PEC-PG. CNPq / As cooperativas de crédito são regidas pelos mesmos princípios cooperativistas, mas agregam funções financeiras como os bancos tradicionais. A solidariedade na área financeira permite que as cooperativas de crédito levem aos seus cooperados os fundos poupados ou repassando os de desenvolvimento governamental das políticas públicas em forma de crédito. É evidente, no entanto, que nem sempre os “apoios” são devolvidos da mesma maneira como foram recebidos, neste mundo de muita perfeição e de alta competitividade. Assim, integra o elemento estranho no meio onde se imagina seu difícil acesso, a falta de confiança no cumprimento do contrato de crédito contraído pelos credores (cooperados). É conhecido como Risco de Crédito ou de um eventual não pagamento das dívidas contraídas pelos cooperados, o principal fator para processo de Gestão de Risco de Crédito nas instituições cooperativas do mundo. Desse modo, se insere o problema da pesquisa, sendo nova a gestão de risco nas cooperativas de crédito e num mercado assombrado pelas crises financeiras, e com isso se questiona: em que medida ou em que ponto o cumprimento das normas da gestão de risco de crédito nessas instituições vem sendo atendido? Com base nessa preocupação, traçaram-se os objetivos, que apontam em avaliar as práticas de gestão de risco em cooperativas de crédito do Sudoeste do Paraná (PR) de acordo com as resoluções do Conselho Monetário Nacional (CMN). Para isso, tem-se como objetivos específicos: listar as práticas de gestão de risco necessárias para a concessão de crédito; identificar de que forma estão sendo abordadas as práticas de gestão de risco de crédito, considerando-se normas de CMN; verificar qual é o impacto da gestão de risco percebido pelos gestores nas cooperativas de crédito; levantar em termos quantitativos quanto essas cooperativas perdem anualmente em inadimplência (default); comparar as práticas das cooperativas pesquisadas entre si e com as resoluções do CMN. O trabalho se justifica pela importância do processo de gestão de risco para as instituições cooperativas, para a academia e pela contribuição no reforço àquilo que o CMN vem recomendado nas suas resoluções. Para se atingir os resultados pretendidos, foi aplicado um estudo de caso múltiplo nas cooperativas CRESOL, SICOOB e SICREDI/PR, com abordagem qualitativa e quantitativa, em parte com dados secundários e primários. Os dados secundários foram levantados por meio da revisão da literatura referente às áreas de cooperativismo em geral e de crédito, a gestão de risco de crédito e normas e as resoluções do CMN sobre o tema, além de informações nos Relatórios Financeiros das três cooperativas. Os dados primários foram levantados por meio de aplicação de questionário nessas instituições, sendo entrevistados os Gerentes de Crédito, o Assessor Sênior da Controladoria e o Assessor e Supervisor de Crédito. Os dados levantados foram comparados entre as informações dos entrevistados da mesma instituição e depois com as outras, sendo que, na análise dos dados, os nomes das mesmas (instituições) deixaram de ser citadas e foi atribuído o nome de CASO (CASO1, CASO2, e 3 ). Foi identificada uma preocupação com a gestão de risco nessas instituições. Também se percebeu que o crédito só é liberado quando passa por análise de, no mínimo, três pessoas. Existem procedimentos de análise de crédito que seguem todas as alçadas necessárias para que a proposta seja deferida e também são avaliados de acordo com a renda do proponente e as cooperativas singulares são classificadas de acordo com seus patrimônios de Referências (PR) e Patrimônios de Referências exigidas (PRE), para receberem crédito das suas centrais e até para liberarem crédito aos seus cooperados Pessoa Jurídica, caso existam. Mesmo assim, foram localizadas as perdas (prejuízos), algumas mais acentuadas do que outras. Percebe-se que ainda existem reduzidos estudos sobre gestão de risco nas cooperativas de crédito, dada à situação em que se encontravam as mesmas, mas atualmente vale reforçar as pesquisas para conhecer como essas instituições lidam com a gestão de risco de crédito e outros riscos. / Credit unions are governed by the same cooperative principles but add financial functions as traditional banks. Solidarity in the financial area allows credit unions to take to their members or transferring funds spared government development of public policy in the form of credit. But it is not always clear that the "support" are returned the same way they were received, in this world of too much perfection and high competitiveness. Integrates the foreign element in the middle where you think its difficult access, lack of confidence in the fulfillment of the credit agreement contracted by creditors (cooperative). Known as credit risk or a possible non-payment of debts contracted by the cooperatives the main factor for Credit Risk Management process in cooperative institutions in the world. Thus falls the research problem, and new risk management in credit unions and a market haunted by financial crises, and if it asks: To what extent or at what point the compliance of the management of credit risk these institutions has been met? Based on this concern were traced objectives, which aim to assess the practices of risk management in credit unions Southwest of Paraná (PR) according to the resolutions of the National Monetary Council (CMN). For this, one has the following specific objectives: List the risk management practices needed to grant credit; Identify how they are being addressed management practices of credit risk considering CMN standard; Ascertain the impact of the management of risk perceived by managers in credit unions; Rise in quantitative terms as these cooperatives lost annually in default; Compare practices of cooperatives surveyed each other and with the resolutions of the CMN. The work is justified by the importance of the risk management process for cooperative institutions, academia and the contribution to the reinforcement to what the CMN has been recommended in its resolutions. And to achieve the desired results a study of multiple case was applied in cooperative CRESOL, SICOOB and SICREDI / PR, with qualitative and quantitative approach in part with secondary and primary data. The secondary data were collected by reviewing the literature pertaining to the areas of cooperative movement in general and credit risk management and credit standards and CMN resolutions on the topic, also collected in the Financial Reports of the three Credit unions. Primary data were collected through a questionnaire in these institutions, being interviewed Managers of Credit, Senior Advisor of the Comptroller, Assessor and Supervisor of Credit. And the data obtained were compared to the information of the respondents from the same institution and then with the other, in the data analysis of the same names (institutions) are no longer quoted and was assigned the name of CASE (case1 case2 and 3). A concern with risk management in these institutions was identified. Also noticed that credit is only been released when passing by analysis of at least three people. There are procedures for credit analysis that follows all the necessary limits for the proposal to be granted and are evaluated according to the income of the applicant and the individual cooperatives are classified according to their Wealth of References (PR in Portuguese) and Heritage References Required (PRE in Portuguese), to receive credit of their plants and up to release credit to their members Corporations if any. Yet losses were located some steeper others do not. Realize that there are still smaller studies on risk management in credit unions, given the situation they were in the same, but is currently worth strengthen research to know how these institutions deal with the management of credit risk and other risks. / 5000

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.