As fraud activities get more sophisticated and gruesome in terms of the ‘swindling’ that occurs, enterprises now require an intelligent and a proactive sentry who can thwart fraudulent attempts. With fraudsters conceiving unimaginable, complex crimes, the onus is on the enterprise to come up with sophisticated fraud prevention measures to keep tricksters at bay. Leaning on Artificial Intelligence and Machine Learning, enterprises are combating fraud, detecting signs of fraud and preventing fraud from causing financial damage.
Remaining one step ahead of the fraudster means modernizing fraud detection, using new data science approaches to make the most of data and make better decisions to prevent fraud.
Which is the right ML algorithm for fraud detection?
Adopting the ideal ‘Approach’
Using relevant machine learning models depend on the approach adopted to detect fraud. Approaches that are embraced to detect potential fraud include:
- Anomaly Detection
Classification becomes the right approach when there are enough fraud examples for training the model to detect potential fraud. When fraudulent attempts turn out to be small in number, Anomaly detection serves the purpose in finding out deviation from that of the normal patterns.
When there are loads of credit card usage data, for instance, and when the need points to finding entities behind the fraudulent work or the techniques used by fraudsters to perpetrate fraud or the fraud scenario, clustering would help guide the analysts take the first best step. Clustering is about breaking data down to make patterns visible. This helps in outlier detection wherein dissimilar, inconsistent activities are unearthed.
There are cases where a hybrid approach is used, wherein ‘clustering’ is used initially for generating segments followed by the use of ‘anomaly detection’ on the clusters that are created initially.
Choosing the ‘Right’ ML algorithm
Using the right ML algorithm depends on the data made and the purpose that we are trying to serve. In case of fraud detection, here are some machine learning algorithms that can be used based upon the case in hand.
Let us consider the case of an enterprise wanting to detect fraud, in a way that it wants to unearth prime factors leading to fraud and find out if there is a combination of factors leading to fraudulent activities. If there is requisite to build a machine learning model with the focus on knowing how individual variables produce an impact on fraud and understanding the effect produce by the combination of variables, Linear Regression can be the ideal algorithm for the fraud detection case.
When you want to create decision rules based on frauds committed against an organization, Decision Tree algorithm works well in the scenario. A predictive model is created with the target values learning from decision rules. Samples of fraud that has happened before become the training material for the set of rules.
Take the case of fraudsters. What rises in relevance are the type of fraud committed, phishing URL, for instance, region that unveils more number of fraud customers and the amount swindled by fraudsters. Forming clusters around ‘fraudsters’ then takes significant attributes into account. In the same way, clustering-based fraud prevention can be built using the widely-used algorithm, K-means. In this, k data points are chosen as centroids, randomly, wherein data points are assigned to the nearest centroid.
Take the case of an abnormal purchase made using credit card, where the purchase pattern spotted in the data doesn’t sync with the expected behavior. Going beyond individual variables that are plotted to see anomalies, there are complex cases where many variables play a pivotal role in guiding detection of anomalies entwined with the need to observe the interaction among these variables. When complex models become a requisite, with many variables coming into play, Bayesian Network serves well in this task of anomaly detection.
When it comes to choosing the right machine learning algorithm for fraud detection, focus on data, quality, nature and size of data is essential to make the right move. Moreover, it is also important to consider the business problem that an organization wants to solve, how the data-to-intelligence journey is going to help an organization prior to choosing the right machine learning algorithm for fraud detection.