Applying AI to Cyber Risk

Applying AI to Cyber Risk

As cybercrime and fraud grow increasingly sophisticated, applying AI to cyber risk and to combat fraud as well as to take remedial action, is an important area of innovation.  Fraudulent transactions and theft happen daily and many businesses and individuals lose revenue without knowing.

Organized criminals and online scammers are increasing the sophistication and scale of their attacks. These attacks are becoming more subtle, with many scamming groups using machine learning algorithms to find new ways to target individuals and online businesses. Traditional approaches are proving inadequate to detect these attacks due to their ever-changing pattern, sequence, and structure. Traditional approaches also don’t capitalize on today’s technological capabilities.

According to McAfee’s latest report, fraud costs the global economy an estimated $600 billion annually. The sheer magnitude of this number creates strong incentives for fraudsters. Automated attacks allow fraudsters to input numerous logins and passwords in a short amount of time. If one combination is successful, they will have access to someone’s bank account or credit card giving them authorization to transfer or wire money to different accounts. It is a serious threat to businesses and clients, costing billions of dollars each year.


AI and Fraud Detection

Artificial Intelligence is the ideal application for detecting and avoiding financial crimes. AI can be used to analyze large volumes of transactions to uncover complex fraud trends and detect fraudulent transactions in real-time. The improved accuracy and versatility AI offers promises substantial cost reduction for many industries and sectors. In addition, Machine Learning algorithms can not only distinguish legitimate and fraudulent behaviors, they also respond to new, previously unknown fraud methods by learning on the fly.

The future of AI-based fraud prevention lies in the combination of supervised and unsupervised machine learning. In supervised machine learning, historical events, trends, and factors are analyzed. In unsupervised machine learning, anomalies and interrelationships between new and old variables are identified. The combination of these two types of machine learning define the overall success of the solution, and forms the foundation for AI-based fraud prevention.

AI enables fraud prevention solutions to scale by integrating the results of supervised and unsupervised machine learning into one risk score. AI can analyze and interpret large amounts of data quickly and deliver a response that is an order of magnitude more predictive than traditional rule-based approaches.

Online businesses benefit the most from AI fraud prevention. Their growth is largely dependent on offering competitive pricing, accessibility, and a positive and seamless customer experience. However, fraudulent transactions costs businesses millions of dollars and can often lead to stricter transaction approval standards for their customers.  Many merchants will trade gross margins for greater scale, customer convenience, and increased transactions.

But how can businesses remain profitable while attracting new customers whose purchase history is not part of the supervised learning history of traditional fraud systems? The solution is in AI-based fraud prevention with both supervised and unsupervised learning that provides better business outcomes.


Market Opportunity

A key factor that has contributed to a wider attack surface has to do with how software has been re-platformed over the past few years.  At the application layer, we have seen the rise of APIs, micro services, and deployments that span on-premise, cloud, and hybrid configurations.

Net-net, these changes have the effect of making software more composable and modular but have introduced new risks.  Software is no longer a monolithic block of code secured behind the firewall.  Software today is dynamically composed from API calls and modules that are called from a number of first- and third-party endpoints.   All these interconnects also create more risk vectors.

According to Gartner, spending on risk management and information security will reach over $170 Billion in 2022, up from $140 Billion just a couple years ago.


Feedzai: The Leading RiskOps Platform

Omega Venture Partners’ portfolio company, Feedzai, has established a market-leading position in applying machine learning to mitigate the risk of fraud across the banking and commerce sectors. They are the world’s first RiskOps platform that encompasses fraud, money laundering, compliance, and risk policies for their clients.

They offer a comprehensive platform to detect, prevent, and remediate fraud, and their new Feedzai Solutions package assesses risk for single and cross-channel transactions in real-time. In addition, Feedzai incorporates Cloud-based solutions within their enterprise portfolio allowing flexibility to transform and exceed customer expectations.

Feedzai’s suite entrusts their customers with a financial intelligence network, assesses risk accurately, amends risk with a case manager, and controls risk with better analytics and reporting.

Rather than relying on multiple tools for each individual customer touchpoint, their all-in-one fraud prevention tool ensures that teams can manage risk exposure across the entire customer journey. Having a unified platform also allows for a straightforward maintenance, upgrades, data integrations, and orchestrations.


Bottom Line

AI fraud detection that combines supervised and unsupervised machine learning enables digital businesses to quickly and accurately detect automated and nuanced fraud attempts. Unifying fraud detection, prevention, and mediation services onto one platform saves businesses money and time. This allows businesses to place their focus on growing their business, while improving customer experience and retention.



Special thanks to Angela Chong and Jenna Malone for the help on this post.