This thesis examines how the implementation process of Expected Credit Loss Model in the accounting standard IFRS 9 – Financial instruments is perceived and interpreted and how these factors can affect comparability in accounting. One of the main changes with IFRS 9 is that companies need to account for expected credit losses rather than just incurred ones. The data is primarily collected through a web survey where all of Nordic banks and credit institutes with a minimum book value of total assets of euro 1 billion, are invited to participate. The presentation of the collected data from the web survey is reported relative frequencies in tables. The analysis is carried out with the assistance of the theoretical framework consisting of Positive Accounting Theory and Agency Theory. The conclusion of the thesis is that how the level of information in the implementation process is interpreted and perceived can affect comparability in accounting negatively due to the room for subjective interpretations.

Bakgrund och problemformulering: Den 1 januari 2018 infördesredovisningsstandarden IFRS 9 för finansiella instrument, vilken ersätter den tidigare kontroversiella standarden IAS 39. Den nya standarden innehåller bland annat en kreditförlustmodell som innebär att inte bara inträffade utan även förväntadek reditförluster ska redovisas, vilket skiljer sig från den tidigare kreditförlustmodellen där endast konstaterade förluster redovisades. Banker ansågs vara den typ av företagsom skulle komma att bli särskilt påverkade av den nya kreditförlustmodellen som IFRS 9 innebär. Bortsett från redovisningsstandarder har banker även Baselregelverket att förhålla sig till. Enligt Basel III, det nuvarande regelverket, finns det ett krav på att banker ska ha en kärnprimärkapitalrelation på minst 4,5 %. Kärnprimärkapitalrelationen kan komma att påverkas negativt om avsättningarna ökar, vilket kan bli en följd av IFRS 9. Syfte: Syftet med denna studie är att undersöka hur noterade banker inom Europeiska unionen påverkats av den nya IFRS 9 standarden. Med hjälp av två underfrågor till forskningsfrågan kommer bankers kärnprimärkapitalrelation och kreditförlustreserver att studeras. Forskningsmetod: Två hypoteser har formulerats för att besvara studiens syfte. Studien har utgått från en kvantitativ metod och en deduktiv ansats har tillämpats. Studiens population utgörs av noterade banker inom EU som därefter selekterats genom ett systematiskt urval. Data består av information hämtad från bankernas årsredovisningar från åren 2017 och 2018. Resultat och slutsats: Resultatet visade att båda hypoteserna kunde förkastas. Kreditförlusterna hade inte ökat signifikant och kärnprimärkapitalrelationen hade inte minskat signifikant

Bakgrund: En ny reglering har införts den 1 januari 2018, vilket är IFRS 9 finansiella instrument som ersätter IAS 39. Värdering och redovisning förändras från en objektiv till en subjektiv bedömning av kreditförluster. Syfte: Syftet med studien är att undersöka vilken effekt IFRS 9 har på svenska banker efter införandet. Studien undersöker även om effekten varierar beroende av bankers storlek. Metod: För att uppnå studiens syfte har en kvantitativ studie med deduktiv ansats tillämpats. Sekundärdata har inhämtats ur bankernas årsredovisningar för 2018 från respektive hemsida. Banker som ingår i studien är 43 svenska banker som står under Finansinspektionens tillsyn. Studiens tre hypoteser testades med hjälp av ttest, där parvis observation gjordes mellan åren 2017 och 2018. Resultat och slutsats: Resultatet visade en signifikant förändring av totala kapitalrelationen och kärnprimärkapitalrelationen i de större bankerna, vilka nyckeltalen var lägre efter införandet av IFRS 9. Egna kapitalet, kreditförlusterna och soliditeten kunde inte visa någon signifikant förändring. Slutsats av studiens resultat är att införandet av IFRS 9 haft en marginell effekt på svenska banker. / Background: The new regulation IFRS 9 has replaced IAS 39. The new regulation is subjective, forward-looking, compared with the old, objective model. Purpose: The purpose of our study was to investigate the effect IFRS 9 has on Swedish banks after the transition. Another aim is to study the effect of IFRS 9 on different bank sizes. Method: To achieve the purpose of the study, a quantitative method has been applied. Data has been obtained from annual reports for the year of 2018. The data consist of shareholders equity, balance sheet total and reported loan losses. Hypothesis testing has been done by using t-test Result and conclusion: The results can support a week significant positive effect on Tier 1 capital and capital adequacy ratio from large banks. No results could be found for Shareholders equity, Credit loss or Solidity.

In this master thesis, the impact of forward-looking macroeconomic information under IFRS 9 is studied using fictional data from a Swedish mortgage loan portfolio. The study employs a time series analysis approach and employs vector autoregression models to model expected credit loss parameters with multiple incorporated macroeconomic parameters. The models are analyzed using impulse response functions to study the impact of macroeconomic shocks and the results show that the unemployment rate, USD/SEK exchange rate and 3-month interest rates have a significant impact on expected credit losses. / I detta examensarbete studeras effekterna av framåtblickande makroekonomisk information enligt IFRS 9 med fiktiv data baserad på en svensk bolåneportfölj. Studien använder sig av tidsserieanalys och vektorautoregressionsmodeller för att modellera förväntade kreditförlust-parametrar med flera inkorporerade makroekonomiska parametrar. Modellerna analyseras med hjälp av impulsresponsfunktioner för att studera effekterna av makroekonomiska chocker. Resultaten visar att arbetslöshet, USD/SEK växelkurs och 3-månaders räntor har en signifikant inverkan på förväntade kreditförluster.

