Fraud Analytics — Strategies and Approaches

The menace of fraud is increasingly becoming more complex and widespread in the digital age. This threat transcends across sectors. Fortunately, the domain of data science, machine learning and AI provide effective ways to not just react, but also to proactively devise mechanisms to fight fraudulent activity. In this article, we discuss four approaches that stand out due to their inventiveness and efficacy.

Let’s explore these four key strategies:

Approach 1: Machine Learning with Labeled Data

This is a supervised machine learning approach that relies on training the machine learning model on historical data that has been labeled as ‘fraudulent’ or ‘non-fraudulent.’

There is a family of ML algorithms, such as logistic regression, decision trees, random forests, or deep neural networks, that can be employed to do this task.

Example: Consider a credit card company that has labeled past transactions. A machine learning model can be trained on these transactions, learning from features like transaction amount, nature of transaction, geographical location, IP address, and time of transaction. When a new transaction occurs, the model assesses it based on learned patterns and flags it if it resembles known fraud.

Approach 2. Anomaly Detection

Basically the idea here is to identify patterns that significantly differ from expected trend or behavior. It’s particularly useful in fraud analytics for identifying novel or previously unseen fraudulent tactics.

For example, if a user in a corporate setup suddenly downloads an unusually large volume of data or accesses sensitive information at odd hours, the system can flag this as an outlier, potentially indicating insider fraud.

Approach 3. Clustering

Clustering is an unsupervised machine learning approach which involves grouping data points in such a way that items in the same group (or cluster) are more similar to each other than to those in other groups. Clustering is done using some form of mathematical distance metrics, like the Euclidean distance. Since it’s an unsupervised learning approach, it doesn’t require labeled data.

Example: An e-commerce platform could use clustering to segment transactions based on various parameters such as product type, order value, shipping address, and payment method. Data points or clusters that indicate unusual patterns can ring the danger bells, and raise the red flags.

Approach 4. Fraud Detection with Text Data

This is not a traditional method of fraud detection. However, we have seen in the Enron fraud case, how text analytics on the email data was used to identify and highlight fraudulent activity.

Natural language processing (NLP) techniques can be used to parse and understand the text, looking for red flags or indicators of fraud.

For example, in insurance claims, NLP can be applied to claim descriptions. Specifics such as certain words, phrases, inconsistencies in the narrative, or other indicators could be extracted that suggest a claim might be exaggerated or entirely false.

To conclude

Each of these approaches offers unique advantages in the fight against fraud. The approach to be used of course depends on the use case, data availability, and the end goal.

But the best result might just be to combine these approaches, wherever possible, as that would enable organizations to develop a robust and multifaceted strategy, to stay ahead in the ongoing battle against fraud.

Hope you liked this article and found it useful. Please share your approach to this burning problem in the BFSI space. If you’re as passionate about AI, ML, DS, Strategy and Business Planning as I am, I invite you to connect with me on LinkedIn.