The new age of fraud detection: AI, machine learning, and cross-institutional collaboration

The rise of social engineering attacks poses a major challenge in the fight against fraud. Their sheer scale, speed, and diversity make them incredibly difficult to detect and prevent—and even more so when they originate from the inside. 

Take the Capital One incident from 2019, in which a former employee of AWS exploited a vulnerability in the bank’s cloud infrastructure to steal the personal information of over 100 million customers, including SSNs. Despite all of the safeguards and fraud detection systems in place, Capital One failed to monitor or detect the unauthorized activity. Wirecard, Tether, and countless others have been victims of similar schemes. 

Combatting today’s rapidly evolving attacks requires real-time fraud detection systems capable of identifying complex patterns across millions of data points. The most sophisticated models even incorporate biometrics and other advanced tech, helping to identify potential threats as they emerge, before they claim victims. 

THE RISE OF SOCIAL ENGINEERING ATTACKS

Subtle changes to texts and emails trick even the most tech-savvy into falling victim to phishing and other scams at a rate of around 300,000 people per year. These attacks aren’t just becoming more sophisticated—they’re becoming more frequent. With attackers leveraging automation and AI to launch large-scale campaigns on autopilot, they continuously evolve their strategies so they’re able to bypass security measures and make it into more inboxes. 

Traditional security systems, which rely on finite rule sets and periodic checks, are simply outmatched by the sheer volume and complexity of these attacks. By the time a new phishing campaign is identified and countermeasures are put in place, the attackers have already moved on to the next iteration, exploiting new vulnerabilities. 

To win, we have to fight fire with fire. 

LEVERAGING AI AND MACHINE LEARNING FOR FRAUD DETECTION

Just as fraudsters continuously refine their techniques, leverage new technologies, and exploit emerging vulnerabilities, fraud detection systems must also be designed to ingest new data continuously, update their models, and refine algorithms to ensure that we can detect and respond to the latest threats in real time.

Advanced machine learning models perfectly fit the bill. By ingesting and processing data from multiple sources, including transaction logs, user behavior patterns, and network traffic, these models can gain a comprehensive understanding of operations and quickly flag deviations that could indicate fraud. This adaptive capability is particularly crucial when it comes to social engineering attacks. 

COMBATING INSIDER FRAUD

Insider threats can take on various forms, from exploiting bugs or vulnerabilities in systems like ATMs to breaching physical security measures. Comprehensive monitoring and analysis of user activity and access patterns are central to detecting these attacks. 

Advanced machine learning models can establish baselines for normal behavior and quickly identify anomalies that may indicate malicious or unauthorized actions. Detailed logging and auditing of user interactions, combined with behavioral analysis, can also help detect potential insider threats before they can cause significant damage.

But insider threats can’t be defeated in a silo. By pooling collective intelligence and creating a roundtable of discussion, companies can work together to stay ahead of attackers. This act of information sharing can help you identify potential vulnerabilities and implement proactive measures sooner, keeping everyone’s users safer. 

THE FUTURE OF FRAUD DETECTION

As fraudsters leverage cutting-edge technologies to launch increasingly sophisticated attacks, fraud detection systems must evolve to match and surpass their capabilities.

Speed, scalability, and adaptability will be critical design principles for the next generation of fraud detection solutions, and continued advancements in complex technologies like quantum computing and AI will prove essential in hitting these marks. For instance, quantum-resistant cryptography will make it easier to secure sensitive data and communications, ensuring that even the most advanced quantum computing technologies cannot compromise the integrity of fraud detection systems.

However, amidst all the technological advancements to come, organizations must seek a delicate balance between security and customer experience. The ultimate goal is to achieve an “invisible” security posture, where fraud detection and prevention measures seamlessly integrate into the customer journey, enhancing their experience rather than hindering it. 

In the years to come, organizations that can successfully navigate this balance and continue to outpace fraudsters will not just avoid setbacks. FinTech companies that find a way to provide superior security and an overall frictionless experience for customers will win out in terms of customer satisfaction and loyalty, too.