Connus sous le nom de Systèmes d’Alerte Avancés, ou Early Warning Systems (EWS), les modèles de prévision des crises financières sont appelés à jouer un rôle déterminant dans l’orientation des politiques économiques tant au niveau microéconomique qu’au niveau macroéconomique et international. Or,dans le sillage de la crise financière mondiale, des questions majeures se posent sur leur réelle capacité prédictive. Deux principales problématiques émergent dans le cadre de cette littérature : comment évaluer les capacités prédictives des EWS et comment les améliorer ?Cette thèse d’économétrie appliquée vise à proposer (i) une méthode d’évaluation systématique des capacités prédictives des EWS et (ii) de nouvelles spécifications d’EWS visant à améliorer leurs performances. Ce travail comporte quatre chapitres. Le premier propose un test original d’évaluation des prévisions par intervalles de confiance fondé sur l’hypothèse de distribution binomiale du processus de violations. Le deuxième chapitre propose une stratégie d’évaluation économétrique des capacités prédictives des EWS. Nous montrons que cette évaluation doit être fondée sur la détermination d’un seuil optimal sur les probabilités prévues d’apparition des crises ainsi que sur la comparaison des modèles.Le troisième chapitre révèle que la dynamique des crises (la persistance) est un élément essentiel de la spécification économétrique des EWS. Les résultats montrent en particulier que les modèles de type logit dynamiques présentent de bien meilleurs capacités prédictives que les modèles statiques et que les modèles de type Markoviens. Enfin, dans le quatrième chapitre nous proposons un modèle original de type probit dynamique multivarié qui permet d’analyser les schémas de causalité intervenant entre différents types crises (bancaires, de change et de dette). L’illustration empirique montre clairement que le passage à une modélisation trivariée améliore sensiblement les prévisions pour les pays qui connaissent les trois types de crises. / Known as Early Warning Systems (EWS), financial crises forecasting models play a key role in definingeconomic policies at microeconomic, macroeconomic and international level. However, in the wake ofthe global financial crisis, numerous questions with respect to their forecasting abilities have been raised,as very few signals were drawn prior to the starting of the turmoil. Two questions arise in this context:how to evaluate EWS forecasting abilities and how to improve them?The broad goal of this applied econometrics dissertation is hence (i) to propose a systematic model-free evaluation methodology for the forecasting abilities of EWS as well as (ii) to introduce new EWSspecifications with improved out-of-sample performance. This work has been concretized in four chapters.The first chapter introduces a new approach to evaluate interval forecasts which relies on the binomialdistributional assumption of the violations series. The second chapter proposes an econometric evaluationmethodology of the forecasting abilities of an EWS. We show that adequate evaluation must take intoaccount the cut-off both in the optimal crisis forecast step and in the model comparison step. The thirdchapter points out that crisis dynamics (persistence) is essential for the econometric specification of anEWS. Indeed, dynamic logit models lead to better out-of-sample forecasting probabilities than those oftheir main competitors (static model and Markov-switching one). Finally, a multivariate dynamic probitEWS is proposed in the fourth chapter to take into account the causality between different types of crises(banking, currency, sovereign debt). The empirical application shows that the trivariate model improvesforecasts for countries that underwent the three types of crises.

