Breaking the Cycle of Static Fraud Prevention Rules By Shifting to Adaptive AI

Breaking the Cycle of Static Fraud Prevention Rules By Shifting to Adaptive AI

Years of watching George Clooney fire off pithy one-liners on the Vegas strip or Leonardo DicCaprio waltz past airport security in a pilot’s uniform have created a certain romance around the tête-à-tête of a fraudster evading detection.

Now we live in an age of artificial intelligence and that brinkmanship has metastasized into a whole new ballgame—one that makes the familiar cat-and-mouse dynamic look like little league.

The Limitations of Static Rules

Fraud prevention platforms are traditionally governed by a set of “static rules”. That is to say, a set of predetermined criteria used to flag or block suspicious activity. This is where we get our game of cat and mouse. The fraudster adapts their tactics in response to the software adapting its ruleset and we rinse, lather, and repeat. It’s cyclical. And, frankly, it’s a waste of resources.

Fraud detection rules generally follow the same simple structure, establishing conditions which trigger an action when met. So, when Brenda from St. Louis buys $1,200 in prepaid cards from an IP address in St. Petersburg, that transaction is flagged. While their logic may be simple, static rulesets are challenging to maintain at scale. Rules created one day may cancel out a rule introduced the day before and be nullified by new additions by the end of the week!

The Rise of Adaptive AI

Back when static rules were all we knew, the competitive advantage went to whoever was quicker to adapt. The stretch of time between when an emergent fraud technique was identified and when new static rules were implemented to address it used to be a fraudster’s bread and butter. But that was before generative AI hit the scene. Now they can enjoy their bread and butter from the dining car of a proverbial gravy train and this should scare you.

It should terrify you; it needs to. Because let’s not mince words: a fraud team armed with static rules that tumbled out of a human brain is not fighting an “uphill battle”. They’re fighting a losing one and they’re being humiliated.

AI's Advantage in Real-time Response

Suddenly, the enemy is able to respond without delay, adapting their tactics with each transaction. In some sense, fraudsters win regardless of the outcome; either succeeding in their fraud or capitalizing on an opportunity to hone their offensive strategy. But their advantage doesn’t end with quick response times.

The human mind is limited in its ability to identify patterns and anomalies in multidimensional data, often leading to inaccurate or limiting rules. A stream of transactions consists of thousands of behavioral and contextual points of data, or “dimensions”, which are used to detect fraud. Human attempts at designing a complex, multidimensional fraud detection strategy often lead to rules which don’t catch enough fraud, deny too many false positives, or both. In fact, nSure.ai has recorded a 30% higher rate of false positives in businesses relying solely on static fraud rules.

Case Studies and Real-world Applications

By contrast, artificial intelligence is capable of processing and considering any number of dimensions at once. Not only does this allow them to identify patterns distinguishing human beings from bot traffic – but also to profile the quirks and characteristics distinguishing one human from another. The best fraud detection solutions (the only ones capable of curbing scalable fraud) incorporate their own defensive AI which similarly learns from each transaction and continuously adapts its criteria for detecting fraud.

At last, merchants have a means of matching pace with scalable fraud operations and the difference an adaptive solution can make is drastic. Several years ago, Verint implemented adaptive fraud rules as a proof-of-concept for a Fortune 500 company responsible for distributing prepaid debit cards throughout the United States on behalf of a federal agency. They immediately uncovered 35% more fraud than their client even knew existed. By the time they published a case study, this technology had prevented $51 million in losses and was saving the client $10 million year over year. The implementation of adaptive AI has led to a 25% reduction in fraudulent transactions, a 10% increase in transaction volume, and a significant decrease in false positives. Beyond its role in fighting fraud, this technology frees up internal resources so merchants can focus on fostering smoother, more trustworthy customer experiences with minimal disruptions.

The Future of Fraud Prevention

While implementing adaptive fraud rules may level the playing field for merchants, it does nothing to stem the rising tide of scalable fraud brought on by the weaponization of AI. It’s important to remember that “ROI” is the name of the game and technology targeting the systemic reduction of scalable fraud is still in its infancy. Recent innovations in the gift card space have emerged which effectively “troll” the fraudster by showing the transaction as successful, continuing the facade in all the follow-through collateral (e.g. text and email confirmations); and even going so far as to issue a zero-balance gift card displaying a false balance to tarnish their reputation as a seller on the black market.

While there is still a long road ahead in the fight against scalable fraud, the transition from static rules to adaptive AI marks a critical evolution in ecommerce transaction security. This shift disrupts the cycle of chasing fraudsters and aligns businesses with the future of secure, dynamic commerce. Businesses adopting adaptive AI aren’t just reacting to fraud; they’re anticipating it. In doing so, merchants can establish and maintain a secure environment that benefits both their customers and their bottom line.

About the Author 

Ziv Isaiah is the CTO and cofounder of nSure.ai; the payment fraud prevention provider of choice for Gaming, Prepaid and Gift Cards, and Crypto. nSure.ai offers a managed service based on adaptive AI that deploys a dedicated model for each customer which is trained on their unique data points. Their fraud detection solution takes a fundamentally different approach to fighting and preventing modern scalable fraud by focusing on behavior patterns. nSure.ai clients benefit from the highest-efficacy decisions delivered in under 300 ms, with up to 98% approval rates and a chargeback guarantee, generating zero-risk net incremental profit.

Relevant Links

Website: https://www.nsure.ai/

nSure.ai Twitter: https://twitter.com/nsureai

Ziv Isaiah LinkedIn: https://www.linkedin.com/in/zivisaiah/

nSure.ai LinkedIn: https://www.linkedin.com/company/nsureai