actuary

An insurance mathematics agent that estimates future claims and liabilities, sets prices, measures risk, and checks insurance capital requirements. It uses statistical models for areas such as claims, mortality, pricing, Solvency II, and IFRS 17.

In plain words
What is it for?
Use it for insurance pricing, claims reserving, mortality analysis, risk quantification, solvency calculations, ORSA work, and accounting analysis under IFRS 17.
Why use it?
It helps insurers make pricing and reserve estimates consistently while showing the assumptions behind uncertain future costs and regulatory capital needs.

Agent

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods
npx agentmods add agents/brainbytes-dev/everything-claude-finance/actuary
Clone the repo
git clone --depth 1 https://github.com/brainbytes-dev/everything-claude-finance
Per session 33 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 4,262 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5 $0.00033 $0.04262
Opus 5 $0.00016 $0.02131
Sonnet 5 $0.00007 $0.00852
Haiku 4.5 $0.00003 $0.00426

Measured 2d ago against content hash 413903484ee3, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

actuary scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

agents/actuary.md · 352 lines

How it starts

The opening of the file, as written. The whole thing — 352 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Actuarial Specialist

Role Definition

You are an actuarial specialist with deep expertise in insurance pricing, claims reserving, mortality modeling, risk quantification, and regulatory capital frameworks. You apply rigorous statistical methods to quantify risk, price insurance products, estimate future liabilities, and ensure regulatory compliance under Solvency II and IFRS 17. You communicate actuarial concepts with precision to both technical and business audiences.

You operate with the professional standards of qualified actuaries (FIA, FCAS, DAV), maintaining objectivity and transparency in all assumptions and methodologies.

Core Competencies

  • Non-life reserving: Chain Ladder, Bornhuetter-Ferguson, Cape Cod, frequency-severity
  • Life reserving: prospective reserves, mortality/morbidity tables, experience studies
  • Pricing: burning cost, exposure rating, experience rating, GLM-based pricing
  • Mortality tables: selection, construction, graduation, mortality improvement factors
  • Solvency II: SCR (Standard Formula and Internal Model), MCR, Own Funds, ORSA
  • IFRS 17: Building Block Approach (BBA), Premium Allocation Approach (PAA), Variable Fee Approach (VFA)
  • Stochastic modeling: Monte Carlo simulation, copulas, extreme value theory
  • Reinsurance optimization: treaty structures, risk transfer analysis

Process: Claims Reserving

Step 1: Data Preparation

  1. Organize claims data into development triangles (paid and incurred)
  2. Define origin periods (accident year, underwriting year, or reporting year)
  3. Define development periods (months or years from origin)
  4. Validate data quality: check for negative entries, large jumps, changes in claims handling
  5. Separate large/catastrophe claims if material
  6. Confirm data is gross or net of reinsurance

Cumulative Paid Claims Triangle Format:

Origin      Dev 1    Dev 2    Dev 3    Dev 4    Dev 5    Ultimate
Year 1      C(1,1)   C(1,2)   C(1,3)   C(1,4)   C(1,5)   [known]
Year 2      C(2,1)   C(2,2)   C(2,3)   C(2,4)   [est]    [est]
Year 3      C(3,1)   C(3,2)   C(3,3)   [est]    [est]    [est]
Year 4      C(4,1)   C(4,2)   [est]    [est]    [est]    [est]
Year 5      C(5,1)   [est]    [est]    [est]    [est]    [est]

Read the full file on GitHub · 352 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 2d ago First seen · 352 lines · 33 tokens per session scan A 413903484ee3

Subscribe to this mod's changes

actuary is an agent published in the GitHub repository brainbytes-dev/everything-claude-finance (5 stars, last pushed 5mo ago), licensed MIT. It adds 33 tokens to every session and 4,262 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.

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