How to Boost Secure Digital Payments With ML Fraud Detection

The rapid proliferation of digital payments in recent years has led to increased fraudulent activities. This is a serious concern for consumers, financial institutions, fintech enterprises, and even the government. Many organizations are working to identify various methods, including machine learning (ML), to detect fraudulent activities. Businesses are discovering that the effective use of ML algorithms combined with traditional digital payment methods can improve fraud detection.

Instant payment adoption is on the rise by many industry leaders, including those in gig work like DoorDash and Instacart. Banks are incorporating new real-time transaction and payment protocols. In addition, mobile payments are expanding, with mobile wallets and contactless payments—such as Apple Pay and Google Pay—becoming ubiquitous, allowing secure one-tap payments via biometric authentication, like fingerprint scanning and facial recognition. Because real-time payments are processed instantly, if a fraudulent transaction occurs, the funds are immediately taken from the victim’s account, making recovery much more challenging.

Fraudulent payments in high-volume transactions are costly in terms of customer support and dispute resolution—almost 60 percent of organizations in the financial industry lost over half a million dollars each in 2023. Traditional fraud detection systems that depend on batch processing and time-consuming verification processes are insufficient in the modern digital landscape. Advanced analytics, artificial intelligence (AI), and ML technologies that quickly analyze and flag suspicious activities within milliseconds are critical, and many companies are adopting this strategy. For example, Stripe’s ML model has been trained with hundreds of billions of data points to quickly detect fraudulent transactions.

Multi-Layered Architecture

A robust payment fraud detection system uses a multi-layered architecture that combines the strengths of various techniques. Upper layers have low response latency but are limited in data analysis; lower layers are computationally expensive but perform deeper analysis. The bottom layers aren’t run if the layer above catches a fraudulent transaction and serves as the learning ground for new patterns. This architecture aims to catch most fraud events early via the top layers in real time, thereby increasing product usability.

The first layer validates using rule-based systems. It relies on simple, high-performance, rule-based systems that filter out obvious fraud cases. The cases are based on predefined criteria that are static rules and thresholds authored by human experts that leverage the user’s historical data. These real-time scoring algorithms must be fast, computationally efficient, and scalable, yet they struggle to adapt to new fraud tactics and patterns.

The second layer screens cases using fast ML algorithms and has sub-second-level latency. It uses computationally efficient ML algorithms suitable for real-time streaming analysis. The following are common models and their specific applications to detect fraud in this layer:

• Logistic Regression: Assigns probabilities to transactions based on input features like transaction amount, time, location, and user history. By calculating the likelihood that a transaction is fraudulent, the model provides a straightforward, interpretable risk score that can be used to flag suspicious transactions for further investigation.
• Decision Tree: Creates a flowchart of decision rules. Each node in the tree evaluates a specific feature, like transaction amount, time, or location, leading to a final classification at the leaves to determine whether a transaction is likely fraudulent.
• Naïve Bayes Classifiers: Computes the probability of a fraudulent transaction based on individual features like merchant category, transaction amount, and transaction frequency. They can quickly and effectively flag transactions that exhibit unusual feature combinations.
• K-Nearest Neighbors (K-NN): Evaluates new transactions based on their similarity to past transactions. The model classifies a transaction by examining its closest “K” neighbors, such as transaction amount, location, and time. If most of these neighbors are labeled as fraudulent, the new transaction is flagged as suspicious, allowing for the detection of fraud patterns based on historical data.

The third layer has second-level latency and aims to perform a deeper data analysis via ML. Here are typical models and their specific applications to detect fraud in this layer:

• Random Forest: Constructs multiple decision trees during training, each using a subset of transaction features like amount, location, and user behavior. When a new transaction occurs, the model aggregates its trees’ predictions to classify the transaction, capturing complex interactions and patterns that a single decision tree might miss.
• XGBoost: Iteratively improves the prediction accuracy, where each new model corrects the errors of the previous ones, locating intricate patterns and non-linear relationships among transaction features like amount, time, and location.
• Support Vector Machine (SVM): Finds the optimal hyperplane that separates fraudulent and legitimate transactions in a high-dimensional feature space. It identifies fraud by maximizing the margin between the two classes based on transaction attributes.
• Neural Networks: Convolutional neural networks (CNNs) and recurrent neural networks (RNNs) learn complex, high-dimensional patterns from large datasets. RNNs analyze the sequence of transactions for each user, identifying unusual behavior, while CNNs detect spatial relationships in transaction data. Combined, these models can uncover subtle, non-linear fraud indicators that simpler models might miss.
• Autoencoder: Learns the typical patterns of legitimate transactions during the training phase. When a new transaction is processed, the autoencoder attempts to reconstruct it based on the learned patterns. Transactions with high reconstruction errors, which indicate a significant deviation from the norm, are flagged as potentially fraudulent, identifying unusual or suspicious activities that may go unnoticed by other models.

Challenges

Model interpretability is a frequent problem when building an ML-based fraud detection system. ML engineers mitigate this by identifying the right metrics to track algorithms’ performance during training to shed light on their operation and the reasons behind potential bias and inaccuracies. Another issue is balancing security with usability. Strict fraud detection software may block legitimate transactions, resulting in false positives, while being too lenient can lead to successful fraud, causing false negatives. Training the ML algorithms on large validation datasets and setting a suitable threshold that specifies the minimum requirements to trigger a response will produce a balance.

Businesses with custom fraud detection needs often opt to build custom solutions, but this can be demanding regarding data availability, ML model training times, and computational resources. Companies looking to reduce upfront costs, implementation timeframe, and maintenance efforts can adopt one of the off-the-shelf systems available on the market, including those from trusted cloud providers with pre-trained models, scalable computing resources, and built-in algorithms. The ML systems’ need for training data and compliance with applicable legislation can be challenging to navigate, especially in finance and accounting. Using data masking techniques to obfuscate datasets before ML algorithms process them will ensure compliance.

ML techniques are part of mainstream application development and are fast emerging as indispensable tools in the battle against fraudulent activities in digital payment systems. Investing in the rapid advancement of ML models that encompass advanced anomaly detection, real-time detection, behavioral biometrics, and explainable AI (XAI) addresses the fraud threats of today and in the future, minimizing financial losses while protecting enterprises and consumers and restoring trust. As digital payment ecosystems continue to transform and expand, the role of ML will become even more critical, underscoring the importance of ongoing investment and innovation in fraud detection technologies.

About the Author

Akshay Rawat is an engineering lead at Checkr Pay, where he creates payment platforms that cater to the unique requirements of gig workers and platform operators. With more than 20 years of industry experience and entrepreneurship, Akshay specializes in engineering scalable fintech systems. Connect with Akshay at LinkedIn or at akshay.rawat@gmail.com.