Actuary — Competency Roadmap

You'll learn

• tidyverse — collection of R packages designed for data manipulation and visualization
• pandas — Python data analysis library providing DataFrame structures
• Analysis ToolPak — Excel add-in for advanced statistical analysis

How to work through it

1. 1Install R and RStudio Desktop
2. 2Install Python along with pandas, numpy, and scipy
3. 3Configure Excel with the Analysis ToolPak add-in enabled
4. 4Write a simple script in R and Python that imports a CSV and calculates mean, standard deviation, and quantiles

You'll learn

• Double integrals — integration over 2D spatial regions used for joint density functions
• Improper integrals — integrals with infinite bounds used for unbounded continuous risk distributions
• Taylor series — polynomial approximation of non-linear financial payoff functions

How to work through it

1. 1Review single-variable integration by parts and substitution
2. 2Practice double integration over non-rectangular regions
3. 3Calculate infinite improper integrals using limits
4. 4Solve Taylor series expansions for exponential and logarithmic functions

You'll learn

• Covariance matrix — matrix whose elements are pairwise covariances between random variables
• Positive semi-definite — property ensuring non-negative portfolio variance in asset modeling
• Matrix projection — geometrical representation of least-squares parameter estimation

How to work through it

1. 1Review matrix multiplication, transpose, and matrix inversion
2. 2Implement matrix-vector multiplication and inversion in R and Python
3. 3Compute eigenvalues and eigenvectors for a sample covariance matrix
4. 4Verify that a covariance matrix is positive semi-definite

You'll learn

• Vectorized calculations — evaluating operations across entire data vectors simultaneously
• Dynamic arrays — modern spreadsheet formulas that spill results across multiple cells
• Model auditability — structuring financial sheets for third-party actuarial review

How to work through it

1. 1Generate synthetic policy records with age, gender, sum assured, and policy type
2. 2Write vectorized formulas in Excel using dynamic arrays (XLOOKUP, FILTER, LET)
3. 3Replicate the calculations in R using dplyr
4. 4Add a comparison tab confirming zero variance between outputs

You'll learn

• Moment Generating Function (MGF) — expected value of e^(tX) used to uniquely identify distributions
• Pareto distribution — heavy-tailed probability distribution used for modeling catastrophic losses
• Memoryless property — unique property of the exponential and geometric distributions

How to work through it

1. 1Derive the CDF and quantile functions for the exponential and Pareto distributions
2. 2Calculate expected values and variances using integration by parts and substitution
3. 3Derive moment generating functions and use them to find higher-order moments
4. 4Graph probability density functions across varying shape and scale parameters in R

You'll learn

• Law of Total Variance — decomposition of total variance into expected conditional variance and variance of conditional expectation
• Ordinary deductible — policy clause where the insurer pays only the loss excess over threshold d
• Truncation and censoring — mathematical modification of loss distributions caused by policy limits

How to work through it

1. 1Calculate marginal densities from bivariate joint distributions
2. 2Apply the Law of Total Probability and Law of Total Variance to hierarchical risks
3. 3Derive the distribution of transformed random variables (e.g., Y = min(X, u) - d)
4. 4Solve 15 exam-standard problems involving deductibles and coverage limits

You'll learn

• Maximum Likelihood Estimation (MLE) — method of estimating parameters that maximizes the likelihood of observed data
• Fisher Information — measure of the amount of information an observable random variable carries about an unknown parameter
• Goodness-of-fit tests — statistical tests determining whether empirical data follows a specific theoretical distribution

How to work through it

1. 1Derive log-likelihood functions for Poisson and Exponential samples
2. 2Compute Fisher information matrices to determine asymptotic standard errors
3. 3Code an optimization routine in R using `optim()` to estimate distribution parameters
4. 4Perform Chi-Square and Kolmogorov-Smirnov goodness-of-fit tests on fitted models

