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))

Criminal career research postulates that offending behaviour develops over time during the course of one’s life. Thus, delinquency is not an isolated incident which occurs at a certain moment in time. This research comprises a mixed-method study of the criminal career of 40 offenders who perpetrated robberies against the banking and CIT industries. Through this research, an exploration is made for possibilities of using criminal career research to develop results which will guide crime prevention policies. The qualitative methodology used for this research included semi-structured interviews in order to collect information on motivations of armed robbers and the various mechanics (i.e. planning, recruitment, group dynamics) of the crime of armed robbery. Through the use of structured questionnaires, biographical data, information on risk factors and figures on the different aspects of a criminal career, such as age of offending onset, offending frequency and seriousness, and career length, were all gathered. The general findings of this research demonstrate that offending onset occurs between the ages of 11 and 15 with petty crimes, and then escalates to serious crimes. Secondly, witnessed throughout the dissertation is the fact that development of delinquent behaviour is not a result of a single risk factor but an outcome of multiple risk factors. Subsequently, a suggestion is made for the introduction of multifaceted deterrence programmes, which will holistically deal with the various offending risk factors (i.e. family, community and the offender’s personal risk factors as well as peer and school dynamics). Thirdly, armed robbers are responsible for various other crimes in the process of committing the offence of robbery. Accordingly, this study confirms the criminal career notion that a small number of chronic offenders are responsible for a large number of offences. That is why it is recommended that policy makers pay attention to disrupting the criminal career of this small number of high risk offenders. / Department of Criminology and Security Science / M.A. (Criminology)

Text in English / The goals of this research were to explore, describe and explain the crime of associated robbery, which at the time of this study, was a scientifically unknown phenomenon. Associated robbery is defined as “a bank-related robbery (by association) of cash or attempt thereto, committed against a bank client or his/her delegate, at any stage while en-route to or from a bank branch, ATM or cash centre or inside the branch to effect a deposit, or, withdrawal” (SABRIC 2013:4) This robbery is divided into two main categories, namely robbery before cash deposit and robbery after cash withdrawal. As found in literature and also evident in the findings of this study, more incidents and related cash losses are reported from robbery after withdrawal, which is sub-divided into muti scam, money bomb and spiked drink. This study followed an exploratory, sequential, mixed-method research approach where the qualitative phase took place first – followed by the quantitative phase. The topic was firstly explored by collecting qualitative data via in-depth, one-on-one interviews (from a phenomenological point of view) where mutual meaning was sought, as understood by victims of associated robbery. To gather quantitative data, 500 bank clients (i.e. individuals, small business owners and stokvel/saving club members) completed a survey questionnaire. Their perspective on the phenomenon of associated robbery was thus explained and described through the use of descriptive statistics, particularly univariate and bivariate statistical analysis. The most significant contribution made by this study, is embedment of the preventative measures used by the banking industry and other stakeholders such as the SAPS into the Situational Crime Prevention (SCP) and Crime Prevention Through Environmental Design (CPTED) principles approaches. This model is heavily grounded on 12 SCP strategies, namely: access control; deflecting offenders; controlling facilitation; entry/exist screening; formal surveillance; surveillance by employees; natural surveillance; target removal; reducing temptation; rule setting; stimulating conscience; and facilitating compliance. Furthermore, the model emphasises that the combating of associated robberies is a collaborative effort and thus the individual (bank client), the banking industry, the criminal justice system (CJS) and the general public all have to work together in fighting this endemic. The lack of knowledge mainly from a victim’s perspective was identified as one of the challenges faced. However, this presented an opportunity for this study to make a significant contribution to the development of scientific literature. Moreover, the use of opportunity theories to explain the reasons why individuals are victimised placed the phenomenon in the criminological research milieu – thus pioneering a way for researchers who may wish to conduct future research on the same topic. / Criminology and Security Science / D. Litt. et Phil. (Criminology)

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.