Essays about: "credit score model"

Showing result 1 - 5 of 23 essays containing the words credit score model.

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

    University essay from Blekinge Tekniska Högskola/Institutionen för datavetenskap

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

    Abstract : 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. READ MORE

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

    University essay from Högskolan i Halmstad/Akademin för informationsteknologi

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

    Abstract : 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. READ MORE

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

    University essay from Uppsala universitet/Företagsekonomiska institutionen

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

    Abstract : Banks have a vital role in the society-wide green transition. However, the field of green finance is relatively unexplored in academia. READ MORE

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

    University essay from Umeå universitet/Institutionen för matematik och matematisk statistik

    Author : Elias Hadad; Angus Wigton; [2023]
    Keywords : 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;

    Abstract : 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. READ MORE

  6. 5. Application of the Merton Model and the Altman Z-score Model in Credit Risk Assessment - an Empirical Study on Chinese Listed Companies

    University essay from Lunds universitet/Nationalekonomiska institutionen

    Author : Runzhou Chen; Hongzhe Fu; [2023]
    Keywords : Credit risk assessment; the Merton Model; The Altman Z-score model; Chinese market; Business and Economics;

    Abstract : Corporate default poses significant risks to investors and stakeholders, highlighting the importance of predicting and managing financial risk effectively. When the geographical scope is narrowed down to China, the unique characteristics of the Chinese market, such as the lack of comprehensive credit risk databases and the influence of state-owned enterprises and small-medium enterprises, present challenges in accurately assessing creditworthiness. READ MORE