This thesis consists of four independent chapters on bank capital regulation and the issue of unsolicited ratings.<p><p>The first chapter is introductory and reviews the motivation for regulating banks and credit rating agencies while providing a detailed overview of the thesis.<p><p>The second chapter uses a simultaneous equations model to analyze how banks from six G10 countries adjusted their capital to assets ratios and risk-weighted assets to assets ratio between 1988 and 1995, i.e. just after passage of the 1988 Basel Accord. The results suggest that regulatory pressure brought about by the 1988 capital standards had little effect on both ratios for weakly capitalized banks, except in the US. In addition, the relation between the capital to assets ratios and the risk-weighted assets to assets ratio appears to depend not only on the level of capitalization of banks, but also on the countries or groups of countries considered.<p><p>The third chapter provides Monte Carlo estimates of the amount of regulatory capital that EMU banks must hold for their corporate, bank, and sovereign exposures both under Basel I and the standardized approach to credit risk in Basel II. In the latter case, Monte Carlo estimates are presented for different combinations of external credit assessment institutions (ECAIs) that banks may choose to risk weight their exposures. Three main results emerge from the analysis. First, although the use of different ECAIs leads to significant differences in minimum capital requirements, these differences never exceed, on average, 10% of EMU banks’ capital requirements for corporate, bank, and sovereign exposures. Second, the standardized approach to credit risk provides a small regulatory capital incentive for banks to use several ECAIs to risk weight their exposures. Third, the minimum capital requirements for the corporate, bank, and sovereign exposures of EMU banks will be higher in Basel II than in Basel I. I also show that the incentive for banks to engage in regulatory arbitrage in the standardized approach to credit risk is limited.<p><p>The fourth and final chapter analyses the effect of soliciting a rating on the rating outcome of banks. Using a sample of Asian banks rated by Fitch Ratings, I find evidence that unsolicited ratings tend to be lower than solicited ones, after accounting for differences in observed bank characteristics. This downward bias does not seem to be explained by the fact that better-quality banks self-select into the solicited group. Rather, unsolicited ratings appear to be lower because they are based on public information. As a result, they tend to be more conservative than solicited ratings, which incorporate both public and non-public information.<p> / Doctorat en sciences économiques, Orientation économie / info:eu-repo/semantics/nonPublished

An investigation of the bombing of automated teller machines (ATMs) with intent to steal cash contentof ATMs. This is inclusive of the impact on society (banks clients) and banking institutions as well as the danger it poses to the general public and public and private law enforcement personnel. / (M.Tech. (Security Management))

The emerging financial industry of asset-backed short-term finance was investigated by this study. Literature indicated that banks, locally and globally, are forced by regulation and the use of information technology, to rely less on human judgement and more on programmed decision-making, when evaluating loan applications. This leads to time-consuming processes with non-standard loan applications and loss of opportunities for business persons. Asset-backed short-term finance is a market response to this tendency. Due to the emerging nature of this industry, no previous academic description of or investigation into this industry could be found – a gap in academic literature which this study aims to fill. The industry is strategically positioned in relation to banks by focusing on functionality for urgent non-standard loan applications (period between application and decision, and access to decision-makers) as value proposition, where banks are found lacking. Relatively high interest rates form the profit proposition, as firms in this industry have limited access to funds. Collateral is central as risk-mitigating strategy, forming a part of the profit proposition. The people proposition is essential, as the industry is distinguished by individualised decision-making. A survey among customers of this industry identified four clusters of potential customers: The first had no needs unfulfilled by banks, while the other three clusters were attracted by either functionality, or the evaluation of collateral in contrast to repayment ability, or a combination of the two. A survey among providers revealed hesitance to supply information and a low level of agreement on strategic matters – possibly due to the emergent nature of the industry. It is asserted that the basis for further study was laid. / Business Management / D. Com. (Business Management)

The purpose of this study was to examine the level of adoption, usage and factors that influence the adoption of electronic banking (e-banking) by small, micro and medium enterprises (SMMEs) listed in the City of Tshwane Metropolitan Municipality (CTMM). Despite efforts by commercial banks to promote e-banking (internet and cell phone banking) to its customers, the adoption rate for internet and cell phone banking appears to be low. Based on the literature reviewed, the SMME sector has been widely excluded from the formal banking services. It is also observed that e-banking can enable SMMEs to grow and enter international markets. Technology acceptance model (TAM) integrated with perceived risk theory (PRT) was used to investigate factors that influence adoption and usage of e-banking. The study followed a quantitative research approach. Respondents were selected using simple random sampling technique. A structured survey questionnaire was used to collect the data. The survey was conducted on 160 SMMEs in the CTMM with the assistance of fieldworkers. Data were analysed using descriptive statistics, exploratory factor analysis (EFA), Pearson’s bivariate correlation, and multiple regression. The results revealed that perceived ease of use had a significant positive influence on the attitude towards e-banking. Perceived usefulness had a positive but insignificant influence on the attitude towards e-banking. Security risk was the only perceived risk dimension that had a significant negative influence on attitude towards e-banking. However, financial risk, privacy risk, performance risk and social risk had a positive and insignificant influence on attitude towards e-banking. It was envisaged that this study will enable banks to develop strategies that are aimed at increasing their SMME market share. / Finance, Risk management and Banking / M. Com. (Finance)

