SRT
Intelligence
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Capital Relief Analytics Platform

Identify SRT candidates
before the market does

Systematic monitoring of 52 global banks across US and Europe. AI-powered capital analysis surfaces high-probability Significant Risk Transfer issuers weeks before deals are publicly announced.

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52 banks · FDIC + ECB data · Updated daily

52
Banks Monitored
24
US Institutions
28
European Banks
8Q
Historical Data

Three core capabilities

Built specifically for alternative credit investors sourcing SRT transactions in the primary market.

FDIC + ECB Data

Bank Universe Monitoring

Track 52 major global banks across US and Europe. Monitor CET1 ratios, RWA growth, loan book composition, and leverage ratios — updated quarterly from FDIC and ECB regulatory filings.

Live Scoring

AI-Powered Scoring

Each bank receives a composite SRT likelihood score (0–100) driven by capital pressure signals, loan book growth, historical SRT behavior, and macro regulatory context. Instantly sortable and filterable.

Llama 3.3 70B

Investment Memo Generation

Click any bank to generate a structured AI investment memo. Llama 3.3 70B analyzes the bank's capital position, identifies the most likely asset class to transfer, estimates deal size, and recommends outreach timing.

How it works

From raw regulatory data to actionable sourcing intelligence

1

Monitor

Daily cron jobs ingest FDIC Call Reports for US banks and ECB filings for European banks. Key capital metrics — CET1 ratios, RWA, loan composition — are stored and updated each quarter.

2

Score

The scoring engine evaluates each bank across four weighted dimensions: capital pressure (40%), loan book growth (30%), SRT history (20%), and macro regulatory context (10%).

3

Analyze

Generate AI investment memos on demand. Llama 3.3 calls four data tools then writes a structured thesis covering asset class, deal sizing, outreach timing, and key risks.

Ready to start sourcing?

Access the full bank universe, score rankings, and AI memo generation from the dashboard.

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SRT Intelligence v0.1-beta