You'll learn

• BA II Plus / TI-30XS — standard approved actuarial calculators for professional exams
• Time allocation strategy — pacing framework targeting 5-6 minutes per question

How to work through it

1. 1Set a strict 3-hour timer in an isolated environment
2. 2Complete 30 authentic sample questions from SOA Exam P or IFoA CS1 papers
3. 3Grade the paper against official solutions
4. 4Categorize incorrect questions into conceptual errors, algebra slips, or timing issues

You'll learn

• Force of interest — instantaneous rate of interest per unit of time
• Nominal interest rate — quoted annual rate payable m times per year
• Discount factor (v) — present value factor 1 / (1 + i)

How to work through it

1. 1Derive relationships between effective rate i, nominal rate i^(m), discount rate d, and force of interest delta
2. 2Calculate present values and accumulated values under continuous non-constant force of interest delta(t)
3. 3Solve equations of value for unknown interest rates and unknown time horizons

You'll learn

• Annuity-immediate — series of equal payments made at the end of each period
• Annuity-due — series of equal payments made at the beginning of each period
• Perpetuity — stream of cash flows with no end date

How to work through it

1. 1Derive standard formulas for a_n and s_n (annuity immediate and due)
2. 2Derive formulas for increasing annuities (Ia)_n and decreasing annuities (Da)_n
3. 3Formulate equations for payment streams with geometric payment growth
4. 4Solve 10 multi-stage annuity scheduling problems

You'll learn

• Clean vs. Dirty price — bond pricing excluding or including accrued interest since the last coupon
• Prospective method — loan balance calculated as present value of remaining future payments
• Spot rate — annualized yield on a zero-coupon bond for a specific maturity

How to work through it

1. 1Calculate outstanding loan balances using prospective and retrospective methods
2. 2Compute principal and interest splits for any installment k
3. 3Price bonds traded at a premium, par, and discount
4. 4Construct spot rate discounting engines in Excel using cubic spline interpolation

You'll learn

• Macaulay duration — weighted average term to cash flow receipt
• Modified duration — direct percentage change in asset price with respect to a change in yield
• Redington immunization — balance sheet matching strategy immunizing net worth against small interest rate shifts

How to work through it

1. 1Calculate Macaulay duration and modified duration for fixed cash flow streams
2. 2Compute portfolio convexity and second-order price approximation equations
3. 3Formulate Redington immunization criteria (matching present value, duration, and convexity)
4. 4Test portfolio asset survival across instantaneous parallel interest rate shifts

You'll learn

• Exponential Dispersion Family — broad family of distributions including Poisson, Gamma, and Tweedie
• Link function — function linking the linear predictor to the expected mean response
• Offset — variable with a known regression coefficient of 1 used to account for exposure differences

How to work through it

1. 1Review ordinary linear models and diagnostic assumptions
2. 2Derive the Exponential Dispersion Family (EDF) formulation
3. 3Study canonical link functions: log link for Poisson/Gamma and logit for Binomial
4. 4Understand offset terms for handling exposure (earned car years, policyholder exposure)

You'll learn

• Pure premium — expected loss cost per unit of exposure (Frequency * Severity)
• Lift chart — visual tool ranking policyholders by predicted risk to test model calibration
• AIC / BIC — information criteria used to penalize model complexity during variable selection

How to work through it

1. 1Import and clean an auto insurance dataset with claims, exposure, and driver covariates
2. 2Fit a Poisson GLM for frequency using `glm(..., family = poisson(link = "log"), offset = log(exposure))`
3. 3Fit a Gamma GLM for claim severity on records with non-zero claims
4. 4Multiply predicted frequency and severity to obtain pure premium
5. 5Generate lift charts and calculate Gini coefficients to assess risk separation

You'll learn

• Stationarity — property where a time series mean, variance, and autocorrelation are constant over time
• ARIMA — AutoRegressive Integrated Moving Average model for non-stationary trend data
• Lee-Carter model — standard demographic model for forecasting mortality rates across age and time