The financial crisis of 2008 highlighted problems with the accounting standard IAS 39, with claims of high complexity, introduction of procyclicality in the financial statements and a proposed role of contributing to the financial crisis. The International Accounting Standard Board issued the predecessor, IFRS 9, which became effective on January 1st, 2018. IFRS 9 introduces a forward-looking Expected Credit Loss model, which significantly change the accounting of loss provisions. With the objective to provide high accounting quality, the International Accounting Standard Board and Financial Accounting Standard Board develop accounting standards based on the conceptual framework, consisting of qualitative characteristics. The study addresses the accounting quality of IFRS 9 through the research question; What implications does IFRS 9 have for equity analysts?  In order to capture the implications, a survey is designed, to reach out to accessible equity analysts of European banks. The results show that the Expected Credit Loss model under IFRS 9 implicate difficulties for equity analysts. Three themes of implications are identified, Time aspect, Complexity and Comparison. Although IFRS 9 provides useful information for the respondents, there are tendencies of a trade-off between relevance and faithful representation. The accounting quality of faithful representation is valued low due to high complexity and low comparability, which might be derived from that IFRS 9 is newly implemented. Despite the implications of IFRS 9, respondents find impairments, today, to be low and a non-vital part of the valuation process of the banking industry.

In recent years, the Buy Now Pay Later (BNPL) consumer credit industry associated with e-commerce has been rapidly emerging as an alternative to credit cards and traditional consumer credit products. In parallel, the regulation IFRS 9 was introduced in 2018 requiring creditors to become more proactive in forecasting their Expected Credit Losses and include the impact of macroeconomic factors. This study evaluates several methods of supervised statistical learning to model the Probability of Default (PD) for BNPL credit contracts. Furthermore, the study analyzes to what extent macroeconomic factors impact the prediction under the requirements in IFRS 9 and was carried out as a case study with the Swedish fintech firm Klarna. The results suggest that XGBoost produces the highest predictive power measured in Precision-Recall and ROC Area Under Curve, with ROC values between 0.80 and 0.91 in three modeled scenarios. Moreover, the inclusion of macroeconomic variables generally improves the Precision-Recall Area Under Curve. Real GDP growth, housing prices, and unemployment rate are frequently among the most important macroeconomic factors. The findings are in line with previous research on similar industries and contribute to the literature on PD modeling in the BNPL industry, where limited previous research was identified. / De senaste åren har Buy Now Pay Later (BNPL) snabbt vuxit fram som ett alternativ till kreditkort och traditionella kreditprodukter, i synnerhet inom e-handel. Dessutom introducerades 2018 det nya regelverket IFRS 9, vilket kräver att banker och andra kreditgivare ska bli mer framåtblickande i modelleringen av sina förväntade kreditförluster, samt ta hänsyn till effekter från makroekonomiska faktorer. I denna studie utvärderas flera metoder inom statistisk inlärning för att modellera Probability of Default (PD), sannolikheten att en kreditförlust inträffar, för BNPL-kreditkontrakt. Dessutom analyseras i vilken utsträckning makroekonomiska faktorer påverkar modellernas prediktiva förmågor enligt kraven i IFRS 9. Studien genomfördes som en fallstudie med det svenska fintechföretaget Klarna. Resultaten tyder på att XGBoost har den största prediktionsförmågan mätt i Precision-Recall och ROC Area Under Curve, med ROC-värden mellan 0.80 och 0.91 i tre scenarier. Inkludering av makroekonomiska variabler förbättrar generellt PR-Area Under Curve. Real BNP-tillväxt, bostadspriser och arbetslöshet återfinns frekvent bland de viktigaste makroekonomiska faktorerna. Resultaten är i linje med tidigare forskning inom liknande branscher och bidrar till litteraturen om att modellera PD i BNPL-branschen där begränsad tidigare forskning hittades.

The number of statistical and mathematical credit risk models that financial institutions use and manage due to international and domestic regulatory pressures in recent years has steadily increased. This thesis examines the evolution of model risk management and provides some guidance on how to effectively build and manage different bagging and boosting machine learning techniques for estimating expected credit losses. It examines the pros and cons of these machine learning models and benchmarks them against more conventional models used in practice. It also examines methods for improving their interpretability in order to gain comfort and acceptance from auditors and regulators. To the best of this author’s knowledge, there are no academic publications which review, compare, and provide effective model risk management guidance on these machine learning techniques with the purpose of estimating expected credit losses. This thesis is intended for academics, practitioners, auditors, and regulators working in the model risk management and expected credit loss forecasting space. / Dissertation / Doctor of Philosophy (PhD)

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.