Credit risk measurement model for small and medium enterprises : the case of Zimbabwe

Abstracts in English, Zulu and Southern SothoThe 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 LeadershipD.B.L

Preventing the deterioration of bank loan portfolio quality: a focus on unlikely-to-pay loans

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Paper presented at the 11th African Finance Journal Conference, Durban, South Africa.Based on simulations of implied values for credit worthiness over a period of 5 years for 1000 consumers, we establish a case for the semi-markov models as a proxy for internal credit risk models for a portfolio of consumer loans. With ample calibration, we prove the robustness of the semi-markov models in forecasting probabilities of default and loss given default. With a view of credit risk as a reliability problem, we generate credit risk indicators as qualifications of adequacy of a loan portfolio. This informs prospective holding of capital based on forecast delinquencies as opposed to the current retrospective practice that relies on the trigger event of default. We use Monte-Carlo simulation techniques to generate consumer ratings and adopt this to the Merton model to derive the initial probability transition matrix. Initial consumer rating is in accordance with industry practice using a credit score sheet backed by the logit model. The banking credit function could espouse the study results to fulfill regulatory credit risk capital requirements for consumer loans in line with the Central Bank of Kenya Prudential Risk Guidelines or banks in other jurisdictions compliant with the Basel banking framework.Based on simulations of implied values for credit worthiness over a period of 5 years for 1000 consumers, we establish a case for the semi-markov models as a proxy for internal credit risk models for a portfolio of consumer loans. With ample calibration, we prove the robustness of the semi-markov models in forecasting probabilities of default and loss given default. With a view of credit risk as a reliability problem, we generate credit risk indicators as qualifications of adequacy of a loan portfolio. This informs prospective holding of capital based on forecast delinquencies as opposed to the current retrospective practice that relies on the trigger event of default. We use Monte-Carlo simulation techniques to generate consumer ratings and adopt this to the Merton model to derive the initial probability transition matrix. Initial consumer rating is in accordance with industry practice using a credit score sheet backed by the logit model. The banking credit function could espouse the study results to fulfill regulatory credit risk capital requirements for consumer loans in line with the Central Bank of Kenya Prudential Risk Guidelines or banks in other jurisdictions compliant with the Basel banking framework

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Regulating for competition with BigTechs: banking-as-a-service and “beyond banking”

En este artículo se analizan dos estrategias novedosas de competencia en el sector bancario: la banca como servicio (banking-as-a-service) y la prestación de servicios más allá de la banca (beyond banking). Se argumenta que estos modelos de negocio emulan la penetración de las BigTech en la prestación de servicios financieros con el trasfondo de su actividad comercial. Pero estos modelos conllevan nuevos riesgos, que requieren respuestas regulatorias adecuadas en una doble vía. En primer lugar, se afirma que la regulación del modelo de competencia disruptivo de las BigTech —en la confluencia de los servicios financieros y la tecnología— precisa de instrumentos de coordinación novedosos entre las distintas áreas de política regulatoria involucradas (banca, pagos, competencia, tecnología digital y datos), así como de una nueva perspectiva sobre el tratamiento de los conglomerados mixtos que consolidan múltiples líneas de negocio y riesgos. En segundo lugar, el hecho de que la «banca como servicio» se base en un pseudo-leasing de la licencia bancaria a empresas no financieras, al objeto de ganar una base transaccional, plantea riesgos morales y de modelo que exigen tratamientos específicos, no muy diferentes de los aplicados al modelo de «originar para distribuir». Las perspectivas de éxito del modelo beyond banking son poco alentadoras en su versión extrema, en la que las entidades de crédito pasan a ser patrocinadoras de plataformas en toda regla, mientras que las versiones híbridas siguen conllevando nuevos riesgos.This paper analyses “banking-as-a-service” and “beyond banking”, two emerging bank competition strategies. These business models are argued to emulate the transaction-based inroads that BigTechs have made into finance. But they entail new risks that call for adequate regulatory responses along a dual track. First, it is argued that regulation of the disruptive competition model of BigTechs at the confluence of finance and technology requires new tools to coordinate the different regulatory policies involved (banking, payments, competition, data, digital) and a new approach to the treatment of mixed business conglomerates that consolidate multiple business lines and risks. Second, the reliance of “banking-as-a-service” on a quasi-renting-out of the banking licence to non-financial companies as a way of obtaining a transactional base poses moral hazard and model risks that require specific treatments not unlike the originate-to-distribute business model did. The prospects for success of the pure version of the “beyond banking” model, where banks become sponsors of full-fledged platforms, are assessed as dim, but hybrid versions still entail new risks

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