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Two years ago, a $900M auto insurance carrier was facing escalating operational and legal challenges. The primary issue was the overwhelming volume of demand letters — often exceeding 2,000 pages with medical documentation — submitted by legal professionals. Each demand letter triggered a 14–21 day legal response window; failure to meet deadlines risked punitive damages, where a $30,000 claim could escalate into a $15 million liability.
The insurer’s claims and legal departments, managing over 30,000 active claims daily, were inundated with legal documentation per claim. Critical data points — such as demand due dates, claimed injuries, diagnoses, and treatment histories — had to be manually extracted, creating significant workload pressure and operational risk. The owner of the company personally reported that lawsuits were trending sharply upward: 10 in 2023, 100 in 2024, and an estimated 200 projected for 2025.
Industrial AI platformed a solution that automated the reading and summarization of medical and legal documents. After three months development, the platform produced consistent summary reports, dramatically reducing the administrative burden and legal exposure. A year and 10,000+ claims later, the process has reduced expenses by $7.29M and sped the turnaround time up by 150X (from 5 hrs down to 2 mins). Demand due date warnings were issued and this intervention significantly lowered operational costs and prevented high-value lawsuits.
Following this success, the carrier used Industrial AI technology for a second project — focused on fraud detection. The system was configured to analyze discrepancies between injuries reported at the initial claim notice and those reported later by attorneys and medical professionals. Industrial AI's automated comparisons surfaced inconsistencies in real-time, empowering in-house counsel to identify potentially fraudulent claims earlier and more effectively — a capability the carrier had never previously possessed.
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