Insurance Bad Faith Reservation Parser
1,400,000.00 in disputed policy indemnity reserves creating independent counsel rights across across 3 items.
Insurer reserved right to deny indemnification on Count I workmanship while defending Count IV fraud ($450,000.00). Creates ethical conflict entitling insured to independent counsel.
Method1Cross-referenced reserved policy exclusions against underlying complaint counts.
2Drafted formal demand for independent Cumis defense counsel funded at insurer expense.
| Reference | Description | Disputed_Coverage |
|---|---|---|
| Exclusion (j)(5) | Insurer Created Ethical Conflict of Interest | 450,000.00 |
| Intentional Tort & Fraud Count IV | Punitive Damage Conflict Allocation | 500,000.00 |
| Exclusion (l) | Completed Operations Exclusion Trap | 450,000.00 |
Insurer Reservation of Rights letter text, plus underlying third-party lawsuit complaint text
Independent Cumis defense counsel entitlement brief and coverage conflict matrix (Resolution Dossier, Findings Schedule)
Open Insurance Bad Faith Reservation Parser on sample data now with no sign-up, then run your own files free for 14 days. $3,000 a month after that, cancel any time. 30% goes to the referring partner.
Read in memory for the session, never stored, never used to train a model.
Outputs are computed from your inputs and the tool's rules. Check them before you rely on them.
14 days on your own files, then $3,000 a month.
Open Insurance Bad Faith Reservation Parser on sample data now with no sign-up, then run your own files free for 14 days. $3,000 a month after that, cancel any time.
On a sample file: 1,400,000.00 in disputed policy indemnity reserves creating independent counsel rights across across 3 items.
Why not just use ChatGPT?
Rules that stay current: the rate cards, tariffs, code sets and regulations it checks against are kept up to date for you; a one-off prompt starts from nothing each time. Evidence that stands up: every finding cites the line, the file and the rule, so it holds up with a vendor, a payer or an auditor. The same answer every time: the checks are written rules, not a fresh guess, so this month's result can be compared with last month's. Nobody has to own it: no one inside has to build, test and maintain a home-made tool, and files are not pasted into a public chatbot.
Start on your own files
If you would rather talk it through, write to dev.sathya@baseloom.app; that reaches Dev Sathya, founder.