Ledger Library

Designed for insurers, MGAs, fronting carriers, reinsurers, and institutional investors, our 2026 mid-year report highlights the key trends shaping pricing, capacity, transaction a...

Ledger gives investors exposure to the full cash flow engine of casualty insurance-upfront premiums, long-tail claim payouts, investment income, and low-volatility risks-by structu...

We rely on time series models for estimating the loss ratios insurers will achieve in future years. Our choice of models is not arbitrary or purely driven by backtesting performanc...

When an insurance company issues a policy, the company knows the amount of premium it will receive, but it may not know for many years exactly how much it will cost to settle all o...

Many traditional loss development models center around the notion of link ratios. We describe the importance of prior distributions in the context of Bayesian modeling, and some of...

In this paper, we lay out our Bayesian workflow for securitizing casualty insurance-linked securities that uses: (1) theoretically informed time-series and state-space models to ca...

Loss development tail factors are difficult to estimate, due to training data that is typically quite sparse and volatile. We propose a new estimation technique that is well-grounde...