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Visar resultat 1 - 5 av 50 uppsatser som matchar ovanstående sökkriterier.

  1. 1. Credit Card Approval Prediction : A comparative analysis between logistic regressionclassifier, random forest classifier, support vectorclassifier with ensemble bagging classifier.

    Kandidat-uppsats, Blekinge Tekniska Högskola/Institutionen för datavetenskap

    Författare :Dhanush Janapareddy; Narendra Chowdary Yenduri; [2023]
    Nyckelord :Machine Learning; Logistic Regression; Random Forest; Support Vector Machine; Ensemble Learning Bagging.;

    Sammanfattning : Background. Due to an increasing number of credit card defaulters, companies arenow taking greater precautions when approving credit applications. When a customermeets certain requirements, credit card firms typically use their experience todecide whether to grant them a credit card. LÄS MER

  3. 2. ESG-betygets påverkan på kreditbetyget : En kvantitativ studie på 311 publika nordiska företag

    Kandidat-uppsats, Högskolan i Gävle/Företagsekonomi

    Författare :Jessica Berg; Josephine Persson; [2023]
    Nyckelord :ESG; CSR; credit rank; credit risk; stakeholder theory; principal-agent-theory; Nordic.; ESG; CSR; kreditbetyg; kreditrisk; intressentteorin; principal-agent-teorin; Norden;

    Sammanfattning : Syfte: Syftet med denna studie var att undersöka om det finns ett samband mellan ESG-betyg och kreditbetyg hos publika företag i Norden. Forskningsfrågorna som skulle besvaras var om det finns samband mellan ESG, dess dimensioner, och kreditbetyg, samt om det finns skillnader i dessa samband mellan sektorer och länder. LÄS MER

  4. 3. RNN-based Graph Neural Network for Credit Load Application leveraging Rejected Customer Cases

    Uppsats för yrkesexamina på avancerad nivå, Högskolan i Halmstad/Akademin för informationsteknologi

    Författare :Oskar Nilsson; Benjamin Lilje; [2023]
    Nyckelord :Machine Learning; Deep Learning; Reject Inference; GNN; GCN; Graph Neural Networks; RNN; Recursive Neural Network; LSTM; Semi-Supervised Learning; Encoding; Decoding; Feature Elimination;

    Sammanfattning : Machine learning plays a vital role in preventing financial losses within the banking industry, and still, a lot of state of the art and industry-standard approaches within the field neglect rejected customer information and the potential information that they hold to detect similar risk behavior.This thesis explores the possibility of including this information during training and utilizing transactional history through an LSTM to improve the detection of defaults. LÄS MER

  5. 4. Green corporate loans : A model-creating study exploring what information is used and its role when assessing green corporate loans

    Master-uppsats, Uppsala universitet/Företagsekonomiska institutionen

    Författare :Maria Rydén; Lourdes Zemariam Ermias; [2023]
    Nyckelord :Green Finance; Green Corporate Loans; Hard Information; Soft Information; Information Asymmetry; Relationship Lending; Transactional Lending;

    Sammanfattning : Banks have a vital role in the society-wide green transition. However, the field of green finance is relatively unexplored in academia. LÄS MER

  7. 5. Peeking Through the Leaves : Improving Default Estimation with Machine Learning : A transparent approach using tree-based models

    Uppsats för yrkesexamina på avancerad nivå, Umeå universitet/Institutionen för matematik och matematisk statistik

    Författare :Elias Hadad; Angus Wigton; [2023]
    Nyckelord :Machine learning; Expected credit loss; Probability of default; ECL; PD; Risk Management; Credit Risk Management; Default Estimation; AI; Artificial intelligence; Fintech; Supervised learning; Decision tree; Random forest; XG boost; Transparency; Machine learning transparency;

    Sammanfattning : 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. LÄS MER