How to work through it

1. 1Test time series for stationarity using the Augmented Dickey-Fuller (ADF) test
2. 2Examine Autocorrelation (ACF) and Partial Autocorrelation (PACF) plots
3. 3Fit ARIMA(p,d,q) models and evaluate residual white noise properties
4. 4Project historical loss inflation forward 5 years with confidence bands

You'll learn

• Gradient Boosting — ensemble technique building decision trees sequentially to minimize pseudo-residuals
• SHAP values — game-theoretic approach to explaining individual machine learning model predictions
• Rate relativity — price multiplier applied to base rates for specific risk characteristics

How to work through it

1. 1Train an XGBoost model on policy loss data with hyperparameter tuning via cross-validation
2. 2Calculate SHAP (SHapley Additive exPlanations) values to interpret model features
3. 3Compare out-of-sample predictive power between XGBoost and the baseline GLM
4. 4Draft a 1-page summary justifying rate relativities under regulatory fairness constraints

You'll learn

• Force of mortality (mu) — instantaneous hazard rate of death at age x
• Select and ultimate tables — mortality tables recognizing lower initial mortality among recently underwritten lives
• Uniform Distribution of Deaths (UDD) — assumption that deaths are evenly distributed across single years of age

How to work through it

1. 1Derive relationships between S_x(t), f_x(t), mu(x+t), and _t p_x
2. 2Construct a standard life table from raw mortality probabilities
3. 3Understand select, ultimate, and aggregate mortality tables
4. 4Solve problems applying Uniform Distribution of Deaths (UDD) for non-integer ages

You'll learn

• Actuarial Present Value (APV) — expected value of a contingent payment discounted for interest and mortality
• Joint life status (xy) — status that fails upon the first death of either life x or y
• Last survivor status — status that fails upon the death of the second/final life

How to work through it

1. 1Derive the actuarial present value formula A_x and its relation to annuity factor a_ddot_x
2. 2Calculate expected present values for n-year term and endowment products
3. 3Compute second moments to find the variance of the present value of benefit payouts
4. 4Extend calculations to joint life and last survivor statuses (T_xy and T_overline_xy)

You'll learn

• Equivalence principle — pricing standard setting expected value of future loss at issue to zero
• Net premium — premium calculated solely to fund expected benefit payouts without expense loading
• Expense loading — additions to net premiums to cover underwriting, administration, and acquisition costs

How to work through it

1. 1Formulate the equivalence principle equation for annual and m-thly premiums
2. 2Incorporate expense cash flows (per-policy, percentage of premium, per-thousand face value)
3. 3Calculate gross level premiums for standard product designs
4. 4Perform sensitivity testing on interest rates, mortality shifts, and expense assumptions

You'll learn

• Prospective reserve — liability value equal to the expected present value of future obligations minus future premiums
• Death strain at risk (DSAR) — net payout amount at risk of claim exceeding accumulated reserve
• Thiele's differential equation — continuous differential equation governing policy reserve changes over time

How to work through it

1. 1Derive the prospective reserve formula _t V as APV(future benefits) - APV(future premiums)
2. 2Derive the recursive reserve formula linking _t V to _{t+1} V
3. 3Calculate death strains at risk and expected mortality profits for seasoned cohorts
4. 4Implement Thiele's differential equation for continuous reserve progression

You'll learn

• (a,b,0) class — family of count distributions (Poisson, Binomial, Negative Binomial) satisfying p_k/p_{k-1} = a + b/k
• Left truncation — data omission where losses below a deductible threshold are entirely unrecorded
• Heavy-tailed distribution — distributions with tails heavier than exponential, prone to extreme outliers

How to work through it

1. 1Analyze the recurrence relations defining (a, b, 0) and (a, b, 1) frequency classes
2. 2Construct likelihood functions for left-truncated and right-censored severity data
3. 3Fit parametric distributions to heavy-tailed catastrophe loss data in R
4. 4Generate QQ plots to evaluate tail behavior of Pareto versus Log-logistic models

