Korra launched Deal Analysis on October 21, 2026, an application designed to help market participants analyze casualty, non-catastrophe property, and specialty reinsurance and insurance-linked securities opportunities using stochastic models and AI-driven data extraction.
Property and casualty reinsurance and insurance-linked securities platform Korra announced the rollout of Korra Deal Analysis, an application built to help ceding reinsurers, managing general agents, fronting carriers, and reinsurance brokers structure, price, and publish formal risk and investment reports. According to the company, the tool brings together loss modeling, deal structure, collateral requirements, capital structure, and investor returns into a single transparent modeling environment.
Evaluating Risk Through Economic Metrics
On the capital side of transactions, collateralized reinsurers and ILS investors can utilize the platform’s analytical framework to evaluate reinsurance submissions on economic terms such as internal rate of return (IRR) and multiple on invested capital (MOIC), moving away from reliance solely on traditional accounting metrics like combined ratio. Kyle Safran, Head of Data Science at Korra, explained that structuring an ILS transaction requires a comprehensive view of loss development and deal economics.
Structuring and evaluating an ILS transaction requires understanding not only what losses may look like, but how they develop over time, flow through the deal structure, and affect each party’s economics.
Kyle Safran, Head of Data Science at Korra
Safran added that the application combines loss modeling, reinsurance structure, collateral requirements, capital structure, and investor returns in an environment where brokers and capital providers can test assumptions and compare alternative structures.
AI Extraction and Bayesian Loss Models
The application uses artificial intelligence to extract key information and historical loss triangle data from incoming reinsurance submissions. Users can view historical performance through a proprietary graphics library and tap into Korra’s database of industry performance to compare and blend datasets.
To model prospective losses, teams can employ traditional actuarial techniques or Korra’s hierarchical Bayesian models, which generate full probability distributions of future loss ratios. These Bayesian models rely on published, peer-reviewed methodologies and incorporate industry priors to handle thin historical data frequently provided by managing general agents. Following the loss modeling phase, users can test alternative transaction structures and forecast investor metrics before packaging results into investor-ready reports.
Industry Perspective and Portfolio Monitoring
Market observers note that alternative capital accounts for a growing share of reinsurance, increasing the need for sophisticated evaluation tools. Meredith Barnes-Cook, Senior Principal at Datos Insights, stated that platforms combining actuarial modeling, deal structuring, and investor reporting into one environment make these transactions easier to assess, compare, and explain.
Once a transaction goes live, evaluation data is carried forward in the Korra platform to support ongoing portfolio performance monitoring, continuous revaluation, and renewal decisions as claims develop. Umair Rasool, General Manager of Korra, pointed out that risk originators and capital providers frequently look at identical underlying risk through separate perspectives.
Risk originators and capital providers are ultimately looking at the same underlying risk from different perspectives, yet much of the analysis still happens through disconnected spreadsheets, static reports and manual workflows.
Umair Rasool, General Manager of Korra
Rasool noted that Deal Analysis offers a transparent and repeatable method to model risk, evaluate transaction economics, and align on deal terms by consolidating workflows onto a single platform.
