Comparison of Survival Analysis Approaches to Modelling Credit Risks

Mungasi, Sammy

doi:10.11648/j.ajtas.20190802.11

Cited by 3 publications

(1 citation statement)

References 14 publications

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“…According to [5] on the study about modelling of credit risks by use of survival approaches where they fitted different survival models into the Kenyan credit data and systematically compare the performance of models. The research attempted to evaluate and give recommendations on the best survival model in credit risk context in relation American Journal of Applied Mathematics and Statistics 97 to the Kenyan data.…”

Section: Review Of Previous Research On Extended Cox Modelmentioning

confidence: 99%

Extended Cox Model on Duration Taken to Release Cargo at Kenyan Border Entry Point: A Case Study of Malaba Osbp

Emoru¹,

Chelule²,

Imboga³

et al. 2020

AJAMS

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Duration taken to release cargo is one of World Customs Organisation's most significant instruments of measuring trade facilitation. It is a way of reviewing clearance procedure by measuring the time taken to release cargo at border entry points. This research therefore aimed to model clearance time using the Extended Cox model, which is a modification of the proportional hazard Cox model in which proportional hazard assumptions are not met, and tested the practicability of the model and compared the performance of Extended Cox model with the other conventional methods using secondary data on time clearance that was obtained from the KRA, Malaba OSBP, for the period 1st January 2015 to 31st July 2019. The variables in the study were both time depend (time taken from registration to release) and time independent (customs regimes, origin, destination, customs value and description of goods). The variables used in the research project were tested for proportional hazard assumptions. Extended Cox model was the most suitable model for analysis duration taken to release cargo data has it was found to have a lower AIC and BIC as compared to conventional cox model. Therefore, customs administration should embrace the use of Extended Cox model to analysis duration taken to release cargo data as it is statistically significant and give efficient results that are based on proper statistical methods. It is recommended that further studies to be done on a large data set that encompasses both entry and exit cargo in all the Kenyan border points.

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Section: Review Of Previous Research On Extended Cox Modelmentioning

confidence: 99%

Extended Cox Model on Duration Taken to Release Cargo at Kenyan Border Entry Point: A Case Study of Malaba Osbp

Emoru¹,

Chelule²,

Imboga³

et al. 2020

AJAMS

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Personal Loan Default Prediction Based on LightGBM Model and Zhima Credit

Zhang,

Liu

2023

Proceedings of the 2023 3rd Guangdong-Hong Kong-Macao Greater Bay Area Artificial Intelligence and Big Data Forum

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Deep Fuzzy Credibility Surfaces for Integrating External Databases in the Estimation of Operational Value at Risk

Peña,

Sepúlveda-Cano,

Gonzalez-Ruiz

et al. 2024

Sci

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Operational risk (OR) is usually caused by losses due to human errors, inadequate or defective internal processes, system failures, or external events that affect an organization. According to the Basel II agreement, OR is defined by seven risk events: internal fraud, external fraud, labor relations, clients, damage to fixed assets, technical failures and failures in the execution and administration of processes. However, the low frequency with which a loss event occurs creates a technological challenge for insurers in estimating the operational value at risk (OpVar) for the protection derived from an organization’s business activities. Following the above, this paper develops and analyzes a Deep Fuzzy Credibility Surface model (DFCS), which allows the integration in a single structure of different loss event databases for the estimation of an operational value at risk (OpVar), overcoming the limitations imposed by the low frequency with which a risk event occurs within an organization (sparse data). For the estimation of OpVar, the DFCS model incorporates a novel activation function based on the generalized log-logistic function to model random variables of frequency and severity that define a loss event (linguistic random variables), as well as a credibility surface to integrate the magnitude and heterogeneity of losses in a single structure as a result of the integration of databases. The stability provided by the DFCS model could be evidenced through the structure exhibited by the aggregate loss distributions (ALDs), which are obtained as a result of the convolution process between frequency and severity random variables for each database and which are expected to achieve similar structures to the probability distributions suggested by Basel II agreements (lean, long tail, positive skewness) against the OR modeling. These features make the DFCS model a reference for estimating the OpVar to protect the risk arising from an organization’s business operations by integrating internal and external loss event databases.

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Comparison of Survival Analysis Approaches to Modelling Credit Risks

Cited by 3 publications

References 14 publications

Extended Cox Model on Duration Taken to Release Cargo at Kenyan Border Entry Point: A Case Study of Malaba Osbp

Extended Cox Model on Duration Taken to Release Cargo at Kenyan Border Entry Point: A Case Study of Malaba Osbp

Personal Loan Default Prediction Based on LightGBM Model and Zhima Credit

Deep Fuzzy Credibility Surfaces for Integrating External Databases in the Estimation of Operational Value at Risk

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