How AI is Enhancing Fraud Detection

The modern digital finance network is one of the most dynamic, fast-moving technological entities on the face of the planet. According to Statista, in 2016, there were 40 million card transactions processed per day in the UK alone, forecast to rise to some 60 million by 2026.

Worldwide, you’re talking about literally billions of transactions, many of which involve significant quantities of money. Fraud rates have mirrored the growth in digital payments; global losses from fraud tripled from $9.84 billion in 2011 to $32.39 in 2020. In recent years, AI and machine learning (ML) have joined the anti-fraud arms race.

Using a combination of time-series analysis, predictive analysis and anomaly detection, ML and AI algorithms provide you and your clients with an effective way to fight back against fraud.

How AI is Overcoming the Practical Issues of Fraud Detection

The Speed of AI-Enhanced Fraud Detection

According to TowardsDataScience, over 83% of fraud reviews are still conducted manually. Payments, loan applications and other fraudulent activity are classified using linear logic, or “rules.” These rules may be tailored to each business and its unique operations, but conceiving the rules in the first place is a tricky process and dependence on qualitative methods leads to a less-than-robust anti-fraud strategy.

Data-driven anti-fraud strategy helps clients tap into a more sophisticated and nuanced quantitative or multi-level strategy. This is both faster and more accurate.

Fraud detection is extremely time-sensitive and manual reviews are time-consuming – the two are rather antithetical to each other. Manual or semi-automated fraud detection often fails to prevent financial damage, which is the main aim of the game!

Manual anti-fraud strategy requires significant training, is archaic in its use of ad-hoc rules and often slows down customers. And then there’s the issue of bias and accidentally declining legit payments – some of which could be valuable to your clients. Through anomaly detection and time-series analysis, AI works at scale to locate anomalies from real-time data streams. Since anomaly detection platforms work with real-time data, they will enable your clients to make quicker decisions than if they were using ad-hoc rules and a manual review process.

Often, this can be the difference between financial loss and successful loss mitigation.

AI’s Digital Edge

Fraud is still overwhelmingly carried out by a human operative and involves human decision-making and strategy. Fraudsters often succeed because their actions are lost in noisy data – it’s exceptionally hard to delineate legitimate actions from fraudulent actions without an accurate conception of all the data. The noisier the data, the easier it can be to slip under the radar without detection.

In data engineering, this is called a classification problem, but modern anti-fraud ML algorithms can induct vast quantities of real payment data, including noisy data, and through a combination of oversampling, undersampling and combined class methodology, they’re able to cut through the noise and accurately classify fraudulent anomalies.

Using traditional fraud detection methods, detecting an anomaly in scarce data is relatively simple, but we’re no longer dealing with scarce data, we’re dealing with thousands, or even millions of payments. AI is very effective at working with these large, complex data sets. The contemporary nature of AI and ML-driven time-series analysis and anomaly detection is cut-out for contemporary fraud. Vast resources of historical payment data allow data scientists to engineer and create increasingly complex algorithms that have the benefit of both hindsight and foresight. Noisy data is no longer the problem that it once was.

Enhance Customer UX

Fraud affects clients as well as their customers. False positives – falsely classified fraudulent activity – are catastrophic for customer relations. Falsely highlighting a customer as a fraud risk, resulting in a declined payments, loans, credit or other financial requests can permanently damage the relationship.

Customers lost in this way will likely just switch to a competitor. One key area here is customer verification or KYC, which has become a mandated regulatory responsibility for many industries worldwide.

The issue is, manual age verification can cause customer friction; Idology’s Sixth Annual Fraud Report found that 75% of surveyed businesses highlighted verification as a source of customer friction and churn. Slow verification processes could lead to product abandonment, with potential customers simply moving onto the next available competitor. On the flip side of the coin, failure to verify identity properly results in fraud and regulatory risk.

AI-enhanced services take the legwork out of anti-fraud ID verification, reducing customer friction. AI systems remove human error and judgement from the equation, properly classifying legitimate and fraudulent verification attempts. Again, this yields both customer and client-side benefits.

The benefits of AI for fraud detection are wide-ranging and constantly developing. By keeping your finger on the pulse of new developments in AI-related anti-fraud strategy, you can equip your clients with what they need to manage fraud and thrive in an increasingly digitized financial world.