4 Major Challenges facing Fraud Detection; Ways to Resolve Them using Machine Learning
Fraud detection has been one of the major challenges for most organizations particularly those in banking, finance, retail, and e-commerce. This goes without saying that any fraud negatively affects an organization’s bottom line, its reputation and deter future prospects and current customers alike to transact with it.
More often than not, for any fraud detected, the organization ends up paying for the losses. Additionally, it takes the good customers away from them while attracting more fraudsters.
Given the scale and reach of most of these vulnerable organizations, it has become indispensable for them to stop these frauds from happening or even predict all suspicious actions beforehand at all times.
Frauds can range from really small like non-payment for e-commerce orders to threatening (to organization’s existence) like public exposure of customers’ credit card details.
Machine learning comes to the rescue here. On setting up automated data science processes with deep learning algorithms, organizations can greatly reduce the risk of their exposure to most of such frauds.
Existing Fraud Detection Methods
Detecting and reducing fraud using artificial intelligence isn’t new. There are machine learning models already being deployed by enterprises across the globe.
Most modern fraud detection methods (employing Machine Learning) involve a domain expert tasked with 2 responsibilities -
1. They are needed to gather historic transaction data, and
2. Help with the feature generation process for a classic or advanced machine learning models.
The said features are derived from the raw data to be used in detecting frauds. A simple example could be an “incorrect zip code entered” which indicates potential fraud.
Based on these features generated from the historical data, machine learning models are build to detect or preempt any fraud.
The path to fraud detection isn’t a straight line, though!
The method explained above isn’t perfect in the true sense. Here are some of the challenges that complicate the fraud detection process -
1. Changing fraud patterns over time — This one is the toughest to address since the fraudsters are always in the lookout to find new and innovative ways to get around the systems to commit the act. Thus it becomes all-important for the deep learning models to be updated with the evolved patterns to detect. This results in a decrease in the model’s performance and efficiency. Thus the machine learning models need to keep updating or fail their objectives.
2. Class Imbalance — Practically only a small percentage of customers have fraudulent intentions. Consequently, there’s an imbalance in the classification of fraud detection models (that usually classify transactions as either fraudulent or non-fraudulent) which makes it harder to build them. The fallout of this challenge is a poor user experience for genuine customers, since catching the fraudsters usually involves declining some legitimate transactions.
3. Model Interpretations — This limitation is associated with the concept of explainability since models typically give a score indicating whether a transaction is likely to be fraudulent or not — without explaining why.
4. Feature generation can be time-consuming — Subject matter experts can require long periods of time to generate a comprehensive feature set which slows down the fraud detection process.
How to tackle these challenges?
Fortunately, there are several measures available to resolve these challenges. Some of them are -
1. Ensemble Modeling — To tackle the ever-evolving fraudulent patterns, Ensemble modeling leverages multiple models for a single task such as fraud detection. Ensembling with classic machine learning, deep learning, and linear models can capture various fraud patterns to maximize outputs. For example, an LSTM (Long Short Term Memory) deep learning model is useful for detecting fraud in a sequence of events. If a user logs in with a new IP address from a different city, changes his street address on file, then purchases an expensive item on an e-commerce site, LSTM might flag this transaction as fraudulent. None of these events alone is indicative of fraud, but the sequence of all three is.
2. Human-in-the-loop — This technique addresses the classification imbalance issue as well as shortens the time taken for feature detection. It involves humans assisting the models by providing information to identify new patterns, features, and dimensions of fraud. In the preceding e-commerce use case, for instance, a human could denote that such a sequence was indicative of fraud. The model will then extrapolate this information and apply it to different use cases, such as when users change email addresses instead of physical addresses. Based on human input, the model learns from these examples then identifies more from its own learning.
3. Explainability — The concept of explainable AI can provide reasons for the approval or decline of transactions as fraudulent, solving the model interpretation challenge. Specific explainability techniques such as surrogate modeling, maximum activation analysis, and others provide these benefits.
The way forward
Each of these resolution techniques augments the machine learning models to increase their efficacy and reduce the instances and degree of frauds. In other words, they’re foundational for using both classic and advanced machine learning for fraud detection. In the future, challenges for fraud detection are most likely to transform and metamorphosize into unseen impediments based on the evolving ways fraudsters commit these illicit acts. Nevertheless, the aforementioned resolutions can ensure organizations’ fraud detection measures evolve as well, decreasing this crime’s impact.
Want to learn more about Fraud Detection using machine learning? Here is a use case to demonstrate a real-world payment fraud scenario, proposed solution and the approach followed on the Razorthink AI Platform.
