As a response to the financial crisis of 2008 the IASB and the FASB developed IFRS 9 and ASC 326, respectively. These accounting regulations are supposed to increase reporting transparency and promote financial stability by determining the calculation and recognition of loan loss provisions. However, previous literature has brought up concerns that loan loss provisions can negatively impact the lending activity in banks. If that was the case, they would negatively affect the amount of capital available in an economy and thereby threaten financial stability and economic growth especially during times of economic downturns. To shed light on this topic, this thesis investigates the relationship between loan loss provisions and lending activity in banks applying IFRS and US GAAP. The thesis provides practical as well as theoretical implications as it discusses the findings in a practical context and relates it to relevant theories.  The dataset utilized includes empirical data from Q1 2020 until Q4 2022 and covers 330 banks from 38 countries. The data was gathered from Refinitiv´s Eikon database as well as from the International Monetary Fund. It was then statistically analyzed by conducting different kinds of statistical inference. All methods applied are of a quantitative nature and the underlying methodology is positivist. The results of this thesis suggest that loan loss provisions under IFRS 9 are on average higher than under ASC 326. Further, it was found that loan loss provisions under IFRS 9 exhibit a statistically significant negative relationship with lending activity. In contrast, this relationship was found to be insignificant under ASC 326. Together, these findings suggest that higher loan loss provisions have a negative effect on lending activity. It is concluded that the impairment model of IFRS 9 might compromise financial stability by limiting lending activities during times of economic turmoil.  Additionally, due to the increased room for managerial judgment under IFRS 9 it is theorized that the higher loan loss provisions can be the result of earnings management. Loan loss provisions under IFRS 9 could thus be more supported by Agency theory. On the other hand, ASC 326 offers less room for managerial discretion and could be more supported by Stewardship theory. This thesis also suggests topics for potential future research. The knowledge about loan loss provisions and their effects on lending activity could be extended by using different variables in the regression model. Additionally, a longer timeframe as well as other accounting standards could be investigated. Furthermore, the effects of loan loss provisions on loan quality and risk management in banks are in need of further examination. Lastly, the capital requirements of Basel III and their impact on procyclicality should be researched.

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 credit crunch that started the 9th of August 2007 is generally viewed as the most significant crisis to affect the financial markets and the global economy since the 1930s.The UK financial sector was heavily hit by the crisis which resulted in a dry up in lending and left a black hole in the British banks‟ finances. During the last quarter of 2010 the GDP shank unexpectedly with 0.5 percent from the third quarter which created concerns about going back into the recession. Contrarily, for Swedish economy 2010 was an impressing year with an unexpected GDP growth of 7, 3 percent in the last quarter.The purpose of this study is to analyse how the finance crisis has affected the leading banks‟ performance within the two countries and see whether the differences in values can explain the difference in GDP growth during the last quarter of 2010. The analyse is performed through a financial ratio analysis of the different banks.The final results of the research indicates to that the Swedish banks have been more profitable, have had a more secure and higher quality of lending and more capacity to lower cost related to income than the British banks. The more distinctive negative influence is mostly based on the larger amount of credit losses the British banks had to experience which contributed to their significant decrease in earnings per share which created scepticism on the credit market followed by a severe slowdown in consumption and in GDP growth. Since the credit losses never got to same levels in Sweden as in the UK the scepticism of the Swedish banking system did not affect the reduction in credit use and house prises to the same extent and GDP growth could recover back to normal levels sooner than in the UK.

本研究探討台灣於2011年依據IAS 39進行34號公報之第三次修訂實施，採用已發生損失模型後的兩項議題：（1）放款壞帳費用之提列與盈餘波動性以及資本適足率波動性之關聯性，（2）以歷史成本評價之期末金額及以公允價值評價之期末金額，究竟何者對於未來之帳款沖銷與不良債權較具有關聯性。 實證結果顯示，自2011年採用已發生損失模型後盈餘波動性無顯著之變化，且壞帳費用對於盈餘波動性無解釋能力；而自2011年後資本適足率波動性亦無顯著變化，但壞帳費用對於資本適足率波動性有顯著的影響，顯示銀行明顯透過壞帳費用之提列進行資本管理而非盈餘管理。在未來信用損失預測之部分，以歷史成本評價之期末放款金額對於未來之帳款沖銷及不良債權有顯著的負相關，而以公允價值評價之期末放款金額對於未來之帳款沖銷及不良債權卻無解釋能力，可能係因未來帳款沖銷與未來不良債權之發生與放款之帳齡有顯著的關聯性，而與未來可收取之現金流量無顯著之相關。 / This study aims to investigate how Incurred Loss Model affects the recognition of loan loss provisions and the valuation of loans due to the third revision of SFAS No. 34 which was revised based on IAS 39 in 2011. For the recognition of loan loss provisions, it focuses on the relationship with earnings volatilities and capital adequacy volatilities, and for the valuation of loans, it specializes on whether credit loss predicting is related to historical cost accounting or fair value accounting. The result shows that, since the implementation of Incurred Loss Model in 2011, both the adoption of Incurred Loss Model and the loan loss provisions have no significant impact on earnings volatilities. For capital adequacy volatilities, implementing Incurred Loss Model has no effect on capital adequacy volatilities neither. However, the loan loss provisions since 2011 significantly enhance the volatilities of capital adequacy. It reveals that banks use loan loss provisions to manage capitals instead of earnings. For credit loss predicting, loans evaluated with historical cost accounting have significant negative relations with future charge-offs and non-performing loans while loans evaluated under fair value accounting do not have any explanation power. It may suggests that future charge-offs and non-performing loans are related to the aging of loans, but not the future payoffs of loans.

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.