Over 50% of fraud now involves AI. FIDZY surveyed 562 fraud professionals globally and found AI-powered fraud has become the norm, not the exception. We're talking about deepfakes, synthetic identities, and AI-powered phishing so sophisticated it's basically indistinguishable from legitimate communications. The counter punch? 90% of banks are now using AI to fight back—fighting fire with fire.

Sam and Mac paint the threat landscape: deepfake calls that sound exactly like your bank's fraud department, using your bank's actual spoofed phone number, with perfect voice and professional script asking for your PIN. California bank customers received dozens of these calls and many fell for it because the technology is that convincing.

This is an arms race. Fraudsters use AI, banks use AI—there's no final victory. As bank AI gets smarter at detection, fraud AI evolves to evade those systems. It's like computer viruses and antivirus software—never-ending evolution and counter-evolution. The economic stakes are enormous: Deloitte estimates US banking losses from fraud could increase from $12.3 billion in 2023 to $40 billion by 2027, more than tripling in four years due to generative AI sophistication.

Human oversight remains essential. 88% of banking professionals say human oversight is non-negotiable. AI identifies potential issues and surfaces them to analysts, but humans make final calls on complex cases. The benefit: 43% of institutions report increased efficiency because AI handles high-volume straightforward cases, freeing human experts for complex nuanced cases requiring judgment.

Stanford just dropped a bombshell study: an AI analyst made 30 years of stock picks and outperformed 93% of human mutual fund managers by an average of 600 basis points—that's 6% annually. This is absolutely massive in the investment world, kicking off Inside AssembleAI's AI in Finance series with the technology that's shaking Wall Street.

Here's what's fascinating: the AI mostly used simple variables, not the sophisticated ones everyone expected. Firm size and dollar trading volume were dominant factors, but it used complex AI techniques to squeeze maximum predictive value from simple data everyone can access. The insight isn't about finding hidden data-it's about extracting more signal from obvious data. Any investment firm could have had this data in the pre-AI era, but it was simply too costly to justify economically.

Sam and Mac explore three main approaches institutions use today: pattern recognition for known scenarios (AI learns what fraud or manipulation looks like), anomaly detection for unknown threats (establishing what's normal and alerting on deviations), and predictive analytics for future behavior (forecasting what's likely to happen next). All happening in real time, in milliseconds-the game changer compared to legacy systems.

The data quality issue compounds everything—garbage in, garbage out. Models require at least five years of high-quality historical data for reliable results, and even then, past performance doesn't guarantee future success. Looking ahead to 2026, expect more hedge funds adopting sophisticated AI systems, models incorporating multi-modal data like satellite imagery and social sentiment, intensifying regulatory scrutiny, and continued democratization as retail investors gain access to tools that were hedge fund exclusive just years ago.