

Similarly, fraud detection machine learning models gain from exposure to billions of examples of all types of transactions, fraudulent and legit. Through the analysis of an abundance of data, algorithms can achieve accuracy in the field. Volume and variety are the heart and soul of machine learning in fraud detection.