TECHNIQUES

This study examines the impact of credit risk modeling techniques on the performance of life insurance companies in Nigeria., focusing on a sample of 12 life insurance companies in Nigeria over the period 2018 to 2024. The variables analyzed included credit scoring, structural model and Altman Z-score model. Various statistical and econometric tools were applied to analyze the data. The findings revealed that credit scoring and Altman Z-score model have positive and statistically insignificant effects on the financial performance of life insurance companies in Nigeria, while the structural model has a negative and statistically insignificant influence on the financial performance of life insurance companies in Nigeria. Based on these findings, the study recommended that life insurance firms in Nigeria strengthen the adoption and integration of credit scoring systems using advanced data analytics and artificial intelligence and firms should review and customize structural models to better suit the operational and regulatory environment of the Nigerian insurance industry.

Natural gas, which can be found in conventional natural gas reservoirs as non-associated gas (NAG), associated gas (AG), or gas condensation products, is a combustible gaseous combination of gaseous hydrocarbons, very light liquid hydrocarbons, free water, water vapor, and other undesirable non- hydrocarbon gaseous and solid components. Water or water vapor is eliminated rom the streams of natural gas through the process of natural gas dehydration. Production, handling, and transportation of natural gas are hampered by the presence of water that is free in the gas. Therefore, it is imperative to eliminate a large portion moisture as feasible from the gas stream. While there are a number of ways to dehydrate natural gas, the following are the most idely used ones:

The purpose of this study was to examine the performance of five model-fitting estimation techniques; Likelihood Ratio Test (LRT), Akaike Information Criterion (AIC), Bayesian information criterion (BIC), Deviance Information Criterion (DIC) and Cross- Validation Log-Likelihood (CVLL) techniques that effectively selected an IRT dichotomous model which fitted NABTCE 2018 May/June Mathematics multiple choice test. This was carried out by comparing the performances of the five techniques used based on relative fit. Four research questions guided the study. No hypothesis was formulated and tested, due to the fact that the techniques used in this study were non-significant statistics. The research design employed was the descriptive survey of the ex-post facto method. The population of the study consisted of 49,581 candidates who sat for the National Business and Technical Certificate Examinations in 2018 in the six Geo-Political Zones in Nigeria. The sample size comprised 4,948 candidates and a statistical sample of 50 items. The Multistage simple random sampling technique was employed for randomly selecting the sample for the study. The instrument used to collect data was 50-item Mathematics multiple choice test from NBTCE May/June 2018. The instrument was a standardized instrument and as such it was valid and reliable. Item parameters were estimated from the examinees’ responses to the items using the computer programme BILOG-MG3. For the five estimation techniques BILOG-MG3 was used for LR, AIC and BIC. WinBUGS 1.4 was used for DIC, while MATLAB was used for CVLL which answered the research questions