You'll learn

• Compound distribution — sum of a random number of independent random variables
• Panjer recursion — numerical algorithm computing compound aggregate loss distributions without continuous convolution
• Tail Value at Risk (TVaR) — coherent risk measure computing the expected loss given that loss exceeds the VaR threshold

How to work through it

1. 1Derive formulas for E[S] and Var(S) under compound distribution assumptions
2. 2Implement Panjer's recursive algorithm for discretized severity distributions
3. 3Write a Monte Carlo engine in R simulating frequency from Poisson and severity from Gamma
4. 4Calculate Value at Risk (VaR) and Tail Value at Risk (TVaR / CTE) at the 99.5% confidence level

You'll learn

• Buhlmann credibility — linear empirical Bayes estimation minimizing mean squared error
• Expected Process Variance (EPV) — expected within-risk variance of individual policyholder claims
• Variance of Hypothetical Means (VHM) — variance across different risk classes in the population

How to work through it

1. 1Calculate full credibility standards under limited fluctuation criteria (e.g., within 5% of mean with 90% probability)
2. 2Derive the Buhlmann credibility factor Z = n / (n + k) from Bayesian principles
3. 3Calculate expected process variance (EPV) and variance of hypothetical means (VHM)
4. 4Apply Buhlmann-Straub credibility to fleets with unequal annual exposures

You'll learn

• Surplus process — mathematical function tracking insurer capital over time as premiums accumulate and claims emerge
• Lundberg's inequality — mathematical bound on the probability that an insurer's capital ever drops below zero
• Safety loading — percentage markup on pure premium required to prevent certain ruin

How to work through it

1. 1Formulate the classical Cramér-Lundberg surplus process
2. 2Derive the adjustment coefficient (Lundberg coefficient) R
3. 3Calculate upper bounds on ruin probability using Lundberg's inequality psi(u) <= e^(-Ru)
4. 4Simulate 10,000 multi-year surplus paths to measure empirical ruin frequency

You'll learn

• Arrow-Pratt measure — mathematical metric of absolute risk aversion -u''(w)/u'(w)
• Efficient frontier — set of optimal portfolios offering highest expected return for a given level of risk
• CAPM — model establishing linear relationship between systematic risk and expected return

How to work through it

1. 1Derive absolute and relative risk aversion coefficients for standard utility functions
2. 2Formulate the Markowitz quadratic programming optimization problem
3. 3Calculate optimal asset weights along the efficient frontier
4. 4Derive CAPM beta and security market lines

You'll learn

• Brownian motion (Wiener process) — continuous-time stochastic process with stationary, independent Gaussian increments
• Ito's Lemma — stochastic chain rule accounting for non-zero second-order variance terms
• Risk-neutral measure (Q-measure) — probability measure under which discounted asset prices are martingales

How to work through it

1. 1Review properties of Wiener processes and continuous martingales
2. 2Derive and state Ito's Lemma for multivariable functions
3. 3Formulate Geometric Brownian Motion (GBM) as the standard stock price model
4. 4Understand the Cameron-Martin-Girsanov theorem for risk-neutral measure changes

You'll learn

• The Greeks — sensitivity measures of option price with respect to underlying asset parameters
• Binomial tree — discrete-time lattice model for valuing financial derivative options
• GMDB — policy guarantee ensuring a minimum payout upon death regardless of investment portfolio performance

How to work through it

1. 1Derive the Black-Scholes formula for European call and put options
2. 2Calculate option Greeks (Delta, Gamma, Vega, Theta, Rho)
3. 3Build an n-step recombining binomial tree in Python
4. 4Price a Guaranteed Minimum Death Benefit (GMDB) subject to equity market volatility and mortality discounting