In this thesis Generalized Additive Neural Networks (GANNs) are studied in the context of predictive Data Mining. A GANN is a novel neural network implementation of a Generalized Additive Model. Originally GANNs were constructed interactively by considering partial residual plots. This methodology involves subjective human judgment, is time consuming, and can result in suboptimal results. The newly developed automated construction algorithm solves these difficulties by performing model selection based on an objective model selection criterion. Partial residual plots are only utilized after the best model is found to gain insight into the relationships between inputs and the target. Models are organized in a search tree with a greedy search procedure that identifies good models in a relatively short time. The automated construction algorithm, implemented in the powerful SAS® language, is nontrivial, effective, and comparable to other model selection methodologies found in the literature. This implementation, which is called AutoGANN, has a simple, intuitive, and user-friendly interface. The AutoGANN system is further extended with an approximation to Bayesian Model Averaging. This technique accounts for uncertainty about the variables that must be included in the model and uncertainty about the model structure. Model averaging utilizes in-sample model selection criteria and creates a combined model with better predictive ability than using any single model. In the field of Credit Scoring, the standard theory of scorecard building is not tampered with, but a pre-processing step is introduced to arrive at a more accurate scorecard that discriminates better between good and bad applicants. The pre-processing step exploits GANN models to achieve significant reductions in marginal and cumulative bad rates. The time it takes to develop a scorecard may be reduced by utilizing the automated construction algorithm. / Thesis (Ph.D. (Computer Science))--North-West University, Potchefstroom Campus, 2006.

In this thesis Generalized Additive Neural Networks (GANNs) are studied in the context of predictive Data Mining. A GANN is a novel neural network implementation of a Generalized Additive Model. Originally GANNs were constructed interactively by considering partial residual plots. This methodology involves subjective human judgment, is time consuming, and can result in suboptimal results. The newly developed automated construction algorithm solves these difficulties by performing model selection based on an objective model selection criterion. Partial residual plots are only utilized after the best model is found to gain insight into the relationships between inputs and the target. Models are organized in a search tree with a greedy search procedure that identifies good models in a relatively short time. The automated construction algorithm, implemented in the powerful SAS® language, is nontrivial, effective, and comparable to other model selection methodologies found in the literature. This implementation, which is called AutoGANN, has a simple, intuitive, and user-friendly interface. The AutoGANN system is further extended with an approximation to Bayesian Model Averaging. This technique accounts for uncertainty about the variables that must be included in the model and uncertainty about the model structure. Model averaging utilizes in-sample model selection criteria and creates a combined model with better predictive ability than using any single model. In the field of Credit Scoring, the standard theory of scorecard building is not tampered with, but a pre-processing step is introduced to arrive at a more accurate scorecard that discriminates better between good and bad applicants. The pre-processing step exploits GANN models to achieve significant reductions in marginal and cumulative bad rates. The time it takes to develop a scorecard may be reduced by utilizing the automated construction algorithm. / Thesis (Ph.D. (Computer Science))--North-West University, Potchefstroom Campus, 2006.

Credit Decisions are extremely vital for any type of financial institution because it can stimulate huge financial losses generated from defaulters. A number of banks use judgmental decisions, means credit analysts go through every application separately and other banks use credit scoring system or combination of both. Credit scoring system uses many types of statistical models. But recently, professionals started looking for alternative algorithms that can provide better accuracy regarding classification. Neural network can be a suitable alternative. It is apparent from the classification outcomes of this study that neural network gives slightly better results than discriminant analysis and logistic regression. It should be noted that it is not possible to draw a general conclusion that neural network holds better predictive ability than logistic regression and discriminant analysis, because this study covers only one dataset. Moreover, it is comprehensible that a “Bad Accepted” generates much higher costs than a “Good Rejected” and neural network acquires less amount of “Bad Accepted” than discriminant analysis and logistic regression. So, neural network achieves less cost of misclassification for the dataset used in this study. Furthermore, in the final section of this study, an optimization algorithm (Genetic Algorithm) is proposed in order to obtain better classification accuracy through the configurations of the neural network architecture. On the contrary, it is vital to note that the success of any predictive model largely depends on the predictor variables that are selected to use as the model inputs. But it is important to consider some points regarding predictor variables selection, for example, some specific variables are prohibited in some countries, variables all together should provide the highest predictive strength and variables may be judged through statistical analysis etc. This study also covers those concepts about input variables selection standards.

