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Showing result 1 - 5 of 31 essays matching the above criteria.

  1. 1. Explainable Artificial Intelligence and its Applications in Behavioural Credit Scoring

    University essay from Stockholms universitet/Institutionen för data- och systemvetenskap

    Author : Robert Iain Salter; [2023]
    Keywords : Behavioural Credit Scoring; Deep Learning; Machine Learning; Long Short-Term Memory; Default Prediction;

    Abstract : Credit scoring is critical for banks to evaluate new loan applications and monitor existing customers. Machine learning has been extensively researched for this case; however, the adoption of machine learning methods is minimal in financial risk management. READ MORE

  3. 2. Modelling Proxy Credit Cruves Using Recurrent Neural Networks

    University essay from KTH/Matematisk statistik

    Author : Lucas Fageräng; Hugo Thoursie; [2023]
    Keywords : Deep Neural Networks; Credit Risk; Financial Modelling; LSTM; Credit Default Swaps; Credit Valuation Adjustment; Djupa Neurala Nätverk; Kreditrisk; Finansiell Modellering; LSTM; Kreditswappar; Kreditvärderingsjustering;

    Abstract : Since the global financial crisis of 2008, regulatory bodies worldwide have implementedincreasingly stringent requirements for measuring and pricing default risk in financialderivatives. Counterparty Credit Risk (CCR) serves as the measure for default risk infinancial derivatives, and Credit Valuation Adjustment (CVA) is the pricing method used toincorporate this default risk into derivatives prices. READ MORE

  4. 3. 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

  5. 4. Loan Loss Provisions and Lending Activity in Banks : A quantitative study comparing the effects of loan loss provisions on lending activity in banks applying IFRS 9 and ASC 326

    University essay from Umeå universitet/Företagsekonomi

    Author : Rikard Fredmer; Alicia Julienne Zanic; [2023]
    Keywords : Agency Theory; ASC 326; Basel III; Earnings Management; Expected Credit Loss es ; Expected Credit Loss Model; FASB; IASB; IFRS; IFRS 9; Lending Activity; Loan Loss Allowance s ; Loan Loss Provision s ; New Loans Originated; Procyclicality; Signaling Theory; Stewardship Theory; US GAAP;

    Abstract : 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. READ MORE

  6. 5. Credit scoring using Logistic regression

    University essay from Mälardalens universitet/Akademin för utbildning, kultur och kommunikation

    Author : Iftho Hara Khanam; [2023]
    Keywords : Kurtosis; Skewness; Winsorization; Logistic regression analysis; Maximum likelihood estimation; Newton–Raphson method; T–ratio test; P-value; LR test;

    Abstract : In this thesis, we present the use of logistic regression method to develop a credit scoring modelusing the raw data of 4447 customers of a bank. The data of customers is collected under 14independent explanatory variables and 1 default indicator. The objective of this thesis is toidentify optimal coefficients. READ MORE