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Casualty Actuarial Society Credentials (ACAS/FCAS) Exam 7 — Estimation of Policy Liabilities, Insurance Company Valuation, and Enterprise Risk Management Syllabus
The Exam 7 — Estimation of Policy Liabilities, Insurance Company Valuation, and Enterprise Risk Management syllabus for Casualty Actuarial Society Credentials (ACAS/FCAS) in full: 4 chapters, 12 topics and 25 sub-topics, every one listed below, plus 61 Exam 7 — Estimation of Policy Liabilities, Insurance Company Valuation, and Enterprise Risk Management flashcards with answers.
Exam 7 — Estimation of Policy Liabilities, Insurance Company Valuation, and Enterprise Risk Management syllabus: all 4 chapters
Expand any chapter to see its topics and sub-topics.
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Advanced Reserving Techniques
3 topics- Stochastic Reserving Models
- Mack model and variability of chain ladder estimates
- Bootstrap methods for reserve distributions
- Over-dispersed Poisson and GLM reserving
- Bayesian and Credibility Reserving
- Clark's growth curve / LDF and Cape Cod methods
- Bayesian reserve estimation approaches
- Reserve Ranges and Uncertainty
- Process vs. parameter vs. model risk
- Quantifying and communicating reserve variability
- Stochastic Reserving Models
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Reinsurance Reserving and Pricing
3 topics- Reinsurance Loss Reserving
- Challenges of long-tail reinsurance triangles
- Estimating ceded and assumed reserves
- Excess and Layered Losses
- Development of excess layers
- Allocating losses across reinsurance layers
- Reinsurance Program Considerations
- Treaty structures and their reserving impact
- Commutations and finality of reinsurance liabilities
- Reinsurance Loss Reserving
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Insurance Company Valuation
3 topics- Valuation Frameworks
- Embedded value and appraisal value
- Economic value of in-force business
- Cost of Capital and Financial Pricing
- Cost of capital allocation
- Discounting liabilities and risk margins
- Earnings and Performance Measurement
- Sources of earnings analysis
- Return on equity and value creation metrics
- Valuation Frameworks
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Enterprise Risk Management
3 topics- ERM Frameworks and Risk Identification
- Risk taxonomy and risk appetite
- ORSA (Own Risk and Solvency Assessment)
- Risk Measurement
- Value at Risk and Tail Value at Risk
- Economic capital modeling
- Solvency Frameworks and Regulation
- Solvency II and internal models
- Rating agency capital models
- ERM Frameworks and Risk Identification
Casualty Actuarial Society Credentials (ACAS/FCAS) Exam 7 — Estimation of Policy Liabilities, Insurance Company Valuation, and Enterprise Risk Management flashcards — 61 questions with answers
In the Mack (1993) distribution-free chain-ladder model, what are the three key assumptions?
(1) Expected cumulative losses scale with the prior period: $E[C_{i,k+1}\mid C_{i,1},\dots,C_{i,k}] = f_k\, C_{i,k}$. (2) Variance proportional to the prior: $\mathrm{Var}[C_{i,k+1}\mid \cdots] = \sigma_k^{2}\, C_{i,k}$. (3) Accident years $i$ are independent.
State the Mack estimator of the age-to-age development factor $f_k$ and its variance parameter $\sigma_k^{2}$.
$\hat{f}_k = \dfrac{\sum_{i=1}^{n-k} C_{i,k+1}}{\sum_{i=1}^{n-k} C_{i,k}}$ (volume-weighted). And $\hat{\sigma}_k^{2} = \dfrac{1}{n-k-1}\sum_{i=1}^{n-k} C_{i,k}\left(\dfrac{C_{i,k+1}}{C_{i,k}} - \hat{f}_k\right)^{2}$.
How does Mack estimate $\sigma_{n-1}^{2}$ for the last development period when the usual formula is undefined?