This thesis aims to predict the probability of default (PD) of non-performing loan (NPL) customers using transaction and debt data, as a part of developing credit scoring model for Hoist Finance. Many NPL customers face financial exclusion due to default and therefore are considered as bad customers. Hoist Finance is a company that manages NPLs and believes that not all conventionally considered subprime customers are high-risk customers and wants to offer them financial inclusion through favourable loans. In this thesis logistic regression was used to model the PD of NPL customers at Hoist Finance based on 12 months of data. Different feature selection (FS) methods were explored, and the best model utilized l1-regularization for FS and predicted with 85.71% accuracy that 6,277 out of 27,059 customers had a PD between 0% to 10%, which support this belief. Through analysis of the PD it was shown that the PD increased almost linearly with respect to an increase in either debt quantity, original total claim amount or number of missed payments. The analysis also showed that the payment behaviour in the last quarter had the most predictive power. At the same time, from analysing the type II error it was shown that the model was unable to capture some bad payment behaviour, due to putting to large emphasis on the last quarter. / Det här examensarbetet syftar till att predicera sannolikheten för fallissemang för nödlidande lånekunder genom transaktions- och lånedata. Detta som en del av kreditvärdighetsmodellering för Hoist Finance. På engelska kallas sannolikheten för fallissemang för "probability of default" (PD) och nödlidande lån kallas för "non-performing loan" (NPL). Många NPL-kunder står inför ekonomisk uteslutning på grund av att de konventionellt betraktas som kunder med dålig kreditvärdighet. Hoist Finance är ett företag som förvaltar nödlidande lån och påstår att inte alla konventionellt betraktade "dåliga" kunder är högrisk kunder. Därför vill Hoist Finance inkludera dessa kunder ekonomisk genom att erbjuda gynnsamma lån. I detta examensarbetet har Logistisk regression används för att predicera PD på nödlidande lånekunder på Hoist Finance baserat på 12 månaders data. Olika metoder för urval av attribut undersöktes och den bästa modellen utnyttjade lasso för urval. Denna modell predicerade med 85,71 % noggrannhet att 6 277 av 27 059 kunder har en PD mellan 0 % till 10 %, vilket stödjer påståendet. Från analys av PD visade det sig att PD ökade nästan linjärt med avseende på ökning i antingen kvantitet av lån, det ursprungliga totala lånebeloppet eller antalet missade betalningar. Analysen visade också att betalningsbeteendet under det sista kvartalet hade störst prediktivt värde. Genom analys av typ II-felet, visades det sig samtidigt att modellen hade svårigheter att fånga vissa dåliga betalningsbeteende just på grund av att för stor vikt lades på det sista kvartalet.

In this study we propose to enhance the predictive power of a Buy Now, Pay Later (BNPL) consumer credit scorecard by leveraging detailed product information. The object of analys is in this study is Klarna Bank AB, which is the largest retail finance provider in Sweden. This research conducts a quantitative study in order to firstly, investigate if it is possible to find subcategories that correlate more with credit risk than the existing product categories at Klarna. This will be investigated by categorizing already accepted orders into more granulated product categories than Klarna's existing level. Secondly, this study investigates how more detailed product categorization can improve a BNPL e-commerce consumer credit scorecard. Lastly, a qualitative analysis of what the business impact an implementation of this feature could entail for Klarna Bank AB is conducted. Our results demonstrate that it is possible to find subcategories that correlate more strongly with credit risk than the existing categories. The characteristics of the high-and low risk product categories align with existing research on online consumer behavior. More specifically, we found that luxury products, ego-related products, and products related to addictive behavior had the highest risk. We also contribute to existing research within the credit risk management field by finding that trending/new products on the market have a higher risk, and that the novelty of a product should be taken into consideration in credit risk prediction. By applying a hypothetical credit scoring model on a dataset of already accepted orders that took the new detailed product categories into consideration, the discrimination performance could be improved. However, risks regarding adding more data into a credit risk model need to