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Mulberri Launches AI-Powered Risk Engine for Workers Compensation Underwriters Organizations
Mulberri, an AI-driven embedded insurance platform, today announced the launch of its Risk Engine, a first of its kind risk assessment offering for workers compensation underwriters. The Risk Engine, which is already being deployed by firms like PayChex, uses machine learning models to put the information underwriters need at their fingertips, making it possible to make fast, accurate decisions.
Insurers and underwriters face mounting pressure in their daily work, including an increased volume of applications, difficulties accessing the right data, pressure to minimize losses, and growing competition. Workers’ comp is a multi-billion-dollar market, but these policies are written using manual workflows, archaic processes, and low-tech solutions — leading to friction as well as poor underwriting outcomes. Mulberri designed its Risk Engine to meet the needs of underwriters and their customers, extending Mulberri’s existing portfolio of insurance products for payroll businesses, HR providers, brokers and small-medium businesses.
“Our mission from day one has been to leverage technology to complement underwriters’ judgment so that the business insurance process can be simple, efficient, and transparent,” said Hamesh Chawla, CEO and cofounder of Mulberri. “The Risk Engine is a transformative step forward.”
Mulberri trained its Risk Engine to determine factors that impact claims based on millions of pieces of information including firmographic information, previous loss experience and workers compensation information. The cloud-based product allows intuitive access to predictions on demand from any SaaS application as well as easy deployment. It also enables users to analyze and score prospects one at a time or in bulk. All data is obfuscated, so PII remains safe.
It allows underwriters to make predictions for:
- Claim Propensity—Likelihood of an insured filing a claim in twelve months
- Claim Probability—Likelihood of an insured filing multiple claims in twelve months
- Claim Frequency—Claim repetition in twelve months
- Claim Severity—Severity of the claim should it occur
- Loss Ratio—Expected loss ratio for a given premium
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