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    •   BracU IR
    • School of Engineering and Computer Science (SECS)
    • Department of Computer Science and Engineering (CSE)
    • Thesis & Report, BSc (Computer Science and Engineering)
    • View Item
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    Building a credit scoring model to assign a reference score based on credit transaction and relevant profile data

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    16101084,19141019,16101216,16101320_CSE.pdf (811.8Kb)
    Date
    2019-09
    Publisher
    Brac University
    Author
    Islam, Saqib Al
    Aziz, Rifah Sama
    Ahmed, Aritra
    Abida, Fauzia
    Metadata
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    URI
    http://hdl.handle.net/10361/13644
    Abstract
    A credit score is a numerical expression based on a level analysis of a person's credit files, to represent the creditworthiness of an individual. The credit score plays a major role in banks, financial institutions loaning money to individuals for their personal or business needs. This score is given based on factors such as personal information, assets, financial behavior and financial history. This system is not digitized or implemented yet in Bangladesh. So our aim is to build a reliable and robust credit scoring model which would help institutions like such to have an accurate reference score to rely on when validating a client. We were able to obtain an optimized model with an accuracy of( 93%). The model is based on CART(Classification and Regression Trees) using Gradient Boosting method(GBM). We also proposed a new hybrid model consisting of a two step architecture. The first one based on distributed Random Forests, the individual decision tree outputs of which was fed into a Deep Neural Network(DNN), and trained on to achieve marginally better results than using only Random Forest approach. Since, credit scoring an individual is a sensitive issue, it is not ethical to provide a score without proper justification. We conducted interpret-ability analysis on our model and generated visual representations of the criterion affecting the output of our model and provide necessary information to analyze the client efectively. Our results were conclusive and imitated the process of evaluating an individual precisely. The work- ow we proposed could be implemented in production to provide a concrete base for evaluation and prediction of defaulters. Simultaneously provide a detailed overview of the results obtained. This could help financial institutions immensely and help them save millions lost by default loans.
    Keywords
    Credit score; Credit risk; Loan assessment; Machine learning; Artifcial intelligence; Random forests; Gradient boosting; GBM; Extreme gradient boosting; Deep neural networks; Interpret-ability
     
    LC Subject Headings
    Finance--Data processing.
     
    Description
    This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2019.
     
    Cataloged from PDF version of thesis.
     
    Includes bibliographical references (pages 49-52).
    Department
    Department of Computer Science and Engineering, Brac University
    Collections
    • Thesis & Report, BSc (Computer Science and Engineering)

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