be considered before implementing the proposed solution. Our study, therefore, demonstrates the potential of including more granulated product category information in a BNPL e-commerce consumer credit scorecard to improve risk prediction. While the results of this study are limited to the studied context, it is considered generalizable in that the proposed method could effectively be adapted to retrieve corresponding findings in other contexts. / Denna studie föreslår att förbättra prediktionsförmågan hos en konsumentkreditriskmodell inom Buy Now, Pay Later (BNPL) genom att utnyttja detaljerad produktinformation. Analysobjektet i studien är Klarna Bank AB, som är den största BNPL-aktören i Sverige. I detta arbete genomförs en kvantitativ studie för att först och främst undersöka om det är möjligt att hitta produktkategorier som korrelerar mer med kreditrisk än vad de nuvarande produktkategorierna på Klarna gör. Detta ska undersökas genom att kategorisera redan accepterade ordrar i mer granulerade produktkategorier än Klarnas befintliga nivå. Därefter ska det undersökas hur mer detaljerad produktkategorisering kan förbättra en kreditriskmodell för BNPL-företag. Till sist genomförs en kvalitativ analys över vilken affärspåverkan en implementering av denna lösning skulle kunna innebära för Klarna Bank AB. Våra resultat visar att det är möjligt att hitta underkategorier som korrelerar starkare med kreditrisk än de befintliga kategorierna hos Klarna. Egenskaperna hos de produktkategorier med hög och låg risk överensstämmer med befintlig forskning inom konsumentbeteenden online och psykologi. Mer specifikt fann vi att lyxprodukter, ego-relaterade produkter, och produkter relaterade till beroendebeteenden hade den högsta risken. Vi bidrar också till befintlig forskning inom forskningsfältet för kreditrisk genom att finna att trendiga/nya produkter på marknaden har högre risk och att det borde beaktas vid kreditriskbedömning. Genom att tillämpa en hypotetisk kreditvärderingsmodell på en dataset av redan accepterade ordrar, som tog hänsyn till de nya detaljerade produktkategorierna, kunde prediktionsförmågan förbättras. Risker med att lägga till mer data i en kreditriskmodell måste dock övervägas innan den föreslagna lösningen implementeras. Vår studie visar på potentialen i att inkludera mer detaljerad produktkategoriinformation i ett consumer credit scorecard för BNPL-företag för att på så sätt förbättra riskprediktionsförmågan. Även om resultaten av denna studie är begränsade till den studerade kontexten, anses det vara generaliserbart genom att den föreslagna metoden effektivt skulle kunna anpassas för att hitta motsvarande resultat i andra kontexter.

Cette Thèse de sciences de gestion, se propose d’élucider les difficultés que rencontrent les acteurs du système bancaire comorien et apporter des solutions afin de lui garantir sa solidité, stabilité et enfin sa pérennité. Elle est divisée en deux parties. La première porte plus particulièrement sur le cadre national et internationale du système bancaire comorien. La deuxième met en évidence les banques comoriennes confrontées à la transparence financière et aux exigences de supervision prudentielle. Le premier titre de la première partie, tâche à mettre en lumière l’organisation actuelle du système bancaire comorien inspiré du modèle français (chapitre 1) et l’apport du développement récent de la finance islamique (chapitre 2) afin de combler le retard de la banque conventionnelle. La réorganisation de la Banque Centrale des Comores et la mise en place de la banque islamique locale, peuvent contribuer au changement radical du système bancaire comorien. Le deuxième titre, permet au régulateur et prêteur en dernier ressort (Banque Centrale des Comores) de prendre le modèle des normes prudentielles internationales proposées par le Comité de Bâle (Bâle II et III), pour réguler le système bancaire comorien afin de lui garantir sa solidité, stabilité et enfin sa pérennité (chapitre 1). A travers ces recommandations du comité de Bâle, nous avons apporté des solutions en élaborant la Matrice Msahazi Credit Scoring Corporation, destinée aux analyses des données des banques comoriennes contre un risque endogène (Chapitre2). Nous avons aussi élaboré d’autres matrices que les banques comoriennes se serviront pour la notation interne, des risques de contreparties (entreprises et particuliers) afin de lutter contre le risque exogène. La deuxième partie de cette Thèse suggère deux autres solutions : la première est l’exigence de transparence financière des banques comoriennes (Pilier 3 : Bâle2 et 3) afin de lutter contre les malversations financières orchestrées par certains agents (titre I). Le premier chapitre