You'll learn

• Vasicek model — mathematical model describing the evolution of interest rates via mean-reverting Ornstein-Uhlenbeck processes
• Cholesky decomposition — matrix factorization technique used to generate correlated random variables
• Economic Scenario Generator (ESG) — software engine generating stochastic financial trajectories for balance sheet stress testing

How to work through it

1. 1Implement the Vasicek and CIR mean-reverting interest rate models
2. 2Simulate correlated Brownian motions using Cholesky decomposition
3. 3Generate stochastic yield curves across 30 annual projection steps
4. 4Validate scenario sets against risk-neutral 1-dollar arbitrage tests

You'll learn

• IBNR (Incurred But Not Reported) — estimated reserve for claims that have occurred but have not yet been reported to the insurer
• Loss Development Factor (LDF) — ratio of cumulative losses between two development periods
• Mack method — distribution-free stochastic model estimating the standard error of Chain Ladder reserves

How to work through it

1. 1Format raw transaction claims into accident-year by development-year loss triangles
2. 2Calculate volume-weighted loss development factors (link ratios)
3. 3Project cumulative losses to ultimate and calculate IBNR reserves
4. 4Apply Mack's formula to estimate the standard error of reserve predictions

You'll learn

• Bornhuetter-Ferguson method — reserving technique weighting actual emerged losses with a priori expected loss expectations
• Cape Cod method — variant of BF method estimating the prior loss ratio directly from pooled historical triangle data
• Long-tail lines — insurance lines where claims take many years to emerge and settle

How to work through it

1. 1Establish a priori loss ratio assumptions for each accident cohort
2. 2Implement the Bornhuetter-Ferguson formula: Reserve = Exposure * Expected_LR * (1 - 1/LDF)
3. 3Implement the Cape Cod method to calculate an endogenous expected loss ratio
4. 4Compare reserve estimates across lines with long reporting tails (e.g., medical malpractice vs. auto physical damage)

You'll learn

• Parallelogram method — geometric technique adjusting historical earned premiums to current rate levels
• Loss trend factor — inflation factor adjusting historical claim sizes and frequencies to future policy periods
• Rate indication — calculated actuarial recommendation for overall rate level adjustment

How to work through it

1. 1Calculate historical on-level earned premium adjustments using the parallelogram method
2. 2Apply loss development and trend factors to adjust past losses to future cost levels
3. 3Incorporate variable expense provisions, fixed expense provisions, and target underwriting profit
4. 4Calculate indicated rate change: % Change = (Loss + Fixed Expense) / (1 - Variable Expense - Profit) / Premium - 1

You'll learn

• Excess of Loss (XOL) reinsurance — non-proportional treaty where reinsurer covers losses exceeding an agreed retention
• Exceedance Probability (EP) curve — curve showing the annual probability that catastrophic losses exceed specified financial thresholds
• Reinstatement premium — contractual fee paid by primary insurer to restore exhausted reinsurance coverage limits

How to work through it

1. 1Simulate catastrophic event occurrences using non-homogeneous Poisson processes
2. 2Apply vulnerability damage functions to property exposures
3. 3Structure excess of loss reinsurance treaties (e.g., $50M xs $10M layer with reinstatements)
4. 4Compute net loss distributions and reinsurance treaty pricing yields

You'll learn

• Profit signature — vector of annual net cash profits produced by a cohort of insurance policies
• Dynamic lapse assumption — surrender behavior modeled as a function of external market interest rates
• Profit margin — present value of future profits expressed as a percentage of present value of future premiums

How to work through it

1. 1Set up decrement arrays tracking policy counts across death, surrender/lapse, and maturity
2. 2Project cash inflows (premiums, investment returns) and outflows (claims, surrenders, commission, maintenance expenses)
3. 3Calculate annual increase in reserves and cost of holding required solvency capital
4. 4Calculate the discounted profit signature, Net Present Value (NPV), and discounted payback period

