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 recent years the development and implementation of AI and machine learning models has increased dramatically. The availability of quality data paving the way for sophisticated AI models. Financial institutions uses many models in their daily operations. They are however, heavily regulated and need to follow the regulation that are set by central banks auditory standard and the financial supervisory authorities. One of these standards is the disclosure of expected credit losses in financial statements of banks, called IFRS 9. Banks must measure the expected credit shortfall in line with regulations set up by the EBA and FSA. In this master thesis, we are collaborating with a Swedish bank to evaluate different machine learning models to predict defaults of a unsecured credit portfolio. The default probability is a key variable in the expected credit loss equation. The goal is not only to develop a valid model to predict these defaults but to create and evaluate different models based on their performance and transparency. With regulatory challenges within AI the need to introduce transparency in models are part of the process. When banks use models there’s a requirement on transparency which refers to of how easily a model can be understood with its architecture, calculations, feature importance and logic’s behind the decision making process. We have compared the commonly used model logistic regression to three machine learning models, decision tree, random forest and XG boost. Where we want to show the performance and transparency differences of the machine learning models and the industry standard. We have introduced a transparency evaluation tool called transparency matrix to shed light on the different transparency requirements of machine learning models. The results show that all of the tree based machine learning models are a better choice of algorithm when estimating defaults compared to the traditional logistic regression. This is shown in the AUC score as well as the R2 metric. We also show that when models increase in complexity there is a performance-transparency trade off, the more complex our models gets the better it makes predictions. / Under de senaste ̊aren har utvecklingen och implementeringen av AI- och maskininl ̈arningsmodeller o ̈kat dramatiskt. Tillg ̊angen till kvalitetsdata banar va ̈gen fo ̈r sofistikerade AI-modeller. Finansiella institutioner anva ̈nder m ̊anga modeller i sin dagliga verksamhet. De a ̈r dock starkt reglerade och m ̊aste fo ̈lja de regler som faststa ̈lls av centralbankernas revisionsstandard och finansiella tillsynsmyndigheter. En av dessa standarder a ̈r offentligg ̈orandet av fo ̈rva ̈ntade kreditfo ̈rluster i bankernas finansiella rapporter, kallad IFRS 9. Banker m ̊aste ma ̈ta den fo ̈rva ̈ntade kreditfo ̈rlusten i linje med regler som faststa ̈lls av EBA och FSA. I denna uppsats samarbetar vi med en svensk bank fo ̈r att utva ̈rdera olika maskininl ̈arningsmodeller f ̈or att fo ̈rutsa ̈ga fallisemang i en blankokreditsportfo ̈lj. Sannolikheten fo ̈r fallismang ̈ar en viktig variabel i ekvationen fo ̈r fo ̈rva ̈ntade kreditfo ̈rluster. M ̊alet a ̈r inte bara att utveckla en bra modell fo ̈r att prediktera fallismang, utan ocks ̊a att skapa och utva ̈rdera olika modeller baserat p ̊a deras prestanda och transparens. Med de utmaningar som finns inom AI a ̈r behovet av att info ̈ra transparens i modeller en del av processen. Na ̈r banker anva ̈nder modeller finns det krav p ̊a transparens som ha ̈nvisar till hur enkelt en modell kan fo ̈rst ̊as med sin arkitektur, bera ̈kningar, variabel p ̊averkan och logik bakom beslutsprocessen. Vi har ja ̈mfo ̈rt den vanligt anva ̈nda modellen logistisk regression med tre maskininla ̈rningsmodeller: Decision trees, Random forest och XG Boost. Vi vill visa skillnaderna i prestanda och transparens mellan maskininl ̈arningsmodeller och branschstandarden. Vi har introducerat ett verktyg fo ̈r transparensutva ̈rdering som kallas transparensmatris fo ̈r att belysa de olika transparenskraven fo ̈r maskininla ̈rningsmodeller. Resultaten visar att alla tra ̈d-baserade maskininla ̈rningsmodeller a ̈r ett ba ̈ttre val av modell vid prediktion av fallisemang j ̈amfo ̈rt med den traditionella logistiska regressionen. Detta visas i AUC-score samt R2 va ̈rdet. Vi visar ocks ̊a att n ̈ar modeller blir mer komplexa uppst ̊ar en kompromiss mellan prestanda och transparens; ju mer komplexa v ̊ara modeller blir, desto ba ̈ttre blir deras prediktioner.

