Risk Management eJournal最新文献_第6页

Understanding the Importance and Practices of Operational Risk Management in Ghanaian Banks 理解加纳银行操作风险管理的重要性和实践

Risk Management eJournal Pub Date : 2021-02-05 DOI: 10.37227/jibm-2020-04-124/

Maxwell Dela Yao Gakpo

{"title":"Understanding the Importance and Practices of Operational Risk Management in Ghanaian Banks","authors":"Maxwell Dela Yao Gakpo","doi":"10.37227/jibm-2020-04-124/","DOIUrl":"https://doi.org/10.37227/jibm-2020-04-124/","url":null,"abstract":"This quantitative study examines the importance and practices of operational risk management in Ghanaian banks. The objective of this paper is to investigate the practitioner’s understanding of the difference (if any) between importance and practices of operational risk management programs in Ghanaian banks using a survey research design. The survey instrument, used in collecting data from 170 respondents, was duly validated. The results confirmed that there exists a high level of ORM awareness among the respondents. The banks deploy different risk management solutions to control and mitigate operational risk. The study also concluded that there was a clear difference between perceive ORM importance and ORM practices. This meant that there exists a gap between ORM awareness and ORM program practices. The lack of effective implementation of ORM programs are likely to cause an operational risk contagion among banks with very catastrophic impact on the whole financial sector. The study therefore, recommends that bank mangers commensurate ORM awareness creation with practical implementation of ORM programs and policies in their banks to mitigate operational risk hazards.","PeriodicalId":306152,"journal":{"name":"Risk Management eJournal","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131038072","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Three-Factor Commodity Forward Curve Model and Its Joint P and Q Dynamics 三因素商品远期曲线模型及其联合P和Q动力学

Risk Management eJournal Pub Date : 2021-02-05 DOI: 10.2139/ssrn.3780286

S. Ladokhin, S. Borovkova

引用次数: 6

An Explanation Framework for Interpretable Credit Scoring 可解释信用评分的解释框架

Risk Management eJournal Pub Date : 2021-01-31 DOI: 10.5121/IJAIA.2021.12102

Lara Marie Demajo, Vince Vella, A. Dingli

{"title":"An Explanation Framework for Interpretable Credit Scoring","authors":"Lara Marie Demajo, Vince Vella, A. Dingli","doi":"10.5121/IJAIA.2021.12102","DOIUrl":"https://doi.org/10.5121/IJAIA.2021.12102","url":null,"abstract":"With the recent boosted enthusiasm in Artificial Intelligence (AI) and Financial Technology (FinTech), applications such as credit scoring have gained substantial academic interest. However, despite the ever growing achievements, the biggest obstacle in most AI systems is their lack of interpretability. This deficiency of transparency limits their application in different domains including credit scoring. Credit scoring systems help financial experts make better decisions regarding whether or not to accept a loan application so that loans with a high probability of default are not accepted. Apart from the noisy and highly imbalanced data challenges faced by such credit scoring models, recent regulations such as the `right to explanation' introduced by the General Data Protection Regulation (GDPR) and the Equal Credit Opportunity Act (ECOA) have added the need for model interpretability to ensure that algorithmic decisions are understandable and coherent. A recently introduced concept is eXplainable AI (XAI), which focuses on making black-box models more interpretable. In this work, we present a credit scoring model that is both accurate and interpretable. For classification, state-of-the-art performance on the Home Equity Line of Credit (HELOC) and Lending Club (LC) Datasets is achieved using the Extreme Gradient Boosting (XGBoost) model. The model is then further enhanced with a 360-degree explanation framework, which provides different explanations (i.e. global, local feature-based and local instance- based) that are required by different people in different situations. Evaluation through the use of functionally-grounded, application-grounded and human-grounded analysis shows that the explanations provided are simple and consistent as well as correct, effective, easy to understand, sufficiently detailed and trustworthy.","PeriodicalId":306152,"journal":{"name":"Risk Management eJournal","volume":"150 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122464123","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4

Reinventing Pareto: Fits for All Losses, Small and Large 重塑帕累托:适合所有损失，无论大小

Risk Management eJournal Pub Date : 2021-01-28 DOI: 10.2139/ssrn.3775007

Michael Fackler

引用次数: 2

Does Mixed Frequency Information Help To Forecast the Value at Risk of the Crude Oil Market? 混合频率信息是否有助于预测原油市场的风险价值?

Risk Management eJournal Pub Date : 2021-01-28 DOI: 10.2139/ssrn.3774891

Yongjian Lyu, Mengzhen Kong, Rui Ke, Yu Wei

引用次数: 0

Three-Layer Problems and the Generalized Pareto Distribution 三层问题与广义Pareto分布

Risk Management eJournal Pub Date : 2021-01-24 DOI: 10.2139/ssrn.3772372

Michael Fackler