AI & Machine Learning in Finance: AI Applications in the Financial Industry – Panel Discussion Accessible from:

QUESTION 1 (20 Marks)
With reference to the presentation made by Kathryn Kaminski from Alpha Simplex critically discuss why she advocates for adaptive models as compared to static models within the financial sector. Your response should also provide for the weaknesses associated to adaptive models. Refer to relevant examples and illustrations.
QUESTION 2 (30 Marks)
Considering the presentation made by Salve Bergstrom from Lynx Asset Management AB on machine learning prepare a report in which you address the following from a financial sector perspective.
-describe and evaluate machine learning in the financial sector
-review why many ML tools have struggled to yield positive results within the financial sector.
QUESTION 3 (20 Marks)
Examine how might ABSA, a financial institution headquartered in South Africa and extending its operations to countries like Botswana and Namibia, benefit from incorporating simulation as a risk modelling tool? Explore the various types of models that can be embraced by the organisation.
QUESTION 4 (30 Marks)
As per the CEO of ABSA’s perspective, sensitivity analysis involves examining how changes in input values impact model outputs. With your modelling expertise, the CEO seeks insights into projecting the organization’s annual revenue. In addressing this inquiry, undertake a sensitivity analysis highlighting the key variables influencing the organization’s profitability. Enumerate and analyse these variables, and additionally, present a detailed Excel spreadsheet illustrating the sensitivity of the variables to demonstrate their impact on revenue projections over the course of 12 months.

Answers to Above Questions on Risk Modelling

Answer 1: There are a number of reasons for which adaptive models are recommended over static models in the financial sector. As evaluated in the presentation made by Kathryn Kaminski, the adaptive model is recommended because financial markets are dynamic and change frequently over economic events and geo political developments. Adaptive models are therefore effective in making adjustments in real time and thereby allows for accurate predictions.


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