The combination of regulatory pressure and rare but impactful defaults together comprise the domain of low default portfolios, which is a central and complex topic that lacks clear industry standards. A novel approach that utilizes external data to create a Shadow Rating model has been proposed by Ulrich Erlenmaier. It addresses the lack of data by estimating a probability of default curve from an external rating scale and subsequently training a statistical model to estimate the credit rating of obligors. The thesis intends to first explore the capabilities of the Cohort model and the Pluto and Tasche model to estimate the probability of default associated with banks and financial institutions through the use of external data. Secondly, the thesis will implement a multinomial logistic regression model, an ordinal logistic regression model, Classification and Regression Trees, and a Random Forest model. Subsequently, their performance to correctly estimate the credit rating of companies in a portfolio of banks and financial institutions using financial data is evaluated. Results suggest that the Cohort model is superior in modelling the underlying data, given a Gini coefficient of 0.730 for the base case, as opposed to Pluto and Tasche's 0.260. Moreover, the Random Forest model displays marginally higher performance across all metrics (such as an accuracy of 57%, a mean absolute error of 0.67 and a multiclass receiver operating characteristic of 0.83). However, given a lower degree of interpretability, the more simplistic ordinal logistic regression model (50%, 0.80 and 0.81, respectively) can be preferred due to its clear interpretability and explainability. / Kombinationen av regulatoriskt påtryck och få men påverkande fallissemang utgör tillsammans området lågfallissemangsportföljer, vilket är ett centralt men komplext ämne med avsaknad av tydliga industristandarder. En metod som använder extern data för att skapa en skuggrating-modell har föreslagits av Ulrich Erlenmaier. Den adresserar problemet av bristande data genom att använda externa ratings för att estimera en kurva över sannolikheten. Sedermera implementeras en statistisk modell som estimerar kreditratingen av låntagare. Denna uppsats ämnar för det första att utforska möjligheterna för kohortmodellen samt Pluto-och-Tasche-modellen att estimera sannolikheten för fallissemang associerat med banker och finansiella institutioner genom användandet av extern data. För det andra implementeras statistiska modeller genom nominell logistisk regression, ordinal logistisk regression, klassificerings- och regressionsträd samt Random Forest. Sedermera utvärderas modellernas förmåga att förutse kreditratings för företag från en portfölj av banker och finansiella institutioner. Resultat föreslår att kohortmodellen är att föredra vid modellering av underliggande data, givet en Ginikoefficient på 0.730 för grundfallet, till skillnad från Pluto och Tasches resultat på 0.260. Vidare genererade Random Forest marginellt bättre resultat över alla utvärderingskriterier (till exempel, 57% träffsäkerhet, 0.67 mean absolute error och 0.83 multiclass receiver operating characteristic). Däremot har den en lägre tolkningsbarhet så att ordinal logistisk regression (med respektive värden 50%, 0.80 och 0.81) skulle kunna föredras, givet dess tydlighet och transparens.

