Browsing by Author "Hoohlo, Mphekeleli"

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    Bancassurance on commercial banks and life insurance in the Southern African Development Community (SADC)
    (University of the Witwatersrand, Johannesburg, 2023) Mapena, Thabang; Hoohlo, Mphekeleli
    The increase in competition and the scramble for customers in the financial industry has led to the convergence of banking and insurance to form what is known as Bancassurance. Bancassurance, as the name suggests is the distribution of insurance products using bank platforms. Although two parties get into the bancassurance agreement in search of some mutual gains, it comes with unintended consequences which among other things affect the banking efficiency of banks. This study assesses the efficiency changes brought about by Bancassurance in the SADC commercial banks. Using Data Envelopment analysis with the return to scale, an analysis was done on 21 banks with active insurance income on a case and control methodology. The results showed insurance income having no impact on the regional banks’ efficiencies. The income insurance however had an impact on some banks’ returns to scale
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    A new internal data measure for operational risk: a case study of a South African bank
    (2015-03-12) Hoohlo, Mphekeleli
    This study examines the e ect of automation on operational risk (OpRisk) measurement in a South African bank. It uses historical process risk loss data from the rst quarter (2013Q1) derived from the automated trade amendment tracker (ATAT) database { a computerised tool designed to automate the collection of internally generated OpRisk events at the bank in question. The results indicate that a Value{at{Risk (VaR) estimate for OpRisk largely depends on the accuracy of the loss data. Capital adequacy is determined using this estimate of VaR, suggesting that the ATAT device used in operational risk measurement improves on investment services activity in South Africa. Finally, it appears that risk management practices in the South African banking industry are more concerned about traditional operational risk management (ORM) rather than the determination of OpRisk VaR as it becomes a matter of great concern for nancial institutions (FI's) across the globe.
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    Towards a framework for operational risk management in the banking sector
    (University of the Witwatersrand, Johannesburg, 2022) Hoohlo, Mphekeleli
    The objective of this study is three-pronged. One, it investigates the factors that influence capital adequacy as measured by the covariates (exposure, frequency and severity) used in banking operations that accompany firms data-log loss reports. Two, it assesses the differential impact of discretionary (by adding artificial data) and non-discretionary (using real world data) loss disclosure on firms’ value-at-risk. R software is used to determine the value-at-risk. GLM and GAMLSS techniques are employed and subsequent tests of significance derive aforementioned influential factors, accompanied by a data augmentation algorithm in Matlab software to determine the differential impact of artificial and real world operational loss disclosures on firms’ performance in relation to meeting capital requirements. Three, it challenges firms’ risk-neutral assumption inherent in operational risk practice, asserting that; in theory, banking operations are more risk averse. Rattle software is used in a k-means cluster analysis method to determine whether controls compensate for persistent losses due to the firms’ natural risk aversion. The research arrived at estimates on the number of losses and their sizes; whereby exposure positively influences the risk ceded by the bank having “learned” from possible variations in past data, therefore improving operational risk manage- ment frameworks by introducing ex ante forward-looking components, whereas the addition of artificial data points by data augmentation circumvents former dilemmas of large and rare events so one can do more “learning”, notwithstanding the nature of the data’s suspect quality as they are constructs not observations. Nevertheless, the artificial intelligent EBOR framework’s performance improves on (Hoohlo (2014)’s applied data scaling and parametization techniques arrived at a proxy of about ZAR3B) former techniques for capital adequacy calculation of OpRisk opening up exploration modeling beyond historical accounts of significance to incorporate forward-looking aspects. Furthermore, checks and balances set up based on operational negligence slow down operational risk losses over time thereby establishing the move of firm risk tolerance levels away from risk neutrality, suggesting that banks are more risk averse