introduit l’objectif de la communication financière de manière générale et la manière dont le comité de Bâle (Bâle 2 et 3) recommande les banques de communiquer leurs informations financières (méthodes d’évaluations des risques et fonds propres). Le deuxième chapitre propose aux banques comoriennes et aux autorités de contrôles, les techniques de notation financière pratiquées au niveau internationale pour distinguer le niveau de solvabilité de la contrepartie. La deuxième solution, nous avons donné à la Banque Centrale des Comores, des techniques pour renforcer la supervision prudentielle (Pilier 2, Bâle 2 et 3), (titre II). Le premier chapitre exige d’une part la direction et le conseil d’administration de la banque de définir les techniques de contrôles, d’indentifications, d’évaluations, gestions des risques et les objectifs de fonds propre à atteindre. D’autre part, l’autorité de contrôle (Banque centrale des Comores) doit passer au crible tous ces outils de contrôle. Au deuxième et dernier chapitre de la recherche, nous avons proposé à la Banque Centrale des Comores des nouvelles méthodes de supervision prudentielle afin de garantir la solidité, stabilité et pérennité du système bancaire. Nous avons l’espoir que l’ensemble de ces suggestions contribueront à préserver la solidité, stabilité et pérennité du système bancaire comorien afin de financer le développement de l’économie comorienne et sortir le pays de la pauvreté. / This thesis on busness management, aims to elucidate the difficulties faced by the stakeholders of the Comorian banking system and to provide solutions to ensure its soundness, stability and sustainability. The thesis is divided into two parts. The first focuses specifically on the national and international context of the Comorian banking system. The second, highlights how the Comorian banks should adapt to the financial transparency and prudential supervision requirements. The first title of the first part, tries toshed light on the current organization of the Comorian banking system based on the French model (Chapter 1) and the contribution of the recent development of Islamic finance (Chapter 2) to close the gap in conventional banking. The reorganization of the Central Bank of the Comoros and the establishment of the local Islamic bank can contribute to a radical change in the Comorian banking system. The second title allows the regulator and lender of last resort (Central Bank of the Comoros ) to take the model of international prudential standards proposed by the Basel Committee (Basel II and III) to regulate the Comorian banking system in order to guarantee its soundness, stability and finally sustainability (Chapter 1). Through these recommendations of the Basel committee, we have provided solutions by developing Msahazi Credit Scoring Matrix Corporation, intended to analyse data of Comorian banks against endogenous risk (Chapter 2). We have also developed matrices other than Comorian banks used for internal rating of the counterparty risk (companies and individuals) to fight against exogenous risk. The second part of this thesis suggests two alternatives: the first is the requirement of financial transparency for Comorian banks (Pillar 3: Basel Conventions 2 and 3) in order to fight against embezzlement orchestrated by certain agents (Title I). The first chapter introduces the objective of financial reporting in general, and how the Basel Committee (Basel 2 and 3) asks banks to disclose their financial information (methods of risk assessments and equity). The second chapter provides credit rating techniques practiced at international level to the Comorian banks and supervisory authorities in order to distinguish the level of creditworthiness of companies and clients concerned. The second alternative we have given to the Central Bank of the Comoros is the techniques for strengthening prudential supervision (Pillar 2, Basel 2 and 3), (Title II) . The first chapter requires both the management and the bank's board of directors to define control techniques, identifications, assessments, risk managements and core capital goals. On the other hand, the supervisory authority (Comoros Central Bank) has to go through all these control tools. In the second and final chapter of the research, we propose to the Central Bank of the Comoros new prudential supervision methods to ensure the soundness, stability and sustainability of the banking system. We hope that all of these suggestions will help to preserve the soundness, stability and durability of the Comorian banking system in order to finance the development of the Comorian economy and lift the country out of poverty.

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.