Improving Credit Risk Modelling Using Alternative Data Sources and Machine Learning in Mauritius

May 23, 2024May 23, 2024 Discipline: Data science

The research
will address the following questions and hypotheses:

  1. Question 1: Can
    alternative data sources, such as social media activity and transaction
    history, augment the accuracy of credit risk assessment compared to
    traditional credit scoring systems?
  2. Question 2: What
    machine learning algorithms, when applied to credit risk     modelling,
    proffer
    the highest predictive accuracy and efficiency?
  3. Question 3: How
    can the transparency and fairness of credit risk models be improved to
    provide actionable insights for lending institutions?

Through an
in-depth investigation of these questions, this research aims to stipulate valuable
insights and solutions to the financial industry, ultimately bettering credit
risk modelling and
lending practices
in Mauritius.

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