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Key Responsibilities and Required Skills for Actuarial Consultant

💰 $80,000 - $140,000

ActuarialConsultingInsuranceRisk

🎯 Role Definition

The Actuarial Consultant partners with insurers, reinsurers, and corporate risk teams to design and deliver pricing, reserving, valuation, capital modeling and risk transfer solutions. You will lead actuarial analyses, build robust models (deterministic and stochastic), translate technical findings into actionable business advice, and support implementation of regulatory frameworks (IFRS 17, Solvency II, local GAAP). This role requires strong technical actuarial skills, hands-on coding and data manipulation, excellent stakeholder management, and experience working in a consulting or client advisory environment.

📈 Career Progression

Typical Career Path

Entry Point From:

  • Actuarial Analyst or Junior Actuary (0–2 years, exam progress)
  • Pricing Analyst or Reserving Analyst with insurance industry experience
  • Data Scientist/Analyst with actuarial-focused projects

Advancement To:

  • Senior Actuarial Consultant / Senior Actuary
  • Actuarial Manager / Engagement Manager (consulting)
  • Principal Actuary / Consulting Principal
  • Head of Pricing, Head of Reserving or Chief Actuary (industry roles)

Lateral Moves:

  • Capital Modeling Specialist / Risk Modeler
  • Product Pricing Lead / Underwriting Analytics Lead
  • Data Science Lead in Insurance

Core Responsibilities

Primary Functions

  • Lead end-to-end pricing engagements: develop risk-adjusted rate plans, build generalized linear models (GLMs) and advanced predictive models using R, Python or SAS, implement credibility and experience rating approaches, and translate model outputs into premium recommendations and product management strategies for both personal and commercial lines.
  • Design, execute and document reserving analyses including loss development triangles, chain-ladder, Bornhuetter-Ferguson, stochastic reserving techniques and IBNR estimation; prepare robust actuarial opinions and recommendations for management and regulatory submissions.
  • Deliver IFRS 17 implementation and ongoing reporting support: map cash flows to contract boundaries, build fulfillment cash flow models, calculate Contractual Service Margin (CSM), ensure appropriate discounting, and prepare documentation for auditors and finance stakeholders.
  • Build and validate capital models and economic capital assessments (internal models, stress testing, scenario analysis) under Solvency II / local RBC frameworks, quantifying diversification benefits and risk concentration.
  • Create and maintain valuation models for year-end actuarial valuations, liaising with finance to reconcile actuarial reserves, provide variance explanations, and support audit and external reporting processes.
  • Perform reinsurance structuring and pricing analyses: model treaty layers, quota-share and excess-of-loss placements, simulate cash flows under multiple recovery scenarios and quantify the impact on capital, volatility and earnings.
  • Conduct profitability and loss ratio analyses by product, segment and distribution channel; identify improvement opportunities through rate changes, underwriting criteria updates or reinsurance optimisation.
  • Develop, test and deploy stochastic simulation frameworks and Monte Carlo models for liabilities, asset-liability management (ALM) and risk mitigation decisions; integrate economic scenario generators where required.
  • Lead client-facing workshops and interactive presentations to explain technical actuarial results to non-technical stakeholders, create executive summaries and actionable recommendations that influence pricing, reserving and capital decisions.
  • Manage project delivery for multiple client engagements: create project plans, allocate resources, monitor timelines and budgets, and coordinate cross-functional teams including data engineers, finance, underwriting and IT.
  • Perform model governance: design validation plans, backtesting, sensitivity and scenario testing, document model assumptions, limitations and usage, and implement model change controls and versioning.
  • Clean, transform and validate large insurance datasets using SQL and scripting languages; implement automated ETL processes and data quality checks to support reliable actuarial outputs.
  • Implement automation and reproducible workflows (version control, modular code, notebooks) to improve efficiency, reduce manual processes and scale actuarial delivery across client portfolios.
  • Prepare regulatory submissions and respond to regulator queries, ensuring actuarial methodologies and documentation meet regulatory standards (Solvency II quantitative reporting templates, IFRS 17 disclosures).
  • Advise on product design and underwriting strategy by providing loss cost estimates, rating algorithms, segmentation strategies and profitability forecasts for new and existing products.
  • Support M&A due diligence and transaction advisory: perform actuarial due diligence, quantify reserve adequacy, evaluate reinsurance programs and produce post-acquisition integration recommendations.
  • Conduct longevity, morbidity and lapse studies for life and health products; model policyholder behavior and its impact on liability cash flows and pricing assumptions.
  • Mentor junior actuaries and analysts: review workpapers, provide technical coaching on reserving, pricing, coding standards and exam study guidance, and foster professional development.
  • Coordinate with finance for statutory and management reporting reconciliations; explain actuarial adjustments, reserve movements and the impact of actuarial assumptions on financial statements.
  • Monitor industry trends, emerging risks (climate, cyber, pandemic), and regulatory changes; translate these into updated models, stress scenarios and client advisory materials.
  • Perform rate filings support: prepare actuarial exhibits, respond to state regulator questions and support actuaries in navigating jurisdictional filing requirements.
  • Conduct credibility and experience studies to set appropriate credibility weights, credibility-adjusted rates and smoothing techniques across portfolios.
  • Drive proof-of-concept data science projects within insurance lines to evaluate advanced techniques (machine learning, gradient boosting, neural nets) against traditional actuarial models and recommend appropriate governance for production use.
  • Provide ad-hoc expert actuarial advice to underwriting, claims and product teams, helping to interpret model drivers, triggers for remediation and pricing renewal strategies.

