| Module Code: |
H9AIFF |
|
Long Title
|
AI for Finance
|
|
Title
|
AI for Finance
|
| Module Level: |
LEVEL 9 |
| EQF Level: |
7 |
| EHEA Level: |
Second Cycle |
| Module Coordinator: |
Rohit Verma |
| Module Author: |
Andrea Del Campo Dugova |
| Departments: |
School of Computing
|
| Specifications of the qualifications and experience required of staff |
Lecturer PhD/Master’s degree in a computing or cognate discipline. May have industry experience also.
Tutor PhD/Master’s degree in a computing or cognate discipline. May have industry experience also.
|
| Learning Outcomes |
| On successful completion of this module the learner will be able to: |
| # |
Learning Outcome Description |
| LO1 |
Develop a systematic understanding of AI related terminologies such ML, Data Science and Big Data and their associated process flows. |
| LO2 |
Critically explore the major applications of AI and the technological disruptions brought about by AI to Finance |
| LO3 |
Research the challenges and evolving opportunities for AI in the finance world |
| LO4 |
Demonstrate advanced technical and interpersonal skills for developing an AI in Finance application |
| Dependencies |
Module Recommendations
This is prior learning (or a practical skill) that is required before enrolment on this module. While the prior learning is expressed as named NCI module(s) it also allows for learning (in another module or modules) which is equivalent to the learning specified in the named module(s).
|
| No recommendations listed |
Co-requisite Modules
|
| No Co-requisite modules listed |
| Entry requirements |
Programme entry requirements must be satisfied.
|
Module Content & Assessment
| Indicative Content |
|
Introduction to AI
Terminologies, Data, Process Flow, Opportunities, Challenges Overview, Regulatory Technology (RegTech)
|
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AI Technologies (1)
Machine Learning – Overview of ML types namely supervised, unsupervised, and reinforcement learning, ML Process flow, ML tools overview
|
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AI Technologies (2)
Deep Learning- Big Idea, Tools, Constraints, Applications, Opportunities, Challenges
|
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AI Technologies (3)
An overview, general applications, opportunities, and challenges related to Computer Vision, Natural Language Processing and Recommendation System
|
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Operationalizing AI
Understanding the infrastructure needs for deploying AI in Industry/ Real-world applications
|
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AI for Portfolio Management
Portfolio Management; Critically analyse AI models for Portfolio Management
|
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AI for Banking Fraud Detection
Understand Banking fraud and how AI models can be used for detecting fraud and develop compliance methods.
|
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AI for Improving Customer Services for Banking Needs
Understanding Customer Services and explore and analyse how AI tools such as NLP and recommendation systems be leveraged for improving customer services
|
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Applications of Robotic Process Automation to Finance
Robotic Process Automation
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Credit Scoring Using AI Models
Understanding Credit Scoring and developing and critically evaluating AI models for credit scoring
|
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AI Models for Insurance Pricing
Develop an understanding of Insurance policies; explore and analyse AI based insurance models
|
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Challenges for AI in Finance
Regulatory Implications, Ethics for using AI in Finance including Transparency and Bias
|
| Assessment Breakdown | % |
|---|
| Coursework | 100.00% |
AssessmentsFull Time
| Coursework |
| Assessment Type: |
Formative Assessment |
% of total: |
Non-Marked |
| Assessment Date: |
n/a |
Outcome addressed: |
1,2,3,4 |
| Non-Marked: |
Yes |
Assessment Description:
Formative assessment will be provided on the in-class individual or group activities. Feedback will be provided in written or oral format, or on-line through Moodle. In addition, in class discussions will be undertaken as part of the practical approach to learning. |
|
| Assessment Type: |
Continuous Assessment |
% of total: |
40 |
| Assessment Date: |
n/a |
Outcome addressed: |
1,2,3 |
| Non-Marked: |
No |
Assessment Description:
Critical review of a paper at the intersection of AI and FinTech |
|
| Assessment Type: |
Project |
% of total: |
60 |
| Assessment Date: |
n/a |
Outcome addressed: |
1,2,3,4 |
| Non-Marked: |
No |
Assessment Description:
Critical analyses of the requirements and the challenges of the application of an AI technology for a finance problem and the proposal of an end-to-end AI system design for this application. |
|
| No End of Module Assessment |
| Reassessment Requirement |
Coursework Only
This module is reassessed solely on the basis of re-submitted coursework. There is no repeat written examination.
|
Reassessment Description
The repeat strategy for this module is by repeat assessment/project that covers all learning outcomes.
|
NCIRL reserves the right to alter the nature and timings of assessment
Module Workload
| Module Target Workload Hours 0 Hours |
| Workload: Full Time |
| Workload Type |
Workload Description |
Hours |
Frequency |
Average Weekly Learner Workload |
| Lecture |
Classroom and demonstrations |
24 |
Per Semester |
2.00 |
| Tutorial |
Mentoring and small-group tutoring |
24 |
Per Semester |
2.00 |
| Independent Learning |
Independent learning |
202 |
Per Semester |
16.83 |
| Total Weekly Contact Hours |
4.00 |
| Workload: Blended |
| Workload Type |
Workload Description |
Hours |
Frequency |
Average Weekly Learner Workload |
| Lecture |
Classroom and demonstrations |
12 |
Per Semester |
1.00 |
| Tutorial |
Mentoring and small-group tutoring |
12 |
Per Semester |
1.00 |
| Directed Learning |
Directed e-learning |
24 |
Per Semester |
2.00 |
| Independent Learning |
Independent learning |
202 |
Per Semester |
16.83 |
| Total Weekly Contact Hours |
4.00 |
| Workload: Part Time |
| Workload Type |
Workload Description |
Hours |
Frequency |
Average Weekly Learner Workload |
| Lecture |
Classroom and demonstrations |
24 |
Per Semester |
2.00 |
| Independent Learning |
ndependent learning |
202 |
Per Semester |
16.83 |
| Tutorial |
Mentoring and small-group tutoring |
24 |
Per Semester |
2.00 |
| Total Weekly Contact Hours |
4.00 |
Module Resources
| Recommended Book Resources |
|---|
-
Arslanian, Henri, and Fabrice Fischer.. (2019), The future of finance: The impact of FinTech, AI, and crypto on financial services, Springer.
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Chishti, Susanne.. (2020), The AI Book: The Artificial Intelligence Handbook for Investors, Entrepreneurs and FinTech Visionaries., John Wiley & Sons.
| | Supplementary Book Resources |
|---|
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Alpaydin, Ethem.. (2016), Machine learning: the new AI., MIT press.
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John D. Kelleher, Brian Mac Namee, and Aoife D’Arcy,. (2015), Fundamentals of Machine Learning for BI and Consumer Relationship Data Analytics: Algorithms, Worked Examples, and Case Studies, The MIT Press.
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Koren, Y.. (2010), The Global Manufacturing Revolution:Product-Process-Business Integration and Reconfigurable Systems,, Wiley.
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Nightingale, D. J. and D. H. Rhodes. (2015), Architecting the Future Enterprise, MIT Press.
| | This module does not have any article/paper resources |
|---|
| Other Resources |
|---|
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