You'll learn

• PMPM (Per Member Per Month) — standard metric in health insurance representing revenue or cost per covered life each month
• Morbidity rate — incidence and duration of disease or disability within a defined population cohort
• Medical Loss Ratio (MLR) — percentage of premium revenue spent by a health insurer on clinical services and quality improvement

How to work through it

1. 1Calculate Per-Member-Per-Month (PMPM) cost benchmarks across outpatient, inpatient, and pharmacy categories
2. 2Apply age/gender morbidity rating factors and annual medical inflation trend factors
3. 3Incorporate insurer administrative expenses and verify compliance with statutory Medical Loss Ratio (MLR) floors
4. 4Simulate health risk adjustment transfers between competing carrier plans

You'll learn

• Cash Flow Testing (CFT) — regulatory requirement testing asset cash flows against policy liabilities under multiple interest paths
• Disintermediation risk — risk that policyholders surrender annuities in rising rate environments, forcing asset sales at a loss
• Conditional Tail Expectation (CTE) — average outcome in the worst (1-alpha)% tail of scenario results

How to work through it

1. 1Link asset portfolio cash flows (bonds, mortgages) with annuity surrender and payout cash flows
2. 2Implement dynamic surrender rules where policyholders lapse if market rates exceed credited rates
3. 3Run 1,000 30-year economic scenarios through the integrated model
4. 4Calculate CTE 70 and CTE 90 capital metrics to assess severe downside insolvency risk

You'll learn

• Entry Age Normal (EAN) — actuarial cost method spreading pension liability levelly over employee career service
• Projected Unit Credit (PUC) — actuarial cost method allocating benefits based directly on accrued service to date
• Actuarial Accrued Liability (AAL) — portion of the total pension liability attributed to past years of employee service

How to work through it

1. 1Formulate the service decrement table including retirement, withdrawal, disability, and mortality
2. 2Incorporate salary scale functions to project final average salaries at retirement
3. 3Derive the formulas for Normal Cost (NC) and Actuarial Accrued Liability (AAL) under Projected Unit Credit
4. 4Derive NC and AAL formulas under Entry Age Normal (level dollar and level percentage of pay)

You'll learn

• Funded ratio — ratio of actuarial asset value to actuarial accrued liabilities (Assets / AAL)
• UAAL — Unfunded Actuarial Accrued Liability (AAL minus plan assets)
• Mortality improvement scale — 2D projection matrix capturing generational increases in life expectancy over time

How to work through it

1. 1Import employee census data (entry age, current age, service years, current salary)
2. 2Project retirement benefit cash flows using the MP-2021 mortality improvement scale
3. 3Discount projected benefits back to valuation date to compute plan AAL and Normal Cost
4. 4Establish an amortization schedule for Unfunded Actuarial Accrued Liabilities (UAAL) over a 15-year period

You'll learn

• Pay-As-You-Go (PAYG) — retirement financing where current worker contributions immediately pay current retiree pensions
• Old-age dependency ratio — ratio of population aged 65+ to the working-age population aged 15-64
• Decumulation — phase of retirement where an individual converts accumulated investment capital into income

How to work through it

1. 1Model individual retirement savings trajectories under stochastic market returns
2. 2Simulate safe post-retirement drawdown strategies (e.g., 4% rule vs. variable spending)
3. 3Analyze Pay-As-You-Go (PAYG) social security equilibrium equations: t * w * L_w = b * L_r
4. 4Evaluate policy responses to demographic dependency ratio shifts (increasing retirement age vs. contribution rates)

You'll learn

• Contractual Service Margin (CSM) — component of IFRS 17 balance sheet representing unearned profit to be recognized over time
• Best Estimate Liability (BEL) — probability-weighted present value of future cash flows without conservatism loading
• Risk Adjustment — compensation required for bearing the uncertainty about amount and timing of cash flows

