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All Journal Mikailalsys Journal of Mathematics and Statistics
Olalekan, Owoade Olusegun
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Engineering Mathematics Mechanical Engineering

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Quantitative Assessment of Interest Rate Fluctuation Sensitivity in Nigerian Insurance Asset-Liability Management Adewale, Taiwo Abiodun; Tinuoye, Oladipo Abiodun; Adebayo, Ajala Olusegun; Oluwaseyi, Olaiya Olumide; Olalekan, Owoade Olusegun; Damilare, Olaleye Peter
Mikailalsys Journal of Mathematics and Statistics Vol 3 No 3 (2025): Mikailalsys Journal of Mathematics and Statistics
Publisher : Darul Yasin Al Sys

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58578/mjms.v3i3.7182

Abstract

This study investigates the sensitivity of insurance portfolios to interest rate fluctuations in Nigerian insurance companies, with particular focus on the implications for asset and liability valuation. The objective is to assess how interest rate variability affects the relative sensitivities of assets and liabilities, and the resulting solvency risks. A quantitative approach was adopted, using a sample of ten insurance companies selected based on asset base and data availability. Data covering a ten-year period (2013–2023) were obtained from published financial statements and Central Bank of Nigeria interest rate bulletins. Analytical techniques included stochastic simulations and regression modeling, applying the Vasicek and Heston frameworks, with visualization performed using Python 3.12.3. The results show that liabilities exhibit greater sensitivity to interest rate fluctuations than assets, with pronounced volatility under stress scenarios, thereby creating significant solvency challenges. These findings validate the importance of dynamic stochastic models in capturing the complexities of interest rate effects, as opposed to static mathematical assumptions. The study concludes that effective asset–liability management (ALM) requires robust dynamic interest rate modeling. Theoretical contributions include extending the application of stochastic differential equations to emerging market contexts, while practical recommendations urge insurance regulators and investment managers to adopt interest rate-sensitive frameworks for risk management and capital adequacy assessments. Future research is recommended on macroeconomic stress factors and stochastic volatility models tailored to African financial markets.
On the Closed-Form Characterisation of the Impact of Risk Misprofiling on Optimal Nigerian Insurance Pricing Models Adewale, Taiwo Abiodun; Tinuoye, Oladipo Abiodun; Adebayo, Ajala Olusegun; Oluwaseyi, Olaiya Olumide; Olalekan, Owoade Olusegun; Damilare, Olaleye Peter
Mikailalsys Journal of Mathematics and Statistics Vol 4 No 1 (2026): Mikailalsys Journal of Mathematics and Statistics
Publisher : Darul Yasin Al Sys

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58578/mjms.v4i1.7597

Abstract

This study addresses the underexplored issue of risk mis-profiling in optimal insurance pricing models and its implications for solvency and regulatory compliance within the insurance industry. It aims to mathematically analyse the effects of classification errors on premium determination, quantify pricing deviations, and assess sensitivity to misclassification biases. Adopting a quantitative research design, the study utilises insurance data spanning 2010–2020, with computational implementation in Python 3.12.3 (2025) and calibration in Weka 3.9.6 (2022). Policyholders were categorised into low-, medium-, and high-risk groups using confusion matrices, while premiums were derived under exponential utility and deterministic-equivalent principles. Analytical techniques included cumulant generating function expansions, Taylor–Lagrange remainder approximations, and optimisation frameworks. The results indicate that even minor classification errors significantly influence premium estimates, particularly due to exponential tilting, variance underestimation, and tail sensitivity. These distortions align with theoretical expectations and highlight solvency vulnerabilities when premiums fall below actuarially fair values. The study concludes that systematic mis-profiling introduces pricing inefficiencies and potential insolvency triggers. Theoretical contributions include the extension of utility-based pricing principles to account for classification uncertainty, while practical implications call for insurers and regulators to adopt robust pricing adjustments, monitor classifier accuracy, and integrate misclassification-aware pricing mechanisms. Future research directions include extending the framework to portfolio-level analysis, applying robust stochastic optimisation, and investigating the effects of machine learning classification errors on pricing precision.
Co-Authors Adebayo, Ajala Olusegun Adewale, Taiwo Abiodun Damilare, Olaleye Peter Oluwaseyi, Olaiya Olumide Tinuoye, Oladipo Abiodun