Estimating Future Discretionary Benefits Without Monte Carlo Simulation

2026-02-12 · Source: HackerNoon · Field: Finance & Economics — Insurance & Risk Management, Banking & Financial Services, Actuarial Science · Depth: Expert, medium

Summary

This content details a method for estimating future discretionary benefits (F DB) in life insurance portfolios, particularly for run-off scenarios. It introduces a technique to derive lower (LBd) and upper (UBd) bounds for F DB without requiring Monte Carlo simulations, relying instead on a priori computations of terms I, II, III, and COG from representation (2.32). The estimation process involves specific assumptions (4.1, 4.2, 4.3, 4.4, 4.5, 4.6, 4.7) to model components like the cost of guarantee (COG) using the Black formula (5.56) for floorlets. The derived bounds are then used to calculate an estimator for F DB (5.60). The methodology is validated by comparing its results against numerically calculated values from a realistic, anonymized life insurance portfolio, demonstrating good agreement, especially relative to the initial market value MV0.

Key takeaway

For AI Scientists developing actuarial models for life insurance, understanding this method for estimating future discretionary benefits (F DB) is crucial. Your models can incorporate these a priori bounds, reducing reliance on computationally intensive Monte Carlo simulations for initial assessments. Focus on validating your model's underlying assumptions against empirical data to ensure the stability and accuracy of your F DB estimates, particularly for run-off portfolios.

Key insights

Future discretionary benefits can be bounded and estimated a priori without Monte Carlo simulations using specific actuarial assumptions.

Principles

• A priori bounds for F DB are computable.
• Model calibration impacts estimation accuracy.
• Simpler assumptions can offer stability over sharpness.

Method

Estimate F DB by calculating lower (LBd) and upper (UBd) bounds based on terms I, II, III, and COG, using specific actuarial assumptions and the Black formula for floorlets, then average the bounds.

In practice

• Apply Black formula (5.56) for floorlet valuation.
• Use (5.60) as an F DB estimator.
• Validate estimates against numerical models.

Topics

• Future Discretionary Benefits
• Life Insurance Modeling
• Black Formula
• Cost of Guarantee
• Numerical ALM

Best for: AI Scientist, Research Scientist, Data Scientist, Domain Expert

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Editorial summary, takeaway, and curation by AIssential. Original article published by HackerNoon.