How to work through it

1. 1Deconstruct the three building blocks: Best Estimate Liabilities (BEL), Discounting, and Risk Adjustment
2. 2Calculate Contractual Service Margin (CSM) at policy inception and amortize it across coverage periods
3. 3Study the simplified Premium Allocation Approach (PAA) for short-duration general insurance contracts
4. 4Build an Excel model illustrating CSM recognition across 5 policy years

You'll learn

• Solvency Capital Requirement (SCR) — capital required under Solvency II ensuring 99.5% survival probability over 1 year
• ORSA (Own Risk and Solvency Assessment) — internal process where an insurer assesses its capital adequacy across strategic plans
• Statutory Accounting Principles (SAP) — accounting framework focused strictly on insurer solvency and policyholder protection

How to work through it

1. 1Analyze Pillar 1 (Quantitative requirements), Pillar 2 (Qualitative/Governance), and Pillar 3 (Reporting/Disclosures)
2. 2Calculate Basic Solvency Capital Requirement (BSCR) by aggregating risk modules via regulatory correlation matrices
3. 3Understand the Own Risk and Solvency Assessment (ORSA) report requirements
4. 4Compare Solvency II balance sheet market-consistent valuation with US Statutory accounting conservatism

You'll learn

• Actuarial Standards of Practice (ASOP) — binding professional guidelines setting minimum quality and disclosure rules
• Model governance — documentation standards ensuring models are peer-reviewable, robust, and reproducible
• Executive translation — skill of explaining tail risk, confidence intervals, and uncertainty to non-technical boards

How to work through it

1. 1Review Actuarial Standards Board (ASB) ASOPs and UK Technical Actuarial Standards (TAS)
2. 2Write a comprehensive methodology and assumption justification section for an actuarial valuation
3. 3Draft a 2-page non-technical executive summary translating technical risks without jargon
4. 4Implement a formal model documentation audit trail matching IFoA CP2 exam standards

You'll learn

• Associate vs. Fellow — intermediate professional credential (ASA/ACAS/AIA) versus fully qualified specialist (FSA/FCAS/FIA)
• VEE (Validation by Educational Experience) — SOA requirement covering Economics, Accounting/Finance, and Math Stats
• Study hour heuristic — industry standard guideline allocating 100 study hours per hour of exam duration (300+ hrs)

How to work through it

1. 1Compare the IFoA sequence (Core Principles CS1-2, CM1-2, CB1-3, CP1-3, Specialist SP/SA) with the SOA/CAS tracks
2. 2Identify study resource ecosystems (Coaching Actuaries ADAPT, ActEd UK, The Infinite Actuary)
3. 3Map your academic background against potential university exemptions
4. 4Finalize your initial target exam (SOA Exam P / IFoA CS1 or SOA Exam FM / IFoA CM1)

You'll learn

• R Markdown / Quarto — publishing systems for reproducible data analysis combining prose and execution code
• Technical portfolio — verifiable public evidence of domain competence for hiring managers
• Code reproducibility — structuring projects so third parties can execute code and replicate identical numerical results

How to work through it

1. 1Organize code, data, and output directories across your 4 primary modeling projects
2. 2Write comprehensive README files detailing the actuarial theory, methodology, and results for each project
3. 3Generate reproducible R Markdown / Jupyter notebook reports
4. 4Publish repositories with open-source licenses and clear citations

You'll learn

• Actuarial Case Study — technical interview format where candidate analyzes a hypothetical insurance portfolio scenario
• Commercial awareness — understanding how macroeconomic shifts directly impact insurer underwriting profits and solvency
• STAR technique — structured interview communication method (Situation, Task, Action, Result)

How to work through it

1. 1Practice mental math, probability puzzles, and distribution estimation questions
2. 2Prepare STAR-format explanations for every project in your portfolio
3. 3Practice live spreadsheet modeling drills under a 30-minute time constraint
4. 4Rehearse answers to current industry trends (climate risk modeling, inflation spikes, IFRS 17 implementation)