The Simility Blog
The Future of Fraud Prevention: Why Businesses Must Look Beyond Point Solutions
Rahul PangamAugust 30, 2018
Businesses need data-driven adaptive platforms that blend insight from multiple sources to fight ever evolving fraud.
Global organizations are facing a cyber-fraud epidemic as the race to digitize gathers pace. In the US, online fraud attempts are said to have jumped 22% during the 2017 holiday season while across the Atlantic, identity fraud reportedly hit an all-time-high in the UK last year. The stats are mirrored globally, where two-thirds (65%) of businesses claim they’ve experienced the same or worse fraud losses over the past 12 months.
The rapid evolution of technology has become a cat-and-mouse game between businesses and fraudsters, putting increasing pressure on fraud managers to find the right tools to manage risk without increasing customer friction. AI and machine learning are important but not a silver bullet. The answer lies not in point tools but in data-driven adaptive platforms that blend insight from multiple sources.
The Weakest Link
There’s no doubt that an over-reliance on humans continues to be a major point of weakness for organizations. Cyber-criminals are always ready, willing and able to capitalize on human error — whether that’s the failure to install a newly released software patch or logging-on to an open Wi-Fi hotspot. Humans also represent something of a weak link in fraud prevention: potentially causing manual errors and inconsistencies in decision-making, and being slow to respond and adapt to changing trends.
On the other hand, fraudsters operate within strong knowledge sharing and connected global networks. Increasingly they know their targets’ systems and operational procedures inside out, while the dark web provides an endless source of digital identity profiles and tools to launch sophisticated global attacks. It’s no surprise that fraud campaigns are growing in size, complexity and frequency as a result.
Differentiating On Data
Data remains the biggest differentiator in the fight against fraud and cybercrime. Artificial intelligence and machine learning (AI/ML) can harness insights and add tremendous predictive power to the decision-making process. Using these tools in real-time helps predict the risk linked to each event or transaction, enabling businesses to detect anomalies and stop fraud even before it has a chance to make an impact. This is what all organizations should be looking to achieve: preventing as opposed to reconciling fraud.
Advances in automation can also help. The machine learning model development lifecycle, feature selection, model build-test, model tuning-retuning, and model deployment can all be automated today, significantly reducing the time it takes to continuously improve fraud prevention models. This begins to mitigate those challenges around human error, speed of response to changing trends, and inconsistencies in decision-making. With greater automation, the organization can move towards a more dynamic, multi-layered fraud prevention model able to adjust quickly based on the threat level per event.
However, it’s important to remember not to rely too much on a single technology or approach.
Most AI platforms suffer from false positives and overfitting which can lead to a deterioration of results over time. So, while automation and AI tools are great at processing large-scale data to generate insights, human involvement is ultimately needed. The end goal should be an adaptive fraud prevention platform that empowers the human analysts in an organization — making them more efficient by delivering scale, explainability, governance and adaptability.
Armed For The Future
The only constant in the fight against fraud is the certainty that attack patterns, surfaces and vectors will continually evolve. That means as point solutions are developed to tackle new attack patterns, they quickly become ineffective as the fraudsters shift their techniques. Loopholes even exist in biometrics which fraudsters are constantly looking to exploit. As fraud and organized crime continue to converge, more structured resources will be made available to launch large-scale attacks, challenging us even further.
The key, therefore, is to look beyond point solutions such as biometrics, behavioral insights, machine learning and AI, and rely instead on adaptive platforms that harness insights from multiple data sources and have the agility to make real-time adjustments. That’s the only way to stop fraud while preserving the customer experience.
Contact us to see how our Adaptive Decisioning Platform will help you reduce fraud, while providing deep user insights for accurate decisions.