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.

Made available in DSpace on 2014-12-17T14:13:32Z (GMT). No. of bitstreams: 1 NilbaLS_Tese.pdf: 636235 bytes, checksum: 871e98d076a82a24e17c822a9ea7198e (MD5) Previous issue date: 2009-12-18 / Hypertensive syndromes in pregnancy (HSP) are configured as one of the major complications in the pregnancy and postpartum period and can lead premature newborn and subsequent hospitalization of the newborn to the Neonatal Intensive Care Unit (NICU). This study aimed to analyze the perceptions, meanings and feelings of mothers on the hypertensive syndromes in pregnancy and premature obstetric labor. The research was qualitative and has a theoretical methodological the Social Representations Theory(SRT) in the approach to the Central Nucleus Theory. The study included 70 women, mean age 29 years, predominantly school to high school, most of them married or in consensual union, primiparous and prevalence of cesarean delivery occurred between 32 and 37 weeks of pregnancy.The data were collected from may to december 2008 in the Maternity School Janu?rio Cicco in Natal , and obtained through the following instruments for data collection: questionnaire including questions about socio-demographic status; the Free Words Association Test (FWAT) and and verbalized mental image construction used three stimuli: such as pregnancy with high blood pressure, preterm birth and NICU, and interview with the following guiding question: what it meant for you to have a pregnancy with high blood pressure and consequently the birth of a premature baby? Data analysis was performed using multi-method obtained from the data processing by EVOC (Ensemble Programmes Permettant L 'Analyze des ?vocations) and ALCESTE (Analyse Lexicale par Contexte d'un Ensemble de Segment de Texte) and thematic analysis in categories. The results will be presented in four thematic units under the following representative universes: HSP, prematurity as a result of HSP, NICU and the social representations of mothers on the hypertensive disorder of pregnancy sequenced premature birth and hospitalization of the child in the NICU. The results obtained by multimethod analyses showed similar constructions and point to death as the central nucleus and negative aspects, coping strategies, need of care, knowledge about the disease, fragility and meanings of the NICU as peripheral elements. It is considered that the perceptions, meanings and feelings of puerperal women in relation to HSPs and to premature delivery are a negative social representation, with representational elements that may have influenced the adverse effects on the disease and its consequences. We suggest action on the peripheral elements of this representation, with adequate orientation, early diagnosis, effective conduct, receptive attitude on the part of the team, health promotion measures and effective public policies, in order to improve the care provided to puerperal women, making them feel welcome and minimizing their suffering / As s?ndromes hipertensivas da gravidez (SHG) s?o complica??es do per?odo grav?dico puerperal e podem levar ? prematuridade neonatal e consequente hospitaliza??o do rec?m-nascido em Unidade de Terapia Intensiva Neonatal (UTIN). O estudo objetivou compreender percep??es, significados e sentimentos de pu?rperas relativos ?s SHGs que tiveram como consequ?ncia o parto prematuro e a hospitaliza??o do filho na UTIN. ? um estudo qualitativo que teve como referencial te?rico metodol?gico a Teoria das Representa??es Sociais (TRS), na abordagem complementar da Teoria do N?cleo Central. Participaram do estudo 70 mulheres ; com idade m?dia de 29 anos; a maioria com n?vel m?dio de escolaridade; em uni?o consensual; prim?paras e com parto ces?rio ocorrido entre 32 e 37 semanas de gesta??o. Os dados foram coletados de maio a dezembro de 2008, na Maternidade Escola Janu?rio Cicco, em Natal/RN, por meio de: question?rio para caracteriza??o sociodemogr?fica das participantes; t?cnica de associa??o livre de palavras (TALP); constru??o verbalizada de imagens mentais; e entrevista, com a seguinte quest?o norteadora: O que significou para voc? ter uma gravidez com press?o alta e, como consequ?ncia, o nascimento de um filho prematuro? A an?lise de dados foi realizada por meio de multim?todos, a partir do processamento de dados pelos software EVOC (Ensemble de programmes permettant L analyse des evocations) e ALCESTE (An?lise lexical por contexto em um conjunto de segmentos de texto), e an?lise tem?tica categorial. Os resultados foram apresentados em quatro unidades tem?ticas, no ?mbito dos seguintes universos representativos: SHG; prematuridade como consequ?ncia das SHGs; UTIN; e as representa??es sociais de pu?rperas sobre as SHGs seguidas do nascimento prematuro e hospitaliza??o do filho na UTIN. Os resultados obtidos por meio dos multim?todos de an?lise apresentaram constru??es semelhantes entre si e apontam a morte como n?cleo perif?rico, aspectos negativos, estrat?gias de enfrentamento, necessidades de cuidados, conhecimento sobre a doen?a, fragilidade e significados da UTIN. Considera-se que as percep??es, significados e sentimentos de pu?rperas relativas ?s SHGs e ao parto prematuro constituem uma representa??o social negativa, com elementos representacionais que podem ter influenciado os efeitos adversos sobre a doen?a e suas consequ?ncias. Sugere-se atua??o sobre os elementos perif?ricos dessa representa??o, com orienta??es adequadas, diagn?stico precoce, condutas efetivas, postura acolhedora da equipe, atitudes de promo??o ? sa?de e pol?ticas p?blicas eficazes, de forma a melhorar a assist?ncia ?s mulheres/pu?rperas, acolhendo-as adequadamente e minimizando seus sofrimentos

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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.

This study was conducted to identify and characterize rumen bacteria that are able to degrade the toxic compound, 3-hydroxy-4(lH)-pyridone (3,4 DHP), that is produced in the rumen from mimosine. Mimosine is a non-protein amino acid that is found in leaves and seeds of Leucaena leucocephala, a leguminous tree used as a forage crop for ruminants in the tropics, and degradation of 3,4 DHP by ruminal microbes is critical for protection of animals from leucaena toxicity. Microbes with this capacity are, however, not ubiquitous and microbial populations in the rumens of animals in some parts of the world are unable to metabolize 3,4 DHP. Four strains of obligately anaerobic, gram-negative, rod-shaped bacteria that degrade 3,4 DHP were isolated from rumen contents from a goat in Hawaii. The isolates do not ferment carbohydrates, but are able to use both 3,4 DHP and its isomer, 3-hydroxy-2(lH)-pyridone (2,3 DHP), as well as arginine and histidine as substrates for growth. Comparisons of the 16S rRNA sequence from one of these isolates with sequences from a widely diverse group of bacteria agree with other information indicating that these isolates do not fit into any existing taxon. Thus, we are hereby proposing a new genus and species designation, Synergistes jonesii, for these organisms.