Notre thèse consiste à expliquer, en apportant quelques éléments théoriques, les imperfections des stress tests macro-prudentiels d’EBA/BCE, et de proposer une nouvelle méthodologie de leur application ainsi que deux stress tests spécifiques en complément. Nous montrons que les stress tests macro-prudentiels peuvent être non pertinents lorsque les deux hypothèses fondamentales du modèle de base de Gordy-Vasicek utilisé pour évaluer le capital réglementaire des banques en méthodes internes (IRB) dans le cadre du risque de crédit (portefeuille de crédit asymptotiquement granulaire et présence d’une seule source de risque systématique qui est la conjoncture macro-économique), ne sont pas respectées. Premièrement, ils existent des portefeuilles concentrés pour lesquels les macro-stress tests ne sont pas suffisants pour mesurer les pertes potentielles, voire inefficaces si ces portefeuilles impliquent des contreparties non cycliques. Deuxièmement, le risque systématique peut provenir de plusieurs sources ; le modèle actuel à un facteur empêche la répercussion propre des chocs « macro ».Nous proposons un stress test spécifique de crédit qui permet d’appréhender le risque spécifique de crédit d’un portefeuille concentré, et un stress test spécifique de liquidité qui permet de mesurer l’impact des chocs spécifiques de liquidité sur la solvabilité de la banque. Nous proposons aussi une généralisation multifactorielle de la fonction d’évaluation du capital réglementaire en IRB, qui permet d’appliquer les chocs des macro-stress tests sur chaque portefeuille sectoriel, en stressant de façon claire, précise et transparente les facteurs de risque systématique l’impactant. Cette méthodologie permet une répercussion propre de ces chocs sur la probabilité de défaut conditionnelle des contreparties de ces portefeuilles et donc une meilleure évaluation de la charge en capital de la banque. / Our thesis consists in explaining, by bringing some theoretical elements, the imperfections of EBA / BCE macro-prudential stress tests, and proposing a new methodology of their application as well as two specific stress tests in addition. We show that macro-prudential stress tests may be irrelevant when the two basic assumptions of the Gordy-Vasicek core model used to assess banks regulatory capital in internal methods (IRB) in the context of credit risk (asymptotically granular credit portfolio and presence of a single source of systematic risk which is the macroeconomic conjuncture), are not respected. Firstly, they exist concentrated portfolios for which macro-stress tests are not sufficient to measure potential losses or even ineffective in the case where these portfolios involve non-cyclical counterparties. Secondly, systematic risk can come from several sources; the actual one-factor model doesn’t allow a proper repercussion of the “macro” shocks. We propose a specific credit stress test which makes possible to apprehend the specific credit risk of a concentrated portfolio, as well as a specific liquidity stress test which makes possible to measure the impact of liquidity shocks on the bank’s solvency. We also propose a multifactorial generalization of the regulatory capital valuation model in IRB, which allows applying macro-stress tests shocks on each sectorial portfolio, stressing in a clear, precise and transparent way the systematic risk factors impacting it. This methodology allows a proper impact of these shocks on the conditional probability of default of the counterparties of these portfolios and therefore a better evaluation of the capital charge of the bank.

本研究所使用違約機率（Probability of Default, 以下簡稱PD）的預測方法為Robust Logistic Regression（穩健羅吉斯迴歸），本研究發展且應用這個方法是基於下列兩個觀察：1. 極端值常常出現在橫剖面資料，而且對於實證結果往往有很大地影響，因而極端值必須要被謹慎處理。2. 當使用Logit Model（羅吉斯模型）估計違約率時，卻忽略極端值。試圖不讓資料中的極端值對估計結果產生重大的影響，進而提升預測的準確性，是本研究使用Logit Model並混合Robust Regression（穩健迴歸）的目的所在，而本研究是第一篇使用Robust Logistic Regression來進行PD預測的研究。 變數的選取上，本研究使用Z-SCORE模型中的變數，此外，在考慮公司的營收品質之下，亦針對公司的應收帳款週轉率而對相關變數做了調整。 本研究使用了一些信用風險模型效力驗證的方法來比較模型預測效力的優劣，本研究的實證結果為：針對樣本內資料，使用Robust Logistic Regression對於整個模型的預測效力的確有提升的效果；當營收品質成為模型變數的考量因素後，能讓模型有較高的預測效力。最後，本研究亦提出了一些重要的未來研究建議，以供後續的研究作為參考。 / The method implemented in PD calculation in this study is “Robust Logistic Regression”. We implement this method based on two reasons: 1. In panel data, outliers usually exist and they may seriously influence the empirical results. 2. In Logistic Model, outliers are not taken into consideration. The main purpose of implementing “Robust Logistic Regression” in this study is: eliminate the effects caused by the outliers in the data and improve the predictive ability. This study is the first study to implement “Robust Logistic Regression” in PD calculation. The same variables as those in Z-SCORE model are selected in this study. Furthermore, the quality of the revenue in a company is also considered. Therefore, we adjust the related variables with the company’s accounts receivable turnover ratio. Some validation methodologies for default risk models are used in this study. The empirical results of this study show that: In accordance with the in-sample data, implementing “Robust Logistic Regression” in PD calculation indeed improves the predictive ability. Besides, using the adjusted variables can also improve the predictive ability. In the end of this study, some important suggestions are given for the subsequent studies.

