Founders are consistently and universally wrong about their financial projections, particularly cash runway. AI tools can provide an objective, data-driven forecast based on trailing growth, correcting for inherent founder optimism and preventing critical miscalculations.

The 1863 False Claims Act created a financial incentive to report fraud, but its impact was limited by the difficulty of detection. Modern AI solves this information processing bottleneck, finally allowing companies to act on the law's incentive at a massive scale.

For complex cases like "friendly fraud," traditional ground truth labels are often missing. Stripe uses an LLM to act as a judge, evaluating the quality of AI-generated labels for suspicious payments. This creates a proxy for ground truth, enabling faster model iteration.

During the time crunch of earnings season, AI excels at synthesizing disparate information. It can instantly compare a CEO's positive guidance against the recently reported cash flow statements of multiple competitors, flagging potential overconfidence or a genuine outlier.

While many focus on AI for consumer apps or underwriting, its most significant immediate application has been by fraudsters. AI is driving an 18-20% annual growth in financial fraud by automating scams at an unprecedented scale, making it the most urgent AI-related challenge for the industry.

A defender's key advantage is their massive dataset of legitimate activity. Machine learning excels by modeling the messy, typo-ridden chaos of real business data. Fraudsters, however sophisticated, cannot perfectly replicate this organic "noise," causing their cleaner, fabricated patterns to stand out as anomalies.

The next frontier of data isn't just accessing existing databases, but creating new ones with AI. Companies are analyzing unstructured sources in creative ways—like using computer vision on satellite images to count cars in parking lots as a proxy for employee headcounts—to answer business questions that were previously impossible to solve.

A core conceit of fraud is faking business growth. Consequently, fraudulent enterprises often report growth rates that dwarf even the most successful legitimate companies. For example, the fraudulent 'Feeding Our Future' program claimed a 578% CAGR, more than double Uber's peak growth rate. This makes sorting by growth an effective detection method.

By analyzing satellite photos of data center construction starts and progress, analysts can accurately predict a hyperscaler's future capital expenditures and revenue growth up to a year in advance. This provides a significant information edge well before trends appear in quarterly earnings reports.