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Tamas Kadar is the CEO and cofounder of <\/em>SEON<\/em><\/a>.<\/em><\/p>\n The rise of instant payment systems has brought as many risks as rewards. <\/p>\n Funds move in seconds, but fraud tactics like authorized push payment<\/a> (APP) fraud and account takeovers<\/a> (ATOs) play out in seconds, often faster than organizations can react. Synthetic identity theft compounds the problem, with fraudsters creating bogus personas to open mule accounts.<\/p>\n Generative AI makes the threat environment even more volatile. Attackers use deepfakes, automated phishing tools and AI-powered voice cloning to scale social engineering. The U.S. experienced a 9% increase in reported identity theft incidents in 2024, with credit card fraud leading the surge. The FBI also found that global cybercrime costs exceeded $16 billion that year<\/a>, a one-third jump from the prior year\u200b.<\/p>\n Since fraud unfolds in real time, even the smallest delays in batch-based review cycles can cause risk teams to miss the threat. Consider a mid-sized digital bank that discovers a coordinated ATO campaign only after dozens of customers report unauthorized transfers. By the time the batch review cycle flags the anomaly, the funds are gone.<\/p>\n By ingesting data as events occur with real-time risk management, risk teams can evaluate signals as they appear. This can help organizations enable instant\u2014even preemptive\u2014action, but it also requires an architectural shift in fraud management.<\/p>\n Batch processing retains utility for post-settlement analysis, performance reporting and model training. <\/p>\n But it often provides insight too late in several places where risk emerges: at sign-up, during payments and at account touchpoints. When a fraudster submits a synthetic identity application, a batch-based system might not flag the suspicious pattern until long after a line of credit has been extended. \u200b<\/p>\n \u200bSeveral trends are emerging in real-time risk management, such as artificial intelligence (AI) and machine learning (ML), to automate risk assessments across high transaction volumes.\u200b<\/p>\n Implementing these technologies means building systems from the ground up. Four architectural requirements form the foundation, each driven by a distinct operational challenge:<\/p>\n \u2022 Scalable data pipelines<\/strong> address the most basic problem: volume unpredictability. Transaction spikes during product launches, seasonal peaks or promotional events can overwhelm fixed-capacity systems and introduce the latency that fraudsters exploit. Pipelines that flex automatically maintain performance under load. The key consideration is how to migrate from fixed infrastructure without disrupting production traffic. Phased rollouts with parallel processing typically reduce that risk.\u200b<\/p>\n \u2022 Event-driven architectures<\/strong> solve the timing problem. Batch systems evaluate risk after the fact. Event-driven systems trigger analysis the moment a critical action occurs, such as a payment initiation or log-in attempt. Implementing them requires rethinking how internal systems communicate, moving from scheduled jobs to publish-subscribe models where each event generates an immediate downstream response.\u200b<\/p>\n \u2022 Streaming analytics<\/strong> power the system’s real-time intelligence, parsing transaction patterns and user behavior continuously for outlier detection. The challenge here is maintaining analytical accuracy at speed. Models that perform well on static datasets can degrade when processing high-velocity streams unless they are purpose-built for that context.\u200b<\/p>\n \u2022 Configurable rules and adaptive thresholds <\/strong>provide sector and scenario specificity. Fraud patterns differ across industries, geographies and customer segments. Static thresholds generate excessive false positives in one context and miss genuine threats in another. Configurable systems can allow organizations to adjust controls without engineering cycles, but they require clear governance to prevent rule sprawl and conflicting logic.\u200b<\/p>\n<\/section>\n \u200bTo realize the benefits of real-time risk operations, organizations should define success metrics before the transition begins.\u200b To understand the overall effectiveness, organizations can track gross fraud loss rate as a percentage of transaction volume and measure the change quarter over quarter after deployment.\u200b\u200b<\/p>\n Then, with \u200bAI-enabled scoring<\/u><\/a>, a key aspect of real-time fraud prevention, organizations can reduce false positives, which means fewer legitimate transactions blocked and less revenue lost to over-caution. The key metric here is the false positive ratio: the number of legitimate transactions flagged per confirmed fraud case. A declining ratio with stable or improved fraud catch rates signals that the model is working.\u200b<\/p>\n Explainable AI creates clear audit trails. Compliance teams can measure success by tracking the time required to produce regulatory reports and the rate of examiner findings or remediation requests.\u200b<\/p>\n Similarly, automated decisioning allows analysts to focus on the most complex cases rather than routine review. Analyst caseload reduction and average investigation time are direct indicators. <\/p>\n Organizations that measure these consistently can quantify the operational ROI of real-time systems within two to three quarters.\u200b<\/p>\n<\/section>\n Real-time decisioning requires investment in infrastructure and expertise. Organizations must support always-on, low-latency systems and remain agile as regulation reshapes the permissible uses of evolving AI models.\u200b<\/p>\n The most resilient companies invest in people and process alongside technology, building modular systems that integrate without extensive custom engineering, prioritizing transparency so scoring outcomes can be tuned and understood and designing for compliance from the start with audit trails and logic controls. <\/p>\n Strong programs develop cross-functional feedback loops to learn from incidents and rapidly update systems and policies.\u200b<\/p>\n<\/section>\n AI is transforming both the attack surface and the defensive playbook. Generative models have lowered the barrier for launching sophisticated fraud, enabling even low-skill actors to execute complex schemes at scale. Advanced defenders are using AI not just to detect threats but to anticipate them.\u200b<\/p>\n For leaders, this demands a mindset shift: Fraud prevention can no longer function as a reactive, compliance-driven cost center. It must become a strategic capability embedded across product, engineering and customer experience decisions from day one.\u200b<\/p>\n The future of fraud prevention lies at the edge, in systems that evaluate risk in real time as users interact. However, technology alone is insufficient without organizational alignment, so leaders must build a culture where cross-functional teams treat every fraud incident as a signal and every model update as a competitive advantage.\u200b\u200b<\/p>\n Forbes Technology Council<\/u><\/a> is an invitation-only community for world-class CIOs, CTOs and technology executives. Do I qualify?<\/u><\/em><\/a><\/p>\nFrom Batch Processing To Real-Time Risk Protection\u200b\u200b<\/h2>\n
What Real-Time Decisioning Looks Like\u200b<\/h2>\n
Navigating Challenges And Setting Best Practices<\/h2>\n
Preparing For The Future Of Risk Management<\/h2>\n
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