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Key Responsibilities and Required Skills for Actuarial Intern

💰 $18 - $35 / hr

ActuarialInternshipInsuranceRiskAnalytics

🎯 Role Definition

The Actuarial Intern plays a hands-on, analytical role within the actuarial or analytics team. Under direct supervision of qualified actuaries or senior analysts, the intern will support pricing and reserving analyses, data preparation and validation, model development and documentation, and preparation of internal management reporting. This role is designed for high-performing actuarial students seeking practical exposure to industry-standard actuarial techniques (chain-ladder, Bornhuetter-Ferguson, GLMs, exposure-based pricing), regulatory and financial reporting considerations (GAAP/STAT/IFRS concepts), and analytics tools (Excel, VBA, SQL, R, Python, SAS). The position emphasizes learning, accuracy, effective communication with business partners, and contributing measurable results to underwriting, claims, finance, and product teams.

📈 Career Progression

Typical Career Path

Entry Point From:

  • Actuarial Intern (college/university internship)
  • Co-op student in actuarial science, mathematics, statistics, finance, or economics
  • Junior data analyst or pricing assistant transitioning into actuarial track

Advancement To:

  • Actuarial Analyst / Junior Actuary
  • Pricing Actuary or Reserving Actuary (associate-level roles)
  • Senior Actuarial Analyst → Actuary with credentialing (ASA/FSA)

Lateral Moves:

  • Data Analyst / Data Scientist (insurance analytics)
  • Risk Analyst / Underwriting Analyst
  • Financial Reporting Analyst (P&L, reserving, capital modeling)

Core Responsibilities

Primary Functions

  • Support the preparation and reconciliation of loss triangles and claims development tables, ensuring data integrity and clear linkage to source systems for reserving analyses.
  • Assist in the calculation of case reserves and IBNR (incurred but not reported) reserves using standard actuarial techniques such as chain-ladder, Bornhuetter-Ferguson, and development factor methods.
  • Participate in pricing project workstreams by preparing exposure and claims datasets, deriving rate indications using GLMs (generalized linear models), and testing pricing hypotheses under supervision.
  • Clean, transform, and validate large datasets from policy, claims, and exposure systems using Excel, SQL, Python or R to create analysis-ready datasets for actuarial models.
  • Build, update, and document actuarial models and workpapers, including version control, input/output mapping, assumptions, and sensitivity testing to support audit and peer review.
  • Run and interpret frequency-severity and severity-only models; produce summary analysis to inform underwriting and product development decisions.
  • Support senior actuaries in performing trend analyses, credibility assessments and experience studies to recommend appropriate pricing adjustments or reserve assumptions.
  • Conduct scenario and stress testing for pricing and reserving assumptions and prepare clear visualizations and narratives explaining the business impact to stakeholders.
  • Assist with month-end and quarter-end actuarial reporting tasks, such as accruals, reserve rollforwards, and contribution-to-change analyses for management and finance partners.
  • Participate in the development and validation of predictive models, including feature engineering, model performance evaluation (AUC, KS, RMSE), and documentation of model selection rationale.
  • Help prepare regulatory and statutory exhibits or schedules by extracting required data and ensuring compliance with reporting formats and deadlines.
  • Support loss cost and exposure trend analysis by integrating external data sources (economic indicators, industry indexes) and internal policy-level information.
  • Prepare slides, memos, and summary tables that translate actuarial results into business recommendations for underwriting, claims, finance, and product teams.
  • Assist in automating recurring actuarial processes (data ingestion, model runs, reporting templates) using macros, scripts, and ETL best practices to improve accuracy and efficiency.
  • Participate in peer review and quality assurance activities, identifying anomalies, performing root cause analysis, and implementing corrective actions under guidance.
  • Contribute to the development of pricing tools and rate manuals by documenting assumptions, calculation steps, and usage guidelines for underwriters and brokers.
  • Support capital and solvency modeling tasks by gathering inputs, building simple stochastic scenarios, and running delegated model components for senior actuaries.
  • Help maintain an actuarial knowledge base, including templates, model libraries, and FAQ documents to accelerate onboarding and ensure consistency across teams.
  • Assist in expense allocation and trend analysis to ensure pricing and profitability models reflect realistic expense assumptions and commission structures.
  • Observe and learn best practices for model governance, including change logs, model validation checklists, and communication protocols for model updates.
  • Collaborate with claims and underwriting teams to investigate outliers, develop explanatory analyses for loss drivers, and support mitigation or pricing actions.
  • Provide timely responses to ad-hoc requests from actuarial, underwriting, claims, and finance partners, prioritizing tasks based on business needs and deadlines.