An investigation of the bombing of automated teller machines (ATMs) with intent to steal cash contentof ATMs. This is inclusive of the impact on society (banks clients) and banking institutions as well as the danger it poses to the general public and public and private law enforcement personnel. / (M.Tech. (Security Management))

Abstracts in English, Zulu and Southern Sotho / The advent of Basel II Capital Accord has revolutionised credit risk measurement (CRM) to the extent that the once “perceived riskier bank assets” are now accommodated for lending. The Small and Medium Enterprise (SME) sector has been traditionally perceived as a riskier and unprofitable asset for lending activity by Commercial Banks, in particular. But empirical studies on the implementation of the Basel II internal-ratings-based (IRB) framework have demonstrated that SME credit risk is measurable. Banks are still finding it difficult to forecast SME loan default and to provide credit to the sector that meet Basel’s capital requirements. The thesis proposes to construct an empirical credit risk measurement (CRM) model, specifically for SMEs, to ameliorate the adverse effects of SME credit inaccessibility due to high information asymmetry between financial institutions (FI) and SMEs in Zimbabwe. A well-performing and accurate CRM helps FIs to control their risk exposure through selective granting of credit based on a thorough statistical analysis of historical customer data. This thesis develops a CRM model, built on a statistically random sample, known-good-bad (KGB) sample, which is a better representation of the through-the-door (TTD) population of SME loan applicants. The KGB sample incorporates both accepted and rejected applications, through reject inference (RI). A model-based bound and collapse (BC) reject inference methodology was empirically used to correct selectivity bias inherent in CRM domain. The results have shown great improvement in the classification power and aggregate supply of credit supply to the SME portfolio of the case-studied bank, as evidenced by substantial decrease of bad rates across models developed; from the preliminary model to final model designed for the case-studied bank. The final model was validated using both bad rate, confusion matrix metrics and Area under Receiver Operating Characteristic (AUROC) curve to assess the classification power of the model within-sample and out-of-sample. The AUROC for the final model (weak model) was found to be 0.9782 whilst bad rate was found to be 14.69%. There was 28.76% improvement in the bad rate in the final model in comparison with the current CRM model being used by the case-studied bank. / Isivumelwano seBasel II Capital Accord sesishintshe indlela yokulinganisa ubungozi bokunikezana ngesikweletu credit risk measurement (CRM) kwaze kwafika ezingeni lapho izimpahla ezazithathwa njengamagugu anobungozi “riskier bank assets” sezimukelwa njengesibambiso sokuboleka imali. Umkhakha wezamaBhizinisi Amancane naSafufusayo, phecelezi, Small and Medium Enterprise (SME) kudala uqondakala njengomkhakha onobungozi obukhulu futhi njengomkhakha ongangenisi inzuzo, ikakhulu njengesibambiso sokubolekwa imali ngamabhange ahwebayo. Kodwa izifundo zocwaningo ezimayelana nokusetshenziswa nokusetshenziswa kwesakhiwo iBasel II internal-ratings-based (IRB) sezikhombisile ukuthi ubungozi bokunikeza isikweletu kumabhizinisi amancane nasafufusayo (SME) sebuyalinganiseka. Yize kunjalo, amabhange asathola ukuthi kusenzima ukubona ngaphambili inkinga yokungabhadeleki kahle kwezikweletu kanye nokunikeza isikweletu imikhakha enemigomo edingekayo yezimali kaBasel. Lolu cwaningo beluphakamisa ukwakha uhlelo imodeli ephathekayo yokulinganisa izinga lobungozi bokubolekisa ngemali (CRM) kwihlelo lokuxhasa ngezimali ama-SME, okuyihlelo elilawulwa yiziko lezimali ezweni laseZimbabwe. Imodeli ye-CRM esebenza kahle futhi eshaya khona inceda amaziko ezimali ukugwema ubungozi bokunikezana