Use the extrapolation $\hat{\sigma}_{n-1}^{2} = \min\!\left(\dfrac{\hat{\sigma}_{n-2}^{4}}{\hat{\sigma}_{n-3}^{2}},\ \hat{\sigma}_{n-3}^{2},\ \hat{\sigma}_{n-2}^{2}\right)$, assuming the $\sigma_k^{2}$ decrease geometrically.
What is the conceptual difference between process variance, parameter variance, and total prediction (mean square) error in reserve estimation?
Process variance is the inherent randomness of future outcomes around the true mean; parameter variance (estimation error) reflects uncertainty in the estimated parameters; total prediction error (MSEP) combines both: $\text{MSEP} = \text{process variance} + \text{estimation variance}$.
In the over-dispersed Poisson (ODP) model used for the bootstrap, what is the variance structure and how does it relate to incremental claims?
Incremental claims $q_{ij}$ satisfy $E[q_{ij}]=m_{ij}$ and $\mathrm{Var}[q_{ij}]=\phi\, m_{ij}$, where $\phi$ is the dispersion (scale) parameter. It reproduces chain-ladder reserve point estimates while allowing a full predictive distribution.
Describe the two-step structure of the ODP bootstrap for reserve variability (Shapland/England-Verrall).
Step 1 — resample the (adjusted) Pearson residuals to create pseudo-triangles and refit, generating the distribution of parameter/estimation error. Step 2 — for each bootstrap point estimate, simulate the process error by drawing future incrementals from a process distribution (e.g., gamma/ODP). Combining gives the full predictive distribution of unpaid claims.
Define the unscaled Pearson residual and the degrees-of-freedom adjustment factor used in the ODP bootstrap.
Pearson residual: $r_{ij} = \dfrac{q_{ij} - \hat{m}_{ij}}{\sqrt{\hat{m}_{ij}}}$. The DoF adjustment is $f^{DoF} = \sqrt{\dfrac{N}{N - p}}$, where $N$ is the number of data points and $p$ the number of parameters, applied to scale residuals upward.
What does a Bayesian credibility approach add to reserving compared with a purely frequentist chain ladder?
It blends prior beliefs (e.g., an a priori loss ratio or industry benchmark) with observed data through a posterior distribution, yielding a credibility-weighted estimate and a full predictive distribution of reserves rather than a single point. It naturally stabilizes immature/volatile years.
Express the Bühlmann credibility estimate and identify the credibility factor $Z$.
Estimate: $\hat{\mu} = Z\,\bar{X} + (1-Z)\,\mu$, with $Z = \dfrac{n}{n + k}$ and $k = \dfrac{\text{EPV}}{\text{VHM}} = \dfrac{E[\mathrm{Var}(X\mid\Theta)]}{\mathrm{Var}(E[X\mid\Theta])}$, where $n$ is the number of observations.
How is the Bornhuetter-Ferguson (BF) method a credibility blend, and what weights does it apply?
$\text{Ultimate} = \text{Actual reported} + (1 - \%\text{reported})\times \text{a priori ultimate}$. It gives weight $Z = \%\text{reported}$ to the chain-ladder (development) estimate and $(1-Z)$ to the a priori expected losses, so immature years lean on the prior.
In Clark's (2003) growth-curve reserving model (LDF and Cape Cod methods), what two growth curves are commonly used and what parameters describe them?
The Loglogistic $G(x)=\dfrac{x^{\omega}}{x^{\omega}+\theta^{\omega}}$ and the Weibull $G(x)=1-\exp\!\left(-(x/\theta)^{\omega}\right)$, where $\theta$ is the scale and $\omega$ the shape parameter. The model uses maximum likelihood under an over-dispersed Poisson assumption.
In Clark's model, how is the variance/scale parameter $\sigma^{2}$ estimated and what is its interpretation?
$\sigma^{2}=\dfrac{1}{n-p}\sum \dfrac{(c_i - \mu_i)^{2}}{\mu_i}$, the over-dispersion factor relating variance to mean ($\mathrm{Var}=\sigma^{2}\mu$). It scales the Poisson variance to match the observed scatter of incremental losses.
