How Waterfall Asset Management Uses AI to Crack Structured Finance Tapes in Hours | Prophecy + WAM

Prophecy - AI Data Prep & Analysis
161 views June 15, 2026

Waterfall Asset Management — an alternative asset manager with $20B+ AUM across structured credit, ABS, and commercial real estate — used to rely on specialist analysts to manually "crack" data tapes before every investment decision. That process was slow, siloed, and a bottleneck. In this webinar, Maciej Szpakowski (Co-founder & CTO, Prophecy) and Shehzad Nabi (CTO, Waterfall Asset Management) walk through how Waterfall now uses Prophecy's AI-powered data platform, built on specialized Claude Code agents, to automate tape cracking, perform on-the-fly strats, handle unstructured documents, and run ongoing portfolio surveillance on Databricks. What you'll learn: → What "tape cracking" is and why it's the critical first step in structured finance workflows → Why generic AI tools (ChatGPT, off-the-shelf LLMs) fall short for proprietary financial data → How Prophecy's confidence-scored, visual mapping approach lets analysts validate AI work without writing code → How the system learns from corrections — reaching 95%+ accuracy by tape five → Real results from Waterfall: faster deal evaluation, reduced dependency on specialists, and legacy tooling replaced → Live product demo: tape ingestion → AI mapping → strats → unstructured doc analysis → scheduled surveillance Learn more about Prophecy for structured finance: https://prophecy.ai/structured-finance Request a demo today: https://www.prophecy.ai/schedule-a-demo-structured-finance Timestamps 0:00 Introductions 1:40 About Prophecy 2:30 About Waterfall Asset Management 3:20 What is tape cracking? 5:00 The bottleneck problem 6:20 Why generic AI falls short 8:10 Prophecy's approach: confidence-scored AI mapping 10:20 How the system learns over time 13:45 Benchmark results 15:45 Waterfall's outcomes 18:30 The future of AI in structured finance 19:55 Demo overview 20:45 Uploading a raw tape 23:00 Reviewing AI mappings 25:45 Accepting mappings & clean output 27:20 Strats & ad hoc analysis 30:00 Ingesting unstructured documents 32:00 Scheduling surveillance 33:00 Takeaways & close

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