How AI is Revolutionizing Fraud Detection in the Finance Sector

Posted on : September 11th 2024

Fraud is a Major Concern

In 2023, global fraud cost a staggering $485 billion, according to the Nasdaq-Verafin Global Financial Crime Report. Fraud is an increasingly complex challenge, further intensified by the advent of real-time payments and the rise of Generative AI (GenAI). For example, credit card losses worldwide are projected to reach $43 billion by 2026 (Nilson Report).

Financial fraud can be perpetrated in various ways. Examples include:

• Harvesting hacked data from the dark web for credit card theft
• Deploying generative AI for phishing private data and information
• Laundering money through cryptocurrency, digital wallets, and fiat currencies.

Fraud costs organizations an estimated 5% of their annual revenue, according to The Association of Certified Fraud Examiners. Given the billions lost to fraud, early detection is critical. However, identifying behavioral trends or patterns indicative of fraud activity proves even more effective in preventing fraud before it occurs. Artificial Intelligence (AI) plays a pivotal role in uncovering these patterns and enabling proactive intervention.

The AI Edge for Fraud Prevention

A recent PYMNTS survey revealed that more than 40% of the financial institutions (FIs) report a rise in fraud incidents, with 70% identifying AI and machine learning (ML) as essential tools in combating fraud. AI-powered fraud detection systems can analyze vast volumes of transactions in real time, detect anomalies, and enable automated responses to thwart fraudulent activities.

Advanced ML algorithms continuously evolve to identify emerging fraudulent patterns. AI’s adaptability enhances defenses against unauthorized access, allowing FIs to proactively tackle evolving fraud challenges. Key AI use cases concerning fighting fraud include:

• Detecting identity theft by large datasets in real-time to identify suspicious patterns.
• Recognizing unusual credit card transactions by assessing user behavior, locations, and data points.
• Countering phishing attacks through AI-driven analysis of potentially malicious messages based on content, tone, and links.

Compared to traditional fraud detection methods – often reliant on rule-based systems and manual reviews, which are both time-consuming and prone to errors – AI is a transformative solution.

Safeguarding the Financial System

In February 2024, the U.S. Department of the Treasury reported recovering over $375 million through an AI-driven fraud detection system implemented in Fiscal Year 2023.

The surge in check fraud, up 385% in the U.S. since the COVID-19 pandemic, led the U.S. Treasury’s Office of Payment Integrity (OPI) to enhance its AI-powered platform for near real-time fraud detection, significantly accelerating the recovery of fraudulent payments.

Likewise J.P. Morgan has leveraged AI-powered large language models (LLMs) for payment validation since 2021. This initiative has not only reduced false positives but also optimized queue management, decreasing account validation rejection rates by 15%-20%. The outcome: reduced fraud levels and an enhanced customer experience.

Challenges – The Dark Side of AI

While AI strengthens defenses, it also makes fraud attempts more sophisticated. Technologies like deep fakes and LLMs are enabling higher quality fraud attempts.

Thus, a collaborative ecosystem is essential for protection. Collaborating with industry peers can confer several benefits, including:

• Early awareness of new cyber threats
• Sharing best practices and techniques
• Keeping abreast of new or pending regulations

AI’s ability to generate nuanced insights and process data at scale ensures systemic integrity, offering speed and accuracy that manual methods cannot match.

Starting Early Compounds Benefits over Time

AI’s accuracy improves over time as it continually learns from vast datasets. The more information it processes, the more adept it becomes at integrating workflows and identifying potential fraudulent activities at an earlier stage.

However, training AI models requires substantial access to real-world data, which is often restricted by regulatory and private concerns in the financial sector.

A viable solution is the use of synthetic data. According to IBM, “Synthetic data is information generated by computers to augment or replace real data, improving AI models, protecting sensitive data, and mitigating bias.”

Synthetic data enables AI models to explore scenarios beyond historical records, equipping them for real-time decision-making.

In fraud detection, synthetic data strengthens AI models by simulating fraudulent behaviors. Given that actual fraud cases are relatively rare compared to legitimate ones, synthetic data provides additional examples for training, thereby enhancing the model’s capacity to detect suspicious activity.

Not a silver bullet, but Unavoidable for Combating Fraud

While AI alone is not a comprehensive solution for combating fraud, it remains an indispensable part of a multi-layered approach that includes expert analysis and other methods. As AI technology advances and natural language processing becomes more refined, detecting patterns and anomalies will become increasingly seamless.

The integration of both historical and synthetic data will provide rapid and large-scale insights, significantly reducing opportunities for fraudulent activities to evade detection.

How Straive Helps

Straive helps clients operationalize the data> insights> knowledge> AI value chain.

We combine data-driven insights with enterprise know-how to create knowledge unique to an enterprise and its specific problems. This is embedded into digital technology and AIs, and operationalized with human experts-in-loop.

Thus, we facilitate iterative technological improvements while navigating the complexity that it can’t yet address, significantly improving client outcomes.