What are the two components of reserve estimation error in Clark's model, and how does parameter variance behave relative to process variance over time?
Process variance ($\sigma^{2}\times\text{reserve}$) and parameter variance (from the covariance matrix of the MLE parameters). For mature/older accident years parameter variance dominates; reserve estimates with longer tails carry more parameter uncertainty.
Distinguish a reserve 'range' from a 'range of reasonable estimates' from a 'distribution of possible outcomes'.
A range of reasonable estimates spans defensible point estimates a reserving actuary could select (a narrow band of central estimates). A distribution of possible outcomes captures the full variability of actual results (much wider). A range of reasonable estimates is NOT the same as a confidence interval of outcomes.
According to the CAS (Working Party on Quantifying Variability), what are the main sources of reserve uncertainty?
Process risk (random future claim variation), parameter risk (error in estimated parameters), model risk (wrong model/specification), and systemic/internal & external systematic risks. Total uncertainty must reflect all of these, not just process risk.
Why can a method that reproduces the chain-ladder point estimate still produce materially different reserve ranges?
The point estimate depends only on the mean structure (development factors), whereas the range depends on the assumed variance/correlation structure, tail behavior, and treatment of parameter and model risk—so different stochastic models (Mack, ODP bootstrap, Clark) can agree on the mean but differ widely on the spread.
What is the key driver that makes excess/reinsurance loss reserving harder than primary reserving (per Siewert / Patrik)?
Excess layers have much longer reporting and payment tails, lower claim frequency with high severity (sparse, volatile data), greater leverage from trend and development, and report lags—so development factors are larger, less stable, and require exposure-based or industry benchmarks.
In reinsurance reserving, define the difference between 'reporting pattern' and 'settlement/payment pattern' and why the report lag matters.
The reporting pattern describes how claims are reported to the reinsurer over time (often delayed because primary insurer must breach the retention first); the payment pattern describes cash settlement. Long report lags mean a large share of ultimate losses are pure IBNR for many years, increasing reliance on a priori methods like BF.
What is the 'leveraged effect' of trend (inflation) on excess layers?
Loss trend inflates excess-of-retention losses at a higher rate than ground-up losses, because a uniform percentage increase in all claim sizes pushes more claims over a fixed retention and increases the excess portion disproportionately. The excess trend rate exceeds the ground-up trend rate.
Give the formula for the expected loss in an excess layer with attachment $a$ and limit $l$ (layer $a$ xs $a$ to $a+l$) using the limited expected value function.
Layer losses $= E[(X\wedge (a+l))] - E[(X\wedge a)]$, where $E[X\wedge d]=\int_0^{d} S(x)\,dx = \int_0^{d}\big(1-F(x)\big)\,dx$ is the limited expected value at limit $d$.
Show the other 41 Exam 7 — Estimation of Policy Liabilities, Insurance Company Valuation, and Enterprise Risk Management flashcards
Define the increased limits factor (ILF) and the loss elimination ratio (LER) in terms of limited expected values.
$\text{ILF}(L)=\dfrac{E[X\wedge L]}{E[X\wedge B]}$ relative to a basic limit $B$ (assuming losses-only / no risk load). $\text{LER}(d)=\dfrac{E[X\wedge d]}{E[X]}$, the fraction of ground-up losses eliminated by a deductible $d$.
What is a consistency requirement among increased limits factors, and why?
ILFs must increase at a decreasing rate as the limit rises (the marginal premium for each additional layer must be non-increasing), i.e., the ILF curve is concave. Otherwise an insured could buy a higher limit cheaper by combining layers — an arbitrage / inconsistency.
Compare proportional (quota share, surplus share) versus non-proportional (excess-of-loss, stop-loss) reinsurance in one sentence each.
Proportional: reinsurer shares a fixed percentage of premiums and losses (quota share = constant %; surplus share = % varies by policy size relative to retention line). Non-proportional: reinsurer pays only losses above a retention—per-risk/per-occurrence XOL, or aggregate stop-loss on the whole book.