Secondary Functions

  • Support ad-hoc data requests and exploratory data analysis to answer business questions and provide actionable insights.
  • Contribute to the organization's actuarial and data analytics strategy by suggesting automation opportunities and improvements in data quality.
  • Collaborate with cross-functional business units to translate product, underwriting, and claims needs into analytics and modeling requirements.
  • Participate in agile ceremonies, sprint planning, and status updates within data and actuarial project teams to help deliver incremental improvements.

Required Skills & Competencies

Hard Skills (Technical)

  • Proficiency in Microsoft Excel (advanced formulas, pivot tables, structured references) and comfort building repeatable workbooks for actuarial use.
  • Familiarity with SQL for data extraction, joining large policy and claims tables, and writing efficient queries to support actuarial analyses.
  • Experience or coursework in R or Python for data analysis, model building, and automation (libraries such as pandas, numpy, scikit-learn, glm in R).
  • Basic understanding of generalized linear models (GLMs), regression techniques, and how they apply to insurance pricing and ratemaking.
  • Exposure to reserving methodologies (chain-ladder, Bornhuetter-Ferguson, Mack, stochastic reserving concepts).
  • Ability to build clear, documented actuarial workpapers and model documentation that support audit and peer review standards.
  • Familiarity with SAS, Prophet, Emblem, or other actuarial software is a plus; ability to learn proprietary actuarial tools quickly.
  • Comfort with data visualization tools and techniques (Excel charting, Tableau, Power BI, ggplot2) to communicate results effectively.
  • Knowledge of insurance products, coverages, exposures, and claim lifecycle across P&C or Life/Health lines relevant to the role.
  • Understanding of basic finance and accounting concepts relevant to reserving and pricing, including earned premium, ceded reinsurance, and incurred loss.
  • Experience with version control basics (Git) or disciplined file/version management to ensure reproducibility of analyses.
  • Exposure to actuarial exam curricula (Probability, Financial Mathematics) or demonstrated progress on actuarial exam track (P/1, FM/2, or equivalents).

Soft Skills

  • Strong analytical and quantitative problem-solving skills with the ability to reconcile exceptions and identify data issues.
  • Excellent written and verbal communication skills; able to translate technical results into concise business recommendations for non-technical stakeholders.
  • Attention to detail and commitment to accuracy in data handling, calculations, and documentation.
  • Collaborative team player who actively seeks feedback, shares learnings, and supports peers and cross-functional partners.
  • Time management and prioritization skills to balance multiple projects and deliverables in a fast-paced environment.
  • Intellectual curiosity and eagerness to learn new actuarial techniques, tools, and insurance business drivers.
  • Professionalism and ethical judgment when handling confidential or sensitive policyholder and financial data.
  • Adaptability to changing priorities and ability to work effectively under supervision in both structured and ambiguous tasks.
  • Presentation and storytelling skills to prepare clear summaries, charts, and talking points for meetings and project updates.
  • Critical thinking and hypothesis-driven approach to exploratory data analysis and root cause investigations.

Education & Experience

Educational Background

Minimum Education:

  • Currently enrolled in a Bachelor's degree program in Actuarial Science, Mathematics, Statistics, Economics, Finance, Data Science, or a closely related quantitative field.

Preferred Education:

  • Bachelor’s or Master’s degree in Actuarial Science, Mathematics, Statistics, Data Science, Finance, or Economics.
  • Formal coursework or certificate in actuarial modeling, probability, statistical inference, or insurance analytics.

Relevant Fields of Study:

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

Experience Requirements

Typical Experience Range:

  • 0–1 years (student internship/co-op); recent graduates welcome for summer or part-time intern roles.

Preferred:

  • Prior internship or project experience in insurance, analytics, or finance.
  • Passing one or more actuarial exams (P/1, FM/2) or demonstrated progress on the exam track.
  • Hands-on experience with Excel-based actuarial models, SQL queries, and at least one scripting language (R or Python).
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