Will AI-Based Fraud Detection Shape the Future of Regulatory Compliance?
In today’s evolving, largely digital economy, the exponential rise in online banking transactions has led to a near equal increase in cases of financial fraud. Today, bad actors have come to employ highly sophisticated methods in their own right to bypass the conventional modes of detection previously used by individual consumers and financial institutions alike to protect their respective funds. While the utilization of artificial intelligence (AI) across a number of sectors remains a polarizing topic, it is almost undeniable that it is already developing into a transformative tool in the financial realm. Many a modern financial institution, regardless of stature, employs advanced machine learning processes and algorithms that can process vast amounts of data in real-time to identify irregular patterns often indicative of financial crime. As time passes, these processes are only growing more efficient and accurate, creating unparalleled levels of operational efficiency in the banking sphere. In line with this mass adoption, an increasing number of financial organizations are also increasing their cybersecurity spending to better stack up against today’s pervasive cyber-crime threats, while also ensuring compliance with ever-evolving state and federal regulations.
According to Gartner’s 2023 Global Security and Risk Management forecast, end-user spending on cyber-security was projected to reach a whopping $215 billion collectively in 2024, a 14.3% rise from 2023.1 The AI fraud detection market alone contributes a significant portion of this percentage and will do so moving forward, underscoring its importance in this space. These protocols, which generally use machine learning algorithms to identify and prevent fraudulent activities by analyzing patterns, behaviors, and anomalies in data, also involve real-time monitoring, predictive analytics, and continuous learning that adapts to evolving fraud tactics and mitigates potential threats both in the moment and the future by analyzing the types of transactions an individual might come to make, and recognizing if a new type of transaction appears unusual.
Several of the driving forces behind current AI-based fraud detection protocols are detailed below.
Supervised & Unsupervised Learning in Fraud Detection
Supervised learning involves training AI models on labeled data, where each transaction is tagged as fraudulent or legitimate. This approach is particularly effective for detecting known fraud patterns while leveraging historical data to better classify new transactions. Supervised learning operates using “ensemble methods” which combine multiple weak models to capture data to create a stronger, more efficient one.2 Two of the top models in this sphere are the Random Forest and Gradient Boosting models. The former combines multiple decision trees, each trained on a random subset of the data – to make predictions based on majority voting for classification tasks. Random Forest is arguably the most effective machine learning model in fraud detection due to its ability to handle large datasets and to capture complex interactions between features, such as transaction amounts, locations, and frequencies. Gradient Boosting Machines, on the other hand, build decision trees sequentially, with each tree correcting the errors of the previous one, making it effective for analyzing imbalanced datasets where fraudulent transactions are rarer.
Unlike its counterpart, unsupervised learning is unique in that it does not rely on labeled or historical data to make its conclusions, which allows it to better detect new and emerging fraud patterns. It instead focuses on anomaly detection, identifying transactions that deviate from normal behavior for a user or firm. This process often clusters similar transactions together, with outliers (i.e. transactions not fitting into any specific cluster) flagged as suspicious. Unsupervised methods are widely viewed as the most adaptable means of AI, as they can identify emerging fraud tactics and complement supervised approaches by addressing evolving threats.
Deep Learning in Fraud Detection
Another AI subset that is paying dividends within the financial space is Deep Learning. This process uses a variety of artificial neural networks to process and learn from data in real time, similar to the function of the human brain. This method is particularly effective for modeling complex patterns in sequential data, such as transaction histories. The primary calling cards of this specific subset are Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM) networks, which are models that can detect unusual transaction sequences and behavioral anomalies. LSTM’s in particular are ultra-effective in enhancing the detection of suspicious transactions by uncovering complex patterns within large datasets, such as sudden spending spikes or transactions made in unusual locations that can otherwise slip through the cracks. This has made these processes ideal for credit card fraud detection due to its ability to capture long-term patterns effectively in one frame.
Implications on Crime
While still evolving, the various AI techniques already on the market work to complement each other, with supervised learning excelling at identifying known patterns, unsupervised learning at analyzing novel threats, and deep learning thriving at sequential analysis. As such their implications for modernizing fraud detection are rather significant, as AI has been instrumental in preventing and recovering substantial amounts of fraud in the financial sector while still in its relative infancy. Further, the ability to process vast datasets in real-time renders physical manpower less vital to a business, increasing a firm’s bottom line and ensuring financial institutions can stay ahead of fraudsters, allowing them to maintain customer trust and operational efficiency. As fraudsters continue to search for new ways to exploit unwary individuals and businesses, the adaptability and scalability of AI will become ever more crucial to ensuring the integrity and security of financial transactions globally.
Citations
- Financial Services Cybersecurity: 2024 Performance in Banking, Financial Services, and Insurance (BFSI), Picus Security, 26 Dec. 2024.
- Idrees, Hassaan. “Gradient Boosting vs. Random Forest: Which Ensemble Method Should You Use?” Medium, Medium, 14 Oct. 2024.