What is the difference between a working-layer, exposed, and clash (or higher) excess reinsurance treaty regarding loss frequency and credibility?
Working-layer treaties attach low, with frequent losses and credible experience (experience-rated). Higher/clash layers attach far above per-risk limits, are loss-free most years, driven by multi-claim/multi-policy events, and must be exposure-rated since experience is non-credible.
List the main reasons (functions) an insurer purchases reinsurance.
(1) Capacity—write larger limits; (2) Stabilization of results (smooth volatility); (3) Catastrophe protection; (4) Surplus relief / financing (improve leverage ratios); (5) Underwriting/market entry & exit assistance; (6) Access to reinsurer expertise.
In experience rating an excess treaty, what adjustments must be applied to historical losses before they can be projected to the future layer?
Trend each loss to the future cost level, apply loss development (to ultimate) including the leveraged excess development, restate to the prospective retention/limit, and adjust exposures (on-level for rate/exposure changes) before computing the burning cost and loading for expenses and risk.
What is the burning cost (experience) rate and how is it converted to a treaty rate?
Burning cost $=\dfrac{\text{trended, developed excess losses}}{\text{subject premium}}$. The treaty rate $=\dfrac{\text{expected excess losses}}{\text{subject premium}}\div(1-\text{expense/profit loading})$, i.e., burning cost loaded for expenses, risk margin, and brokerage.
Define ALAE treatment options 'pro rata' versus 'in addition to' the limit in an excess reinsurance contract.
'Pro rata' (in proportion): ALAE is shared in the same ratio as the loss falls in the layer (reinsurer pays ALAE × layer loss / total loss). 'In addition': ALAE is added to indemnity and the sum is applied against the retention/limit, increasing reinsurer exposure. They produce different recoveries for losses near the attachment.
What distinguishes economic value, fair (market-consistent) value, and statutory/GAAP book value in insurance company valuation?
Economic/intrinsic value discounts expected future cash flows at a risk-appropriate rate; fair value is the market-consistent exchange price (often using risk-neutral valuation); statutory/GAAP book value follows accounting rules with conservatism (statutory) or matching (GAAP) and may not equal economic value.
State the appraisal (actuarial) value of an insurer and its three components.
Appraisal value $=$ Adjusted net worth (ANW) $+$ Value of existing/in-force business (PV of future earnings from current book) $+$ Value of future new business (goodwill). It represents the economic worth to an acquirer.
What is embedded value (EV) and how does it differ from appraisal value?
Embedded value $=$ Adjusted net worth $+$ Value of in-force business (present value of future shareholder earnings from existing policies, net of cost of capital). It excludes the value of future new business, which appraisal value includes as goodwill.
Define the Dividend Discount Model (DDM) valuation of an insurer's equity.
$V_0 = \sum_{t=1}^{\infty} \dfrac{D_t}{(1+r_e)^{t}}$, where $D_t$ are distributable earnings (dividends to shareholders, constrained by required capital) and $r_e$ is the cost of equity. Often split into an explicit forecast period plus a terminal value.
Write the Capital Asset Pricing Model (CAPM) and explain its use in setting an insurer's cost of equity.
$r_e = r_f + \beta\,(E[r_m] - r_f)$, where $r_f$ is the risk-free rate, $\beta$ the systematic risk of the insurer's equity, and $(E[r_m]-r_f)$ the market risk premium. It supplies the discount rate / target return for pricing and valuation.
State the insurance CAPM (underwriting-beta) formula for the fair underwriting profit margin.
$r_{uw} = -k\,r_f + \beta_{uw}\,(E[r_m]-r_f)$, where $k$ is the funds-generating (premium-to-surplus reserve) coefficient, $r_f$ the risk-free rate, and $\beta_{uw}$ the underwriting beta. The $-k\,r_f$ term credits investment income on policyholder-supplied funds.
