Rule-based engines and simple predictive models could identify the majority of fraud attempts in the past, yet they aren’t keeping up with the scale and severity of fraud attempts today. Fraud attempts and breaches are more nuanced, with organized crime and state-sponsored groups using machine learning algorithms to find new ways to defraud digital businesses. Fraud-based attacks have a completely different pattern, sequence, and structure, which make them undetectable using rules-based logic and predictive models alone.
AI Is A Perfect Match For the Challenges Of Battling Fraud
What’s needed to thwart fraud and stop the exfiltration of valuable transaction data are AI and machine learning platforms capable of combining supervised and unsupervised machine learning that can deliver a weighted score for any digital business’ activity in less than a second. AI is a perfect match for the rapid escalation of nuanced, highly sophisticated fraud attempts. Fraud prevention systems can examine years and in some cases, decades of transaction data in a 250-millisecond response rate to calculate risk scores using AI. Taking this more integrative, real-time approach to AI across a digital business yields scores that are 200% more predictive according to internal research completed by Kount. They’ve recently announced their next-generation AI-driven fraud prevention solution as well as a new scoring feature, Omniscore. Omniscore incorporates the most predictive components of both supervised machine learning and unsupervised machine learning and additional predictive factors into one score.
What makes Omniscore noteworthy is how Kount has been able to devise machine learning algorithms that take into account historical data, supervised machine learning trained using Kount’s universal data network that includes billions of transactions over 12 years, 6,500 customers, 180+ countries and territories, and multiple payment networks. The result is a risk score or transaction safety rating that any digital business can immediately rely on to reduce fraud.
Top 9 Ways Artificial Intelligence Prevents Fraud
The future of AI-based fraud prevention relies on the combination of supervised and unsupervised machine learning. Supervised machine learning excels at examining events, factors, and trends from the past. Historical data trains supervised machine learning models to find patterns not discernable with rules or predictive analytics. Unsupervised machine learning is adept at finding anomalies, interrelationships, and valid links between emerging factors and variables. Combining both unsupervised and supervised machine learning defines the future of AI-based fraud prevention and is the foundation of the top nine ways AI prevents fraud:
- AI is re-defining fraud prevention from relying only on past experiences to taking into account emerging activities, behaviors, and trends in transaction anomalies. Before AI, fraud prevention systems would rely on rules alone, which excel at analyzing past fraud patterns without providing insights into the future. By combining supervised learning algorithms trained on historical data with unsupervised learning, digital businesses gain a greater level of acuity and clarity about the relative risk of customers’ behaviors. Decisions to accept or reject payment, stop fraudulent activity to limit chargebacks and reduce risk are all possible now, thanks to AI.
- AI makes it possible to detect fraud attacks in real-time versus having to wait six or eight weeks until chargebacks start coming in. AI’s ability to detect fraud attacks in less than a second using advanced AI-based rating technologies like Omniscore is the future of fraud management. When a digital business relies on structured learning and rules alone, new attacks are very difficult to catch. Chargebacks show up 6 to 8 weeks after the fraud has taken place, and digital businesses rush to update their rules engines. By balancing supervised and unsupervised learning, AI alleviates the need always to play catch-up to online fraud.
- It’s now possible to thwart more sophisticated, nuanced abuse attacks including refer a friend abuse, promotion abuse, or seller collusion in a marketplace. Rules engines and predictive analytics can scale only so far in thwarting fraud attempts. Digital businesses will often revert to stricter, controlling standards for transaction approvals if they have been burned by fraud before. The result is a bad user experience. By having an AI-based fraud prevention system do the work of evaluating historical data and anomalies, customer experiences can stay more positive, and the more sophisticated nuanced abuse attacks can be stopped.
- Provides fraud analysts with real-time risk scores and greater insight into where best to set threshold scores to maximize sales and minimize fraud losses. The best fraud analysts have an intuitive sense of when transaction patterns are legitimate or not. With AI, a fraud analyst receives a 360-degree view of transactions for the first time, having the benefit of seeing historical data in context. Adding in anomaly detection and insights into real-time activity using unsupervised machine learning, fraud analysts can instantly validate or redefine their decision regarding threshold levels, managing risk well.
- AI enables digital businesses to gain greater control over chargeback rates, decline rates, and operational costs so that business objectives can be achieved. One of the most valuable aspects of an AI-based fraud prevention platform is its ability to instantly customize and change business outcomes specific to the entire business, separate products lines, departments, and selling seasons. Digital businesses are relying on the combination of supervised and unsupervised machine learning to attain greater levels of agility, speed, and time-to-market, with AI-based fraud prevention systems being foundational to that effort.
- Enables digital businesses selling virtual goods, including gaming, to provide a more consistent, high-quality user experience on a 24/7 basis. Online games have exponentially grown in popularity over the last five years with online gaming platforms often having over 250 million customers worldwide. Game platform providers want as many people as possible on the platforms to drive advertising, subscription, upsell, and cross-sell revenue. For game platform providers to succeed, they need to provide an immediate, highly responsive purchase experience. Instead of forcing their customers or fans to go through verification, they can assign a risk score to the transaction and fulfill the purchase request in seconds. AI makes it possible for gamers to buy the coins or tokens they need when they need them to keep playing. AI-based fraud prevention systems make it possible to immediately accept the transactions while still staying within the chargeback thresholds from American Express, MasterCard, VISA, and others.
- AI reduces the friction customers experience by helping merchants easily approve online purchases and reduce false positives. One of the paradoxes fraud analysts face is what level to set the decline rate at. Instead of having to make an educated guess, fraud analysts can turn to AI-based scoring techniques like Omniscore that combine the strengths of supervised and unsupervised learning. AI-based fraud scores like Omniscore reduces false positives, which is a major source of friction with customers. All this translates into fewer manual escalations, declines, and an overall more positive customer experience.
- Staying in compliance with internal business policies, those from regulatory agencies and agreements with distribution partners is where AI-based fraud prevention is contributing today. Many digital businesses have internal business policies regarding the sales of specific products to specific countries based on distribution and reseller agreements. Businesses competing in high technology industries need to stay in compliance with export regulations that protect key technologies as well. AI-based scoring and fraud prevention are extensively used to keep businesses in compliance.
- Enables low-margin businesses and product lines to stay profitable by controlling chargebacks levels that have a direct impact on margins. E-Commerce businesses thrive on offering price, availability, and a positive and seamless customer experience. Many sacrifice gross margins for greater scale and more transactions. The challenge is staying profitable while attracting new customers whose purchase history is not part of the supervised learning history of their fraud systems. That’s where an AI-based approach that incorporates both unsupervised and supervised learning pays off from a gross margin standpoint.
originally posted on Forbes.com by Louis Columbus