ngezikweletu ngokusebenzisa uhlelo lokunikeza isikweletu ababoleki abakhethekile, lokhu kususelwa ohlelweni oluhlaziya amanani edatha engumlando wekhasimende. Imodeli ye-CRM ephakanyisiwe yaqala yakhiwa ngohlelo lwamanani, phecelezi istatistically random sample, okuluphawu olungcono olumele uhlelo lwe through-the-door (TTD) population lokukhetha abafakizicelo zokubolekwa imali bama SME, kanti lokhu kuxuba zona zombili izicelo eziphumelele kanye nezingaphumelelanga. Indlela yokukhetha abafakizicelo, phecelezi model-based bound-and-collapse (BC) reject-inference methodology isetshenzisiwe ukulungisa indlela yokukhetha ngokukhetha ngendlela yokucwasa kwisizinda seCRM. Imiphumela iye yakhombisa intuthuko enkulu mayelana namandla okwehlukanisa kanye nokunikezwa kwezikweletu kuma SME okungamamabhange enziwe ucwaningo lotho., njengoba lokhu kufakazelwa ukuncipha okukhulu kwe-bad rate kuwo wonke amamodeli athuthukisiwe. Imodeli yokuqala kanye neyokugcina zazidizayinelwe ibhange. Imodeli yokugcina yaqinisekiswa ngokusebenzisa zombili indlela isikweletu esingagculisi kanye negrafu ye-Area under Receiver Operating Characteristic (AUROC) ukulinganisa ukwehlukaniswa kwamandla emodeli engaphakathi kwesampuli nangaphandle kwesampuli. Uhlelo lwe-AUROC lwemodeli yokugcina (weak model) lwatholakala ukuthi luyi 0.9782, kanti ibad rate yatholakala ukuthi yenza i-14.69%. Kwaba khona ukuthuthuka nge-28.76% kwi-bad rate kwimodeli yokugcina uma iqhathaniswa nemodeli yamanje iCRM model ukuba isetshenziswe yibhange elithile. / Basel II Capital Accord e fetotse tekanyo ya kotsi ya mokitlane (credit risk measurement (CRM)) hoo “thepa e kotsi ya dibanka” ka moo e neng e bonwa ka teng, e seng e fuwa sebaka dikadimong. Lekala la Dikgwebo tse Nyane le tse Mahareng (SME) le bonwa ka tlwaelo jwalo ka lekala le kotsi e hodimo le senang ditswala bakeng sa ditshebetso tsa dikadimo haholo ke dibanka tsa kgwebo. Empa dipatlisiso tse thehilweng hodima se bonweng kapa se etsahetseng tsa tshebetso ya moralo wa Basel II internal-ratings-based (IRB) di supile hore kotsi ya mokitlane ya SME e kgona ho lekanngwa. Leha ho le jwalo, dibanka di ntse di thatafallwa ke ho bonelapele palo ya ditlholeho tsa ho lefa tsa diSME le ho fana ka mokitla lekaleng leo le kgotsofatsang ditlhoko tsa Basel tsa ditjhelete. Phuputso ena e ne sisinya ho etsa tekanyo ya se bonwang ho mmotlolo wa kotsi ya mokitlane (CRM) tshebetsong ya phano ya tjhelete ya diSME e etswang ke setsi sa ditjhelete (FI) ho la Zimbabwe. Mmotlolo o sebetsang hantle hape o fanang ka dipalo tse nepahetseng o dusa diFI hore di laole pepeso ya tsona ho kotsi ka phano e kgethang ya mokitlane, e thehilweng hodima manollo ya dipalopalo ya dintlha tsa histori ya bareki. Mmotlolo o sisingwang wa CRM o hlahisitswe ho tswa ho sampole e sa hlophiswang, e leng pontsho e betere ya setjhaba se ikenelang le monyako (TTD) ya batho bao e kang bakadimi ba tjhelete ho diSME, hobane e kenyelletsa bakopi ba amohetsweng le ba hannweng. Mokgwatshebetso wa bound-and-collapse (BC) reject-inference o kentswe tshebetsong ho nepahatsa tshekamelo ya kgetho e leng teng ho lekala la CRM. Diphetho tsena di bontshitse ntlafalo e kgolo ho matla a tlhophiso le palohare ya phano ya mokitlane ho diSME tsa banka eo ho ithutilweng ka yona, jwalo ka ha ho pakilwe ke ho phokotseho ya direite tse mpe ho pharalla le dimmotlolo tse hlahisitsweng. Mmotlolo wa ho qala le wa ho qetela e ile ya ralwa bakeng sa banka. Mmotlolo wa ho qetela o ile wa netefatswa ka tshebediso ya bobedi reite e mpe le mothinya wa Area under Receiver Operating Characteristic (AUROC) ho lekanya matla a kenyo mekgahlelong a mmotlolo kahare ho sampole le kantle ho yona. AUROC bakeng sa mmotlo wa ho qetela (mmotlolo o fokotseng) e fumanwe e le 0.9782, ha reite e mpe e fumanwe e le 14.69%. Ho bile le ntlafalo ya 28.76% ho reite e mpe bakeng sa mmotlolo wa ho qetela ha ho bapiswa le mmotlolo wa CRM ha o sebediswa bankeng yona eo. / Graduate School of Business Leadership / D.B.L.