Secondary Functions

  • Support ad-hoc data requests and exploratory data analysis to validate assumptions and prepare data for modeling.
  • Contribute to the organization's actuarial methodology playbook and long-term data & model roadmap.
  • Collaborate with business units to translate strategic objectives into actuarial requirements and build KPI dashboards.
  • Participate in sprint planning and agile ceremonies within cross-functional consulting teams and ensure deliverables align with client milestones.
  • Produce clear, audit-ready documentation, methodology notes and reproducible notebooks for both internal review and external audit.
  • Assist pre-sales and proposal development by scoping actuarial workstreams, estimating effort and drafting technical approach sections.
  • Support continuous improvement initiatives including adoption of cloud-based analytics platforms, CI/CD for model deployment and enhancement of model governance frameworks.

Required Skills & Competencies

Hard Skills (Technical)

  • Strong actuarial modeling experience in reserving, pricing and capital modeling using GLMs, generalized additive models and stochastic simulation techniques.
  • Proficiency in R and/or Python for model development, validation, and automation (tidyverse, data.table, scikit-learn, statsmodels).
  • Advanced Excel skills, including VBA macros for automation and sensitivity testing; ability to integrate Excel workflows with code-based models.
  • SQL proficiency for data extraction, transformation and validation across relational databases and data warehouses.
  • Practical experience with actuarial software and tools: Prophet, Moses, ResQ, SAS, or equivalent actuarial systems.
  • Hands-on experience implementing or supporting IFRS 17 and Solvency II reporting and calculations.
  • Experience with reinsurance modelling and treaty pricing, including cash-flow based modelling and recovery testing.
  • Statistical expertise: survival analysis, time series, bootstrapping, credibility theory, and model validation techniques.
  • Knowledge of capital models and economic capital frameworks, including stress testing and scenario analysis.
  • Familiarity with cloud platforms and modern analytics tooling (AWS/GCP/Azure, Docker, Git), and implementing reproducible workflows.
  • Ability to produce regulatory and financial reporting artifacts and reconcile actuarial results with finance (IFRS/GAAP).
  • Experience with visualization and dashboarding tools (Tableau, Power BI, Shiny) to present actuarial findings clearly.
  • Strong testing, model governance and documentation practices including version control and audit trails.

Soft Skills

  • Excellent client-facing communication and presentation skills; able to translate complex actuarial concepts into clear business implications.
  • Strong problem-solving, analytical thinking and attention to detail when building and validating models.
  • Project management skills: ability to prioritize tasks across multiple engagements and deliver high-quality outputs to tight deadlines.
  • Collaborative mindset: work effectively with underwriters, claims, finance, data engineers and senior stakeholders.
  • Mentoring and leadership: coach junior team members, review workpapers and promote best practices.
  • Business acumen for insurance products, distribution channels and profitability levers.
  • Adaptability to changing regulatory environments and evolving data landscapes.
  • Professional integrity and ability to present independent actuarial opinions and sound judgement.

Education & Experience

Educational Background

Minimum Education:

  • Bachelor's degree in Actuarial Science, Mathematics, Statistics, Economics, Finance, Computer Science or related quantitative discipline.

Preferred Education:

  • Master's degree in Actuarial Science, Statistics, Financial Engineering, Data Science or MBA for consulting leadership roles.
  • Progress toward or attainment of actuarial credentials (ASA/ACAS/CERA/FIA/FSA) strongly preferred.

Relevant Fields of Study:

  • Actuarial Science
  • Mathematics / Statistics
  • Economics / Finance
  • Data Science / Computer Science

Experience Requirements

Typical Experience Range: 2–7+ years of actuarial experience in pricing, reserving, valuation, or capital modeling; consulting experience preferred for client-facing roles.

Preferred:

  • 3–5+ years in an actuarial consulting firm or insurer with demonstrable end-to-end project delivery.
  • Evidence of completed actuarial exams and progress toward professional qualification (e.g., ASA, ACAS, FIA).
  • Hands-on experience with IFRS 17 or Solvency II implementations, actuarial valuation systems, and modern analytics toolchains.
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