Give the weighted average cost of capital (WACC) formula.
$\text{WACC} = \dfrac{E}{V}\,r_e + \dfrac{D}{V}\,r_d\,(1-\tau)$, where $E$ and $D$ are market values of equity and debt, $V=E+D$, $r_e$ and $r_d$ the costs of equity and debt, and $\tau$ the tax rate.
What is the risk-adjusted discounted cash flow (DCF) approach to pricing, and what cash flows are discounted?
Project the policyholder and shareholder cash flows—premiums, losses, expenses, taxes, and the cost of holding required capital—and discount them at a risk-adjusted rate (or risk-neutral with risk-free). The premium is set so the NPV of shareholder cash flows equals zero (target return achieved).
Describe the internal rate of return (IRR) pricing model for insurance.
Treat the policy from the equityholder's view: capital is contributed (outflow) and released over time with underwriting and investment earnings (inflows). The IRR is the rate making the NPV of equity flows zero; the premium is adequate if IRR $\geq$ the target cost of equity.
In the risk-adjusted return on capital framework, define RAROC and the decision rule.
$\text{RAROC} = \dfrac{\text{risk-adjusted return (e.g., expected profit net of expected loss)}}{\text{economic (risk) capital}}$. Accept/retain the business or allocate more capital when $\text{RAROC} \geq$ the hurdle rate (cost of capital); it puts all units on a common risk-adjusted footing.
What is Return on Equity (ROE) and how is it decomposed via DuPont for an insurer?
$\text{ROE}=\dfrac{\text{Net income}}{\text{Equity}}$. DuPont: $\text{ROE}=\text{Profit margin}\times\text{Asset turnover}\times\text{Leverage}=\dfrac{NI}{\text{Rev}}\times\dfrac{\text{Rev}}{\text{Assets}}\times\dfrac{\text{Assets}}{\text{Equity}}$. For insurers leverage is largely premium-to-surplus and reserve leverage.
Define the combined ratio and operating ratio, and what each tells you.
$\text{Combined ratio}=\dfrac{\text{Losses+LAE}}{\text{Earned premium}}+\dfrac{\text{Underwriting expenses}}{\text{Written (or earned) premium}}$ = loss ratio + expense ratio. $\text{Operating ratio}=\text{Combined ratio}-\dfrac{\text{Investment income}}{\text{Earned premium}}$. Combined $<100\%$ means underwriting profit; operating ratio adds investment income.
Why can economic value added (EVA) be preferred to accounting ROE for performance measurement?
$\text{EVA}=\text{NOPAT}-r\times\text{Capital}$, charging for the cost of capital used. Unlike ROE it is a dollar measure that rewards growth only when returns exceed the cost of capital, discourages capital-destroying growth, and aligns with shareholder value creation.
What is the COSO definition of Enterprise Risk Management (ERM)?
ERM is a process, effected by an entity's board, management, and personnel, applied across the enterprise and in strategy-setting, designed to identify potential events that may affect the entity, manage risk within its risk appetite, and provide reasonable assurance regarding achievement of objectives.
List the four broad categories of risk in an ERM risk classification (commonly used by rating agencies/regulators).
(1) Market risk; (2) Credit risk; (3) Insurance/underwriting risk (incl. reserving and pricing/cat risk); (4) Operational risk. Some frameworks add strategic risk and liquidity risk as distinct categories.
Distinguish a pure risk-management view from a value-based (strategic) view of ERM.
The traditional/pure view treats ERM as loss control—reducing the probability and severity of adverse events to protect solvency. The value-based view treats risk as a resource to be optimized, balancing risk and return to maximize firm value and inform strategy (taking the 'right' risks, not just avoiding risk).
What are the common methods of risk identification in an ERM program?
Risk workshops/brainstorming, structured questionnaires and surveys, risk registers/checklists, process flow analysis, scenario analysis and stress testing, loss-event databases, SWOT/PESTLE, and reverse stress testing (start from failure and find causes).
