BUSM203
AI for Business
2024/2025
Module Level Learning Outcomes to be assessed (Reference to Module Proposal Form)
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No
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Module Learning Outcome
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Description
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1
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A1
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Understand AI and how it can benefit businesses
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2
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A2
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Understand applications of AI in practice and assess capabilities of core AI tools/technologies
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3
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A3
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Understand the role of data and big-data and how to leverage these for creating customer value
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4
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A4
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Understand ethical implications and limitations of AI in terms of the economy, government and society
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5
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B1
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Identify the potential of AI for creating benefits for organisations and stakeholders
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6
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B2
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Evaluate the appropriateness of AI tools depending on the business problem at hand
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7
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B3
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Critically evaluate the limitations and application of AI, including ethical implications
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8
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C1
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Ability to critically evaluate technological advances
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9
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C2
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Ability to find the right AI tools for the (business) problem that needs addressing
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10
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C3
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Assess practical challenges in implementation of AI technology
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11
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C4
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Appreciate limitations and ethical implications for multiple stakeholders
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Assurance of Learning (selected modules only): contribution to Programme Level Learning Outcomes
(see programme rubric map)
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No
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Programme
Learning Outcome
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Description
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1
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1.1
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Evaluates the breadth and depth of the debates in the relevant field
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2
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1.5
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Demonstrates reflection on the choice of research methods and approaches, including any relevant issues or obstacles.
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3
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2.1
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Selects credible sources of data
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4
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2.10
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Recommends solutions that could be applied in practice
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5
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3.2
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Expresses arguments coherently through writing
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6
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3.4
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Displays good structure, formatting, style. and presentation of writing
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7
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3.5
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Cites sources of information and data using consistent and a recognised
referencing style
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Assessment instructions for students (as per QMPlus ‘Assessment Information’ tab)
DELIVERABLES
You are required to produce a written report, which is presented in a professional style. (as opposed to a theoretical essay) with graphs/tables and references where relevant (these can include industry sources as well as academic). The report will be submitted to a client, which is a bank. The bank has shared a dataset along with descriptions, which is shared in the module assessment page. The report should address the following:
1. Part A: Predict the probability of loan sanction
The client expects you to build a basic Artificial Neural Network (ANN) model to predict and understand the probability of loan sanction. Here are the relevant instructions:
• Initial Analysis - Perform. ANN analysis.
In the report, describe which variables you selected, how you designated these in the model (i.e. Dependent Variable, Factor, Covariate), and your evaluation of the model’s performance (e.g. percentage of incorrect predictions). [5 Marks]
• Model Tuning - Make one change to the model and rerun the analysis.
In the report, describe the tuning task you performed with a brief rationale, and your evaluation of the tuned model’s performance. Compare the performances of the tuned model to the initial model (i.e. consider the pros and cons of each model) and state which model is better. [10 Marks]
Further, examine the relative importance of predictors. You can do this in SPSS via the Output menu -> tick the box for “Independent Variable Importance Analysis” (see screenshot below).
• Compare the loan sanction factors for male vs female
In the report, first discuss the importance of the predictors (independent variables) for loan sanction in general (all data). Then, discuss the loan sanction factors for male vs female in as much detail as possible. For example, you should discuss which factors are more focused for male applicants and which factors get more focus for female applicants. Is there any change? If yes, why? [10 Marks]
2. Part B: Develop a segment-based loan sanction strategy.
The client is interested in understanding the different segments of loan applicants they currently have, with a view to using this knowledge for improved future targeting (and growth). The client expects you to conduct a cluster analysis to identify and understand current applicant segments.
• Initial Analysis:
In the report, discuss the initial cluster solution resulting from the above analysis. This should include (as a minimum), an evaluation of the cluster solution returned (i.e. how well does the model perform?), and the number, size, and characteristics of the clusters/segments (describe them according to the segmentation criteria specified). [10 marks]
• Model Tuning - Make one change to the model and rerun the analysis.
In the report, discuss the change you made to the initial model, offering a brief rationale for it. Then, discuss the performance of the tuned model in comparison to the initial model – this will include comparisons of the number, size, and characteristics of the segment solutions, as well as a performance metric (i.e. Silhouette score). Conclude by stating which model is better based on your evaluation. [10 Marks]
• Recommend the loan marketing strategy based on your analysis. What other products of the bank can be sold to these loan applicant segments. Explain with rationale. [10 Marks]
3. Part C: Discuss the potential for innovation based on emergent technologies.
As mentioned earlier, the client also considers innovation a priority alongside growth. So you have been instructed to examine the potential for leveraging emergent technologies – specifically, the metaverse (including NFTs, blockchain, cryptocurrency, and game engines) and service robots (including humanoid robots, avatars, chatbots, and voice assistants) – for growing the client’s business through introduction of a new business model or revenue stream. Here are the requirements:
• Conduct further research on the service robots and metaverse in the context of banking through academic sources, market research databases and reputed media outlets. Please cite your information sources appropriately.
In the report, provide an introduction and a critical evaluation of these two technologies for the client, highlighting the latest developments, and the positive and negative aspects of the technologies in general. [15 Marks]
• In recent times, generative AI has become an important and path breaking innovation. Yet, the bank’s leadership is not sure how they can use it for their growth. Create a research driven report on how Generative AI can help the bank. [15 Marks]
• The bank employees are skeptical that AI can take away their jobs. The employee union of the bank is against application of AI in the bank. Customers are also skeptical about the use of AI in the bank. How should the company convince its employees and customers while applying AI in bank activities? [10 Marks]
4. Overall presentation of the report – professional layout and report style. appropriate for business/executive readers, writing is clear and to the point, logical/coherent arguments to support points and recommendations, charts/figures and tables (where applicable) to help readers understand key points etc. [5 marks]