New data from a report published by Fortune Business Insights underscores the need for financial institution leaders and fraud managers to align their digital transformation and fraud detection and prevention goals. This report specifically highlights how today's fraud trends have spurred faster adoption of technologies that rely on big data and predictive analytics to detect fraud.
An increased demand for solutions that rely on predictive analytics to detect fraud that's occurring and prevent potential fraud through automated pattern analysis, has paved the way for financial institutions to proactively protect their customers. The application of AI and machine learning has given fraud managers the necessary tools to prevent payment fraud before it occurs, and reduce fraud losses and associated costs when they do occur.
The rise in online fraud on a global scale has caused Fortune Business Insights to project the global fraud detection and prevention market to grow to $110.04 billion by 2026, a 25% growth in a 6-year period. The report specifically notes why machine learning and AI-based fraud detection in the banking sector is fueling the fraud detection and prevention market growth. The report underpins one key obstacle that's holding many financial institutions back: "Limited Data Visibility Often Produces False Positives Outcomes."
"For fraud detection and prevention tools to work effectively and efficiently, they should be able to deal with surprising and unknown fraud occurrences. Fraud detection and prevention solutions should have the ability to provide a versatile mix of features to collect and analyze the data, produce correct conclusions, take actions based on results, and finally produce a comprehensive result. Moreover, these solutions should be able to integrate with the existing ecosystem," the report notes. " However, not every solution in the market lives up to this standard, thereby impacting data visibility."
Cloud-based fraud detection and prevention solutions were specifically noted as a major driver in the market, particularly as they relate to tracking online transactions and preventing fraud associated with data breaches. The report also calls out AI and machine learning as lucrative solutions impacting the market growth, driven in large part by "high-performance analytics" to mitigate fraud.
Rising fraud costs and attempts at lowering false positives has caused many financial institutions to adopt machine learning-based fraud detection solutions that rely on proactive and predictive technologies that can pinpoint fraud trends faster and more accurately. In turn, this has led to financial institutions having the fraud analytics tools necessary to achieve their fraud loss and FPR goals. Through the application of high-performance software, machine learning technology has created advanced computing abilities that have a broad-scale reach for financial institutions that allow them to better compete against the bigger banks, and have a better grasp on how they are managing their fraud risk exposure.
Adopting enhanced fraud detection and prevention technologies associated with AI and machine learning gives financial institution leaders and fraud managers a fighting chance against evolving fraud risks that are often hidden until a fraudulent transaction has occurred. Machine learning tools help fraud manager quickly spot and mitigate these risks before they spread.
The growing impact of these emerging risks is why you're seeing the fraud detection and prevention market so heavily influenced by technologies that rely on automated, predictive fraud model scoring that help fraud managers do their jobs more effectively. For far too long, fraud managers have lacked access to enough data or tools that help them alleviate ineffective manual pattern analysis that bogs down their resources and budget — all without producing the desired results.
Based on the current trajectory of the fraud detection and prevention market, it's clear that financial institution leaders and fraud and risk managers will continue adopting machine learning and AI-driven fraud analytics technologies at a steady pace. The desire to stay relevant and competitive has driven financial institution leader's decisions to embrace digital transformation for their customer-facing applications. These same trends are being seen on the back-office operations. The desire of fraud and risk managers and executives to protect their customers and hit their fraud budget goals is what's driving greater adoption of fraud detection and prevention tools that rely on automated and predictive technologies.
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