The thesis "Credit risk from Basel II point of view" deals with new capital concept with main focus on the credit risk. The particular emphasis is laid on the chief issue of Basel II concept i.e. internal models. The thesis quite in detail describes the usage of basel parameters - LGD particularly - in various day-to-day business processes of credit institutions. An individual part of the thesis is devoted to credit risk mitigants and their impacts on the amount of capital requirements. The analysis carried out precedent Basel II implementation indicated the launching of Basel II should imply risk weighted assests to credit risk decline. This documents the last chapter.

The impact of climate change on the financial performance of companies is of concern to bank credit processes. The main objective of this research was to develop a South African contextualised credit process that incorporates environmental risk. The research methodology comprised of a mixed-method being content analysis – the qualitative portion and the Probability of Default prediction using a Merton Model and the Hoffmann and Busch (2008) carbon risk analysis model - the quantitative portion. A content analysis of the banks’ Annual Reports, Integrated Reports and Sustainability Reports showed that, while South African banks follow a qualitative approach to embedding environmental risk into their credit process, none of the four banks that formed part of the study divulged their quantitative approach to embedding environmental risk. The study used a proximity matrix method to examine the level of embedding. The second part of the study, which used prior studies as the benchmark, adopted the following: (1) a simulated carbon tax regime as a proxy for an environmental risk, and (2) the Hoffmann and Busch (2008) carbon risk analysis tool and the Merton Model (1974) as the bank credit process proxies. The second part of the study used a sample of 33 JSE-listed Carbon Disclosure Project reporting companies out of a population of 107. The carbon risk analysis showed that the companies in the materials and energy sector have a high carbon risk. However, the results from the Merton Model showed that the companies have enough profit to cushion the additional carbon tax liability, given the insignificant shift in probability of default between the three scenarios, where financial data had (1) no carbon tax, (2) was adjusted for a carbon tax with incentives, and (3) adjusted for carbon tax without incentives. Triangulation of the results from the content analysis, carbon risk analysis and the probability of default analysis confirms that South African banks do not fully integrate environmental risk across the credit value chain or process in the 2010 to 2017 period. However, the carbon risk analysis shows a heavy dependency on carbon sources for critical inputs into the South African companies’ production processes, which if not checked, will affect the credit portfolios of banks. / Finance, Risk Management and Banking / D. Phil (Management Studies)

Les crises financières et les problèmes se formaient mais les indicateurs ne sont pas précis pour permettre une intervention réglementaire. La thèse propose un modèle dynamique pour le système bancaire avec une banque centrale afin de calculer un indicateur de faillite en fonction de la probabilité qu'une banque soit en faillite et les pertes rencontrées dans le réseau financier, une méthodologie qui peut améliorer la mesure, le suivi et la gestion du risque systémique.La thèse propose également des mécanismes de compensation : 1- avec un modèle considérant l'ancienneté du passif et avec un type d'actif liquide dont la vente excessive conduit à un impact sur le marché, 2 - avec un modèle considérant les participations croisées entres les banques dont les engagements interbancaires sont de différentes séniorités et avec un type d'actif liquide dont la vente excessive conduit à un impact sur le marché. / Financial crisis pose important theoretical problems on creating reliable indicator of stability of financial systems on which basis the regulators could intervene. The thesis proposes a dynamic model of banking system were the central bank can calculate an indicator of potential defaults taking into consideration the probability for a bank to default and the losses encountered in the financial network, a methodology that can improve the measurement, monitoring, and the management of the systemic risk. The thesis also suggests a clearing mechanisms : 1- in a model with seniority of liabilities and one type of liquid asset whose fire sale has a market impact, 2 - in a model with crossholdings among the banks whose interbank liabilities may be senior and junior and with one liquid asset whose firing sale has a market impact.