Fraud Detection using AI: A Shield in a Constantly Shifting Technological Landscape

By Sumedha Rai

Fraud has been a persistent threat to commerce and businesses, significantly undermining their financial health and adversely affecting their bottomline. In today’s fast paced world of technology and digital supremacy, the landscape of fraud is more dynamic than ever. 

With access to sophisticated infrastructure and vast reservoirs of big data, fraudsters have become increasingly adept at innovating and exploiting technological advancements to target individuals and businesses on an unprecedented scale. A recent article from TechRadar mentioned that according to the FBI’s Internet Crime Complaint Center, online fraud cost Americans in excess of $12.5 billion in 2023, which was an increase of 22% in losses year-on-year as compared to 2022

Just as companies leverage big data and robust infrastructure to safeguard their customers, fraudsters exploit the same massive scale to target them. In our digital age, I think it's crucial to recognize that fraudsters are not mere non-technical opportunists seeking small gains. Today, they operate as highly organized groups and leverage cutting-edge technologies to defraud multiple firms simultaneously, accumulating substantial sums of money at an alarming rate, unless deterred. The unfortunate reality is that many customers also hesitate to adopt new technologies, leaving them vulnerable to these technologically advanced fraudsters.

In my opinion, I would go further and state that while fraudulent activities do cause substantial financial losses for both the company and its customers, the real damage extends far beyond the monetary blows. According to a FICO survey, for 32% of consumers, the top priority when selecting a new provider of financial services is robust security against fraud. This is because erosion of trust is hard to repair. Once deceived, customers are likely to harbor acute reservations about engaging with the offending business again.

This not only results in customer churn but such customers are also likely to spread negative word-of-mouth, dissuading potential future customers for the business. In extreme cases, as we have seen in the past, a surge in fraud can trigger cascading effects, leading to a total collapse of customer confidence and dire situations like bank runs.

Given what we have discussed above, static solutions are unlikely to be effective in thwarting fraud. Fraud detection must be dynamic because methods effective today may be circumvented by fraudsters as they adapt and learn over time. It is clear that combating fraud is a high-stakes issue and it is an essential investment for any business. It helps to safeguard a business’s financial wellbeing, strengthen its security measures and ultimately build long term customer relationships. 

 Revolutionizing Fraud Detection: The weapons of Machine Learning and AI

Fortunately for us, technological progress isn’t solely responsible for the proliferation of fraud. In fact, it has equipped us with some of the most powerful tools like novel algorithms driven by artificial intelligence (AI) and machine learning (ML) to effectively combat fraud.

Using AI, we can analyze vast quantities of data – terabytes, petabytes and even beyond – within minutes and seconds, to uncover intricate patterns and anomalies indicative of fraudulent behavior and separate them from good customer behavior. Even an expert analyst with years of experience in detecting digital fraud may struggle to process such vast amounts of data within a reasonable timeframe.

Additionally, despite their expertise, human analysts may not always identify latent or hidden patterns inherent in massive datasets containing millions of data points simultaneously. In contrast, AI/ML algorithms excel in swiftly and accurately sifting through this data, pinpointing deviations from normal behavior that might otherwise remain undetected.

This not only enhances the effectiveness and boosts precision of detecting fraud but also significantly reduces the time required to detect fraudulent activity from hundreds and thousands of data points, by a substantial margin. In 2022, McKinsey gathered leaders in anti-money laundering (AML) and financial crime from 14 prominent banks across North America to discuss the integration of machine learning (ML) into their transaction monitoring systems. Over 80% of these executives had already started incorporating ML technologies into their operations, with the majority planning significant investments towards enhancing their AML frameworks with ML in the next 2-3 years.

Human error may sometimes go up during repetitive tasks due to factors such as fatigue, monotony, and loss of focus over time. Automating transaction monitoring and fraud detection processes with an ML algorithm can effectively mitigate such errors and ensure a more multifaceted and continuous coverage. This brings us to one of the most critical rationales for integrating AI and ML algorithms into a business’s system for fraud detection. In the realm of fraudulent activity, timing is paramount—one never knows precisely when it might strike, and swift action is imperative.

Fraud must be intercepted within seconds, not minutes. This is where the magic of AI comes into play, serving as an ever-vigilant 24/7 guardian. In a recent interview with CNBC, Mastercard spoke about generating risk scores for its customers to detect fraud in just 50 milliseconds. In certain cases, Mastercard saw a 300% improvement in fraud detection rates using their AI models. 

AI powered systems can facilitate the detection of fraud in real-time and raise relevant alerts. They can monitor and flag suspicious activities, right after a transaction occurs. This can enable companies to take immediate action, such as contacting the customer or blocking the transaction, before any significant damage is inflicted. Depending on the permissions, a fraud system can also autonomously block transactions outside of regular hours, affording companies the opportunity to review them during business hours.

Furthermore, as the name suggests, using a machine learning algorithm implies that the machine (fraud system) learns over time. This means that an AI algorithm will improve itself rapidly as it analyzes more data and encounters new fraud schemes. This continuous learning process of identifying suspicious patterns and behaviors ahead of time enables organizations to proactively tackle emerging threats.

The importance of fraud detection cannot be overstated for any organization or within any industry. Fraudulent activity is like a moving target but with the help of powerful AI techniques, we have been able to bolster fraud detection efforts significantly in recent times. I would encourage businesses to build a robust AI shield for themselves against the ever-changing threats posed by fraudsters. This way they can continue to innovate and grow while preserving trust among their customers and stakeholders. 

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