Define Value at Risk (VaR) and Tail Value at Risk (TVaR/CTE).
$\text{VaR}_p(X)=$ the $p$-quantile of the loss distribution, $\inf\{x: F_X(x)\geq p\}$. $\text{TVaR}_p(X)=E[X\mid X>\text{VaR}_p(X)]$ (for continuous $X$), the average loss in the worst $(1-p)$ tail. TVaR is more sensitive to extreme tail severity than VaR.
What is a coherent risk measure, and which of VaR and TVaR is coherent?
A coherent measure $\rho$ satisfies monotonicity, translation invariance, positive homogeneity, and subadditivity ($\rho(X+Y)\leq\rho(X)+\rho(Y)$). TVaR is coherent; VaR is not, because it can violate subadditivity (it fails to always reward diversification).
In risk aggregation, how does the choice of copula affect tail dependence and required capital?
A copula links marginals into a joint distribution; the Gaussian copula has zero tail dependence (extremes nearly independent), while the t-copula and Gumbel (Archimedean) copulas exhibit upper-tail dependence (joint extremes more likely), producing fatter aggregate tails and higher diversified capital requirements.
What is risk-based capital (RBC) in the U.S. and the formula relationship among its risk components?
RBC is a regulatory minimum capital based on a company's risk profile. The authorized control level uses a square-root covariance/aggregation: $\text{RBC} = R_0 + \sqrt{R_1^{2}+R_2^{2}+R_3^{2}+R_4^{2}+R_5^{2}}$, recognizing diversification among asset, credit, reserving, premium, and off-balance-sheet risks.
Define the RBC ratio and the regulatory action levels.
$\text{RBC ratio}=\dfrac{\text{Total Adjusted Capital}}{\text{Authorized Control Level RBC}}$. Action levels (as % of ACL or of the ratio): Company Action ($<200\%$ of ACL, i.e. ratio $<2$), Regulatory Action ($<150\%$), Authorized Control ($<100\%$), Mandatory Control ($<70\%$).
Contrast the Solvency II three-pillar structure with U.S. solvency regulation.
Solvency II Pillar 1: quantitative capital (SCR via standard formula or internal model, calibrated to 99.5% VaR over 1 year, plus MCR); Pillar 2: governance, ORSA, supervisory review; Pillar 3: disclosure/reporting. U.S. uses factor-based RBC, statutory accounting, and ORSA but is less market-consistent and not a single VaR calibration.
What is ORSA and what is its purpose?
The Own Risk and Solvency Assessment is an internal process (required by the NAIC ORSA Model Act and Solvency II) in which the insurer assesses its own risk profile, risk management adequacy, and current and prospective solvency under normal and stressed conditions, reporting the results to its board and regulator.
Define the Solvency II Solvency Capital Requirement (SCR) calibration and the Risk Margin concept.
SCR is the economic capital to absorb losses at a $99.5\%$ VaR (1-in-200) over a one-year horizon. Technical provisions $=$ best-estimate liabilities $+$ risk margin, where the risk margin is a cost-of-capital margin: $\text{RM}=\text{CoC}\times\sum_t \dfrac{\text{SCR}_t}{(1+r_{t+1})^{t+1}}$ (CoC typically $6\%$).
What is economic capital and how does it differ from regulatory capital?
Economic capital is the amount of capital a firm internally determines it needs to remain solvent at a chosen risk tolerance (e.g., a target probability of ruin or rating) over a defined horizon, using the firm's own models. Regulatory capital is the externally-prescribed minimum (RBC, SCR); economic capital is internal, risk-sensitive, and often used for allocation and pricing.
Describe the cost-of-capital method for determining a risk margin (market value margin) on reserves.
Project the capital required to support the runoff of the liabilities each future year, multiply each by a cost-of-capital rate (e.g., $6\%$) representing the return demanded above risk-free, and discount: $\text{Risk margin}=\sum_t \dfrac{\text{CoC}\times C_t}{(1+r)^{t+1}}$, where $C_t$ is required capital at time $t$.
