Case & Portfolio Risk
Litigation valuation
without guessing
Case valuation is subjective. Plaintiff/defendant win probability is uncertain. Appeals timelines are vague. SimOracle swarms model judicial decisions, jury behavior, legal precedent, and settlement dynamics. Know the probability of winning before you fund a case.
The Problem: Case Valuation Uncertainty
Gut-Driven Decisions
- ✗Case evaluation based on attorney opinion + comparable cases. But every case is unique.
- ✗You fund a case. Jury verdict is lower than expected. Your ROI is crushed.
- ✗Appeals kill your timeline. You didn't model appellate court likelihood or delays.
- ✗Portfolio risk is opaque. You have 20 funded cases. How many are at risk? Correlation?
Data-Driven Case Prediction
- ✓Swarms of agents model judge behavior, jury composition, opposing counsel strategy, legal precedent.
- ✓Plaintiff win probability with confidence scores. Expected settlement range. Appeals risk.
- ✓Portfolio risk modeling: correlated loss scenarios, aggregate settlement distribution.
- ✓Funding decision backed by probability, not opinion.
Case Valuation Simulations
Plaintiff Win Probability
Challenge
Is this plaintiff case worth funding? What's the realistic win probability? At what valuation?
Simulation Logic
Swarms of agents model: judge history (cases of this type, decision bias), jury pool composition (demographic correlation to outcomes), opposing counsel history, legal precedent strength, witness credibility factors. Simulate trial outcome distribution.
Your Funding Decision
Plaintiff win probability. Confidence score. Expected damages range. Settlement probability.
Example Metrics
Plaintiff win: 62% (±8% confidence). Expected damages: $2.5M (range: $1.5M-$4M). Settlement probability: 75% (avg: $1.8M). Appeals risk: 35%.
Settlement Negotiation Timeline
Challenge
How long will settlement take? Will it settle before trial? What's the discount from full claim?
Simulation Logic
Swarms model opposing party behavior: financial stress, legal bill accumulation, risk tolerance, counsel stability. Settlement dynamics based on judicial pressure, timeline, discovery phase.
Your Funding Decision
Settlement probability by timeline. Discount from full claim. Trial cost comparison.
Example Metrics
90-day settlement: 45% probability (avg: $2.1M). 180-day: 65% probability (avg: $1.9M). Trial path: 35% probability (expected recovery: $2.3M after appeals/costs).
Appellate Outcome & Cost
Challenge
If we win trial, what's the appeal risk? How long does it take? How much does it cost?
Simulation Logic
Swarms model appellate court history, legal precedent appeal success rates, appellate cost dynamics, timeline with retrial risk.
Your Funding Decision
Appeal probability if trial win. Reversal risk. Timeline. Cost impact on net recovery.
Example Metrics
Appeal probability (if win): 40%. Reversal probability: 18%. Avg appeal timeline: 2-3 years. Appeal costs: $150-300K. Net impact: $300-500K reduction in expected recovery.
Defendant Solvency & Collection
Challenge
We win the case. But can we collect? Is the defendant solvent? What's collection probability?
Simulation Logic
Swarms model defendant financial health, asset position, bankruptcy risk, collection dynamics, enforcement timeline.
Your Funding Decision
Collection probability by timeline. Expected collection rate on judgment. Bankruptcy risk.
Example Metrics
Collection (12 months): 75% of judgment. Collection (24 months): 85%. Bankruptcy risk: 12%. Average collection time: 18 months.
Portfolio Risk Management
Portfolio Correlation
Your 20 funded cases aren't independent. If one fails (jury verdict surprise), others might too (similar judge, jury pool, defendant profile). SimOracle quantifies correlation.
Identify correlated cases. Hedge high-correlation clusters. Balance portfolio risk.
Portfolio Return Distribution
Monte Carlo simulation across all funded cases. What's your expected portfolio return? Downside risk? Probability of hitting fund targets?
Optimize case selection to maximize risk-adjusted returns. Fund cases that improve Sharpe ratio.
Real Case Study
Fund: $50M litigation finance fund, 15 funded cases
Portfolio Win Rate: Assumed 60% (gut). Actual: 58% (modeled).
Impact: 3% lower expected return than budgeted. 2 cases had > 50% correlation with similar risk profiles.
Action: Decline 1 correlated case. Fund 1 uncorrelated case. Expected return: +2% vs baseline.
Underwriting Automation
Automated case evaluation pipeline. Submit case details. Get probability scoring within hours. Consistent underwriting across deal flow.
Speed up funding decisions. Reduce underwriting variance.
For Litigation Finance: Institutional Suite
Institutional Suite
$5,000/month
- ✓Case valuation API: plaintiff win prob, settlement range, appeals risk, collection probability
- ✓Portfolio risk modeling: correlation analysis, return distribution, Sharpe ratio optimization
- ✓Backtesting: 2+ years of historical case data and actual outcomes for calibration
- ✓Custom alert rules: cases that breach probability thresholds or correlation alerts
Competitive Advantage
- • Fund better cases: data-driven not opinion-driven
- • Faster underwriting: case scoring in hours, not weeks
- • Better portfolio returns: optimize correlation and risk
- • Reduced drawdowns: catch risky cases before funding
Fund cases with probability, not opinion.
See case valuations with confidence scoring. Model portfolio risk. Optimize for Sharpe ratio. No more guessing on jury verdicts.