- PS 2018
PS 2018
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PS18001_Evaluation-of-the-most-pragmatic-approach-to-expected-credit-loss
Project goal: Under IFRS9 the concept of lifetime loss requiring PIT estimates for both stage 2 and stage 3 was introduced. Whilst methodologies are in their infancy the current understanding is that a loss rate or direct approach to ECL is allowed for which is far simpler and possibly more accurate than determining each of the parameters PD, EAD and LGD in isolation. The research problem would then be to evaluate the most pragmatic approach given that accuracy of ECL is the primary objective.
Problem statement
Problem statement
Size: 283.15 KB
Hits: 215
Date added: 15-02-2019
Date modified: 04-02-2020
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PS18002_The-use-of-artificial-intelligence-in-risk-measurement-and-decision-making
Project goal: Developing models that use artificial intelligence / machine learning approaches towards risk research. This includes comparative analysis with more established / traditional methodologies.
Problem statement
Problem statement
Size: 123.74 KB
Hits: 156
Date added: 15-02-2019
Date modified: 04-02-2020
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PS18003_The-use-of-Global-Credit-Data-(GCD)-data-in-LGD-modelling-in-SA
Project goal: To evaluate the appropriateness/representativeness of pooled credit data, specifically the Global Credit Data (GCD) (formerly known as the Pan-European Credit Data Consortium), in the development of new loss given default (LGD) models for the corporate portfolio and potentially other portfolios.
Problem statement
Problem statement
Size: 133.17 KB
Hits: 349
Date added: 15-02-2019
Date modified: 04-02-2020
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PS18004_Machine-learning-and-its-applications-in-predictive-modelling
Project goal: The goal of the project is to propose new methodologies (machine learning) in the predictive modelling context, with a specific focus on: • Factorization• Variable selection using knock-offs• Quantile regression
Problem statement
Problem statement
Size: 25.70 KB
Hits: 178
Date added: 15-02-2019
Date modified: 04-02-2020
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PS18005_Investigating-the-transparent-representation-of-machine-learning-applications
Project goal: The goal of the project is to investigate machine learning models for use in retail credit scoring, with a specific focus on: • Transparent representation of machine learning models• Stakeholder insights into so called “black-box” modelling
Problem statement
Problem statement
Size: 125.62 KB
Hits: 200
Date added: 15-02-2019
Date modified: 04-02-2020