What is the leverage (premium-to-surplus) ratio and why do regulators monitor it?
$\text{Premium-to-surplus ratio}=\dfrac{\text{Net written premium}}{\text{Policyholder surplus}}$. High leverage (commonly a benchmark of $\approx 3{:}1$) signals that a small adverse loss or reserve shock can impair surplus, so it is a key solvency early-warning (IRIS) ratio.
Define the cost-of-capital (risk load) approach to allocating capital by line via the marginal/Myers-Read method.
Capital is allocated so each line's marginal contribution to the firm's default value (option value of default) is covered. Myers-Read allocates the total default cost by each line's marginal effect on the insurer's overall default option, summing exactly to total capital (additive surplus allocation).
What is the difference between the chain-ladder and Cape Cod (Stanard-Bühlmann) methods for estimating ultimate losses?
Chain ladder relies solely on development factors applied to reported losses (heavy weight on immature data). Cape Cod estimates an a priori expected loss ratio from the data itself—$\hat{ELR}=\dfrac{\sum \text{reported losses}}{\sum (\text{premium}\times \% \text{reported})}$—then applies BF logic, producing a more stable result by using exposure (used-up premium).
What is model risk in reserving and how can actuaries mitigate it?
Model risk is the risk that the selected model is structurally wrong or mis-specified (wrong distribution, missing trends, inappropriate assumptions). Mitigation: use multiple methods/models, back-test and validate against actual emergence, perform sensitivity testing, apply expert judgment, and reconcile differences across methods.
State Stone's criteria (desirable properties) for a good risk measure used in capital/risk-load contexts.
A useful risk measure should reflect the relevant features of the loss distribution (e.g., capture tail and dispersion), be consistent/sub-additive to reward diversification, be objective and computable, and align with the firm's risk preferences and the decision being made (pricing vs. solvency).
How do you compute the discounted (present value) reserve, and why might a risk margin then be added?
$\text{PV reserve}=\sum_t \dfrac{\text{expected payment}_t}{(1+r)^{t}}$ using the payment pattern and a discount rate $r$. Because discounting removes the implicit conservatism of nominal reserves and ignores uncertainty, a risk/market-value margin is added so the carried liability reflects the cost of bearing the runoff risk.
Casualty Actuarial Society Credentials (ACAS/FCAS) Exam 7 — Estimation of Policy Liabilities, Insurance Company Valuation, and Enterprise Risk Management FAQ
What is in the Casualty Actuarial Society Credentials (ACAS/FCAS) Exam 7 — Estimation of Policy Liabilities, Insurance Company Valuation, and Enterprise Risk Management syllabus?
Casualty Actuarial Society Credentials (ACAS/FCAS) Exam 7 — Estimation of Policy Liabilities, Insurance Company Valuation, and Enterprise Risk Management is split into 4 chapters: Advanced Reserving Techniques, Reinsurance Reserving and Pricing, Insurance Company Valuation and Enterprise Risk Management. Together they contain 12 topics and 25 sub-topics.
How many chapters are in Casualty Actuarial Society Credentials (ACAS/FCAS) Exam 7 — Estimation of Policy Liabilities, Insurance Company Valuation, and Enterprise Risk Management?
4 chapters, containing 12 topics and 25 sub-topics. The largest chapter is Advanced Reserving Techniques, with 3 topics.
Are there flashcards for Casualty Actuarial Society Credentials (ACAS/FCAS) Exam 7 — Estimation of Policy Liabilities, Insurance Company Valuation, and Enterprise Risk Management?
Yes — a 61-card Exam 7 — Estimation of Policy Liabilities, Insurance Company Valuation, and Enterprise Risk Management deck, printed in full in the flashcards section of this page and available in the Examius app with spaced repetition. The free plan covers one exam of your choice, including its flashcards; Examius Pro, a one-time purchase, unlocks every other exam and deck.