Every now and then, there is a fraudulent activity masquerading as the original – no exception to the business world. And if fraud detection is about dealing with smokes and mirrors with barely any room for errors, then Machine learning and AI have grown into reckoned technology forces giving enterprises the hope of clearing the smoke and smashing the mirror.
Given the most complex of a situation – to decide whether it is a fraud being perpetrated or an original one being conducted– and the need to combat even the most-modern fraud tricks, organizations across Banking, Fintech, Insurance, Retail and other industries are using machine learning techniques for fraud detection to unearth subtle fraud patterns, detect anomalies as well as suspicious behaviors, and prevent fraud.
In using machine learning techniques for fraud detection, what sets the prerogative for using machine prediction or anomaly detection or behavioral analytics?
Machine Prediction for Fraud detection
Now, when we are entrusted with this fraud detection and prevention task, data would be the first stop to frame the solution strategy. And with Exploratory data analysis, when we recognize fraud instances from the given data, we can turn to machine prediction as part of using machine learning techniques for fraud detection to predict and prevent fraud.
The historical datasets that come with those fraud instances, more precisely labelled datasets, can be used for training ML algorithm. Pattern recognition is at the core of this approach wherein the ML model learns to spot patterns exhibiting legitimate behaviors or even fraudulent behaviors.
Fraud or not?
The classification part of this problem takes cue from the pattern recognition ability of the model to flag new transactions fed into it as either a fraudulent one or a normal and a legitimate one.
Anomaly detection as the ML technique
Take this case for instance. There is no help from data in terms of finding patterns of fraudulent transactions. That sets the precedence for unearthing unusual occurrences from data. For unveiling the ‘anomaly class’, identifying pointers in terms of variables that can help detect anomalies becomes the starting point.
Let’s sift through 2000 records, say for instance. In exploring the data, if there are 7 critical parameters that are considered significant in identifying anomalous transactions, then pointers are extracted from these parameters in terms of what would constitute as anomalies – Pointers could be the ‘Purchase amount’ column in the set of critical parameters that are identified. Deep learning techniques take it from here. Needless to say, deep learning techniques now build upon the pointers offered to them to come up with various possibilities of anomalies.
Behavioral analytics for detecting fraud
Behavior is the key here to predict fraudulent activity. And this becomes relevant where patterns are concerned, for instance, shopping patterns and payment patterns to name a few. Profiles covering individuals, devices and account are created with the behavioral aspect getting as granular as it can. But there is only a thin line running between the genuine and suspicious transaction, cued in from behavioral insights.
A man who uses his credit card to buy products from an online store always wants the products to be delivered to his address at Michigan. Now, there is the same credit card being used to buy products from the same online store but there is a change in the delivery address belonging to California. This needs an instant answer – Whether to approve or reject the transaction?
In effect, fraud detection and prevention based on behavioral analytics calls for more profiles to be created, which result from advanced profiling – which also reiterates the significance of fraud specific predictive components based on the fraud detection use case.
A buyer has never wasted a chance to make use of all the discount promotions offered by a product – for apparel products. A buyer is habituated to buying online and returning the bought item for another of the same class. These could form the components of a profile. Buying frequency, wherein the time to make a next purchase is taken into account, payment method and locations, typical buying period and time, can also make up for significant components of a profile. In short, behavioral analytics is leveraged to predict future behaviors with the advanced profiles offering real-time information on significant characteristics that help point the likely behaviors in the future.
