$80-$200/hr actuarial and risk modeling work, on your schedule
Review AI-generated pricing, reserving, and risk models the way you'd review work before signing an actuarial opinion. Flag the assumption that won't hold, the reserve that's light, the model that misprices the tail. The judgment behind a signed opinion - what you're professionally accountable for - is what AI labs need on the record.
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Hi, we're Zac and Jack, the founders of Terac. We want to talk to you directly, because you are the most important part of what we're building.
Terac is a community of experts. People who have spent years getting good at something specific and hard. The world is about to need more of you, not less. As AI takes on more of the world's work, the bottleneck shifts to the people who actually know what they're talking about.
Expert labor is the rarest resource in the world right now, and it is shockingly hard to find. The companies that need an actuary's eye on an under-reserved book spend weeks chasing people, paying placement fees, and settling for whoever is available. Meanwhile thousands of qualified people are sitting with knowledge that no one ever asks for.
That gap is what we're here to close. Every project that lands on Terac is routed to the people who actually know the answer, on their schedule, paid fairly, and only when the work is verified. No middleman taking a cut of your time. No vague gigs. No chasing checks.
We care about every single person in this community. If you join Terac, you're not a row in a database to us. We read the feedback. We answer the emails. We will fight for you when a customer is being unreasonable, and we will be honest with you when something on our side is broken. The quality of this panel is our entire company, and we owe you a serious bar.
If you've made it this far, here is what we're asking: claim your profile. Put your expertise on the record. Let the world's most ambitious teams come find you for the work only you can do.
Actuarial questions
Still curious? Write to us at support@terac.com.
Pension and retirement actuarial work is genuinely useful, particularly for tasks involving funding method explanations, actuarial equivalence calculations, and PBGC premium logic. That said, the broadest current demand is in P&C reserving and life/health pricing, so you may see fewer matching tasks if you work only in the pension space. You can note your specialty during onboarding and the system will route you to tasks where your credential and domain knowledge are directly applicable.
No. Every task is self-contained and uses either synthetic data, publicly available industry data (for example, NAIC Schedule P triangles or published SOA tables), or hypothetical scenarios constructed for the evaluation. You are never asked to bring in, reference, or validate anything from your employer's systems or filed materials. The Actuarial Standards of Practice and your employer's confidentiality obligations are not implicated by the work.
Tasks vary, but common formats include AI-drafted actuarial memoranda explaining reserve methodology, step-by-step chain-ladder or Bornhuetter-Ferguson development walkthroughs, narrative summaries of mortality or morbidity assumptions, and written responses to hypothetical regulator or auditor questions. You assess whether the reasoning is technically sound, whether the model applies the correct ASOP (for example, ASOP No. 25 for credibility, ASOP No. 43 for P&C loss reserves), and whether the stated conclusions follow from the stated assumptions.
Yes, non-traditional roles are valuable precisely because frontier models are increasingly being tested on the intersection of actuarial judgment and quantitative methods like GLMs, catastrophe model interpretation, and predictive analytics. Your CAS credential is the core qualification; the specific job title matters less than your ability to evaluate whether actuarial reasoning is technically defensible. Tasks in exposure rating, reinsurance pricing, or model validation are a natural fit for your background.
The work is evaluative and educational, not the provision of actuarial services to a principal in the sense covered by the Code of Professional Conduct. You are not signing opinions, certifying reserves, or advising a client - you are assessing whether an AI's explanation of actuarial concepts is accurate and well-reasoned. If a specific task ever asked you to produce a signed actuarial opinion or a document that could be used as a formal actuarial communication, that would be out of scope and you should decline it; but no such tasks are assigned through this platform.
Why your expertise matters
Actuarial work sits at the intersection of probability theory, long-tailed risk, and regulatory accountability, making it one of the hardest domains for a model to get consistently right. When a model reasons about mortality tables, reserve adequacy, or loss development patterns, the failure modes are subtle: plausible-sounding answers that violate SOA/CAS standards, misapply credibility weighting, or ignore jurisdictional constraints in ways that a non-actuary reviewer would never catch. Your judgment on whether a model's reserve recommendation would pass a state insurance department review or withstand peer review by a Fellow is exactly the signal labs need to close that gap.
How pay works
Compensation moves toward $200/hr for credentialed Fellows (FSA or FCAS) with hands-on experience in complex lines - structured settlements, long-tailed liability, life reinsurance pricing, or principle-based reserving under VM-20. All work is remote and asynchronous; you pick up tasks on your own schedule and are paid only after Terac verifies the work meets quality standards, with no lock-in or minimum hours.
What the work looks like
A sample of the actuarial and risk modeling work you would pick up. Every project is scoped, remote, and paid on verified completion.
- Evaluate a model-generated loss reserve report for a workers' compensation book, flagging where the selected development factors deviate from actuarial standards without adequate justification.
- Review an AI-produced explanation of the Bornhuetter-Ferguson method and correct any misstatements about how prior loss ratios are selected and blended with emerging experience.
- Annotate a model's attempt at a mortality improvement scale for a pension valuation, identifying where it conflates MP-2021 projection factors with static period tables.
- Create a worked example of a credibility-weighted rate indication for a small commercial auto book, showing the reasoning an actuary uses when observed data is thin.
- Assess whether a model-drafted actuarial opinion paragraph on loss reserves meets the disclosure requirements under ASOP No. 36 and the relevant SAO standards.
- Stress-test a model's catastrophe reinsurance pricing output by identifying scenarios where the layer attachment probability is inconsistent with the cited return period assumptions.
Specialties we match
Actuarial projects span a wide range of focus areas. Tell us where you go deep and we route the work that fits.
- Reserving (loss development, Bornhuetter-Ferguson)
- Pricing and rate adequacy analysis
- Mortality and morbidity modeling
- Credibility theory (Buhlmann, EBCT)
- Principle-Based Reserving (VM-20, VM-22)
- Stochastic modeling (ESG, scenario testing)
- Catastrophe modeling (RMS, AIR)
- Experience studies (lapse, mortality, morbidity)
- Regulatory filings (NAIC, state DOI)
- Embedded value and EV reporting
- Reinsurance structures and treaty analysis
- Excel/VBA, R, Python actuarial workflows
