Underwritegig workerswith confidence.
Monte Carlo simulations capture what flat credit files miss—giving your team the tools to assess gig economy applicants with precision.
Traditional scores see your past.
Not your future.
Traditional credit models assume stable paychecks. Gig workers don't have that luxury.
We simulate
5,000 possible futures.
Each path represents a possible financial trajectory based on real volatility data
Model the Worker
Platform, hours, expenses, savings. We capture the full picture of gig life.
Run the Paths
5,000 simulations accounting for seasonality, macro shocks, and life events.
Assess the Risk
Read default and loss from simulated income paths—not from a static credit score.
We simulate thousands of futures to understand the present. We see what credit scores miss. We believe gig workers deserve better than a number.
SIMULATE
Monte Carlo paths explore thousands of income scenarios—capturing seasonality, market shocks, and the chaos of gig life.
UNDERSTAND
Transform raw volatility into actionable intelligence. Not a score—a probability distribution of financial futures.
EMPOWER
Give lenders the tools to say yes to good borrowers that traditional models would reject. Fairness through simulation.
See it in action.
Select a gig worker persona and watch the simulation reveal their true risk profile.
Select Persona
24-Month Income Projection
Volatile VicCapital One, University of Virginia, JPMorgan Chase Institute, Federal Reserve, Uber, Lyft, DoorDash, Instacart, Gridwise, Bureau of Labor Statistics.
Built by
UVA students.
Adam Carlson
Physics & Math · 3rd Year @ UVA
Minu Choi
Data Science · 3rd Year @ UVA
Otso Karali
Math & Computer Science · 3rd Year @ UVA