The Importance of Stochastic Modelling in Litigation Finance
Single-case litigation finance — where a fund deploys all of its capital into one or two large claims — carries a risk profile that is fundamentally different from portfolio-based litigation finance. The distinction matters enormously for investors, for it is the difference between a binary bet and a managed allocation.
In any individual case, the outcome is uncertain. Even a claim assessed at 80% probability of success will fail one time in five. If a fund is concentrated in a single case, that 20% scenario is catastrophic: not merely a below-average return, but a total loss of principal. This is the core argument for portfolio construction in litigation finance, and it is why stochastic modelling has become central to how institutional-grade funds manage risk.
Stochastic modelling in litigation finance works by running thousands of simulated portfolio outcomes across a range of assumptions for each case — varying the probability of success, the expected recovery, and the timeline to resolution. By running 10,000 or more simulations, the fund builds a probability distribution of total portfolio returns, showing not just the expected outcome but the full range from worst-case to best-case scenario.
The key insight from this approach is the power of low correlation. Unlike a portfolio of equities, where stocks tend to move together in a downturn, litigation outcomes are largely independent of each other and of macroeconomic conditions. A commercial dispute over a construction contract in Mumbai is not materially affected by whether the Sensex rises or falls. This structural independence is what allows a diversified litigation portfolio to generate stable, predictable returns even in volatile market conditions — making the asset class genuinely uncorrelated to traditional investment portfolios.
At Five Rivers Capital, we use stochastic portfolio models to set internal concentration limits, to assess the marginal impact of each new case on the overall portfolio risk profile, and to stress-test the fund's return distribution against scenarios where multiple cases are lost simultaneously. Our target is a portfolio where no single case contributes more than a specified percentage of total expected return, and where the worst-case 95th percentile outcome remains consistent with our downside protection commitments to investors.
The application of rigorous quantitative methods to what has traditionally been a judgment-driven field is, we believe, one of the most important advances in litigation finance practice — and one of the clearest differentiators between institutional-grade funds and smaller, undiversified operators.
