25752 Bank Lending and Analytics
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Subject handbook information prior to 2025 is available in the Archives.
Credit points: 6 cp
Subject level:
Postgraduate
Result type: Grade and marksRequisite(s): ( 25741 Capital Markets OR ( 48 credit points of completed study in spk(s): C04379 Master of Business Analytics (Extension) ))
These requisites may not apply to students in certain courses.
There are course requisites for this subject. See access conditions.
Description
In this subject, students develop the necessary analytics skills to meet the responsible lending standards of banks. Students develop a deep understanding of the bank lending cycle, including key features of consumer and corporate credit, loan structuring and underwriting, loan and loan portfolio risk management, IFRS 9 loan loss provisioning and Basel III bank capital allocation for single loans, loan portfolios and capital market transactions. Students learn how to make real-world credit approval decisions. Analysing loan-level performance data with data science software, students build predictive default and loss rate models. In addition, machine learning, model validation, visualisation and communication techniques are developed.
Subject learning objectives (SLOs)
| 1. | explain the processes of lending to individual and corporate customers and the need to have robust credit assessment and monitoring structures in place. |
|---|---|
| 2. | structure the terms for a loan facility to corporate borrower, including the types of security the lender may require. |
| 3. | describe the capital requirements on credit risk under the Basel framework and the various ways capital adequacy is measured by the different ‘approaches’ approved under the Basel framework. |
| 4. | demonstrate an understanding of credit data analytics and probabilities of default models. |
| 5. | analyse meta data from real mortgage portfolios, build a risk management model. |
| 6. | analyse loan performance data for Basel III and IFRS 9 compliance. |
Contribution to the development of graduate attributes
This subject provides students with knowledge of the core principles and concepts of loan management. Students are introduced to a range of loan products, loan structuring and specialized loan markets. The key focus of the course is the impact of lending and borrowing on the real economy and the effect of the risk reward trade-off in a lending context. Students are trained to critically and creatively analyse loan data, build forward looking lending models and communicate these effectively with stakeholders. Students will discuss ethical and sustainable principles underpinning lending decisions in line with the professional standards of the banking industry. The subject is designed to align with the following UTS Business School graduate attributes:
- Intellectual rigour and innovative problem solving
- Communication and collaboration
- Social responsibility and cultural awareness
- Professional and technical competence
Teaching and learning strategies
This subject uses a broad range of teaching and learning strategies (including active strategies), such as lectures, guest speakers, case studies, and student discussions. Weekly activities include (1) in-class discussions, (2) lectures, and (3) tutorials. In order to get the most of the subject, you should engage in all four activities every week. A detailed schedule of weekly activities, as well as all subject material, is available via the learning management system. Communication and feedback are also encouraged via the learning management system.
Pre-class activity
Students are asked to watch a video available via the learning management system to prepare themselves for classes. Furthermore, students are expected to prepare for the tutorials (see below) by completing the problems independently at home applying the skills and knowledge obtained in the lecture of the previous week.
In-class discussions
Current banking news will be made available to students and will form the basis of discussions about current bank operations and prudential regulation. Case studies will also be provided for discussion and student will receive immediate feedback on the quality of the discussions immediately in class.
Lectures
Students are expected to attend all lectures. Lectures introduce and describe the key concepts through a range of interactive and engaging learning experiences. There will be opportunities for collaborative student discussions in which students can share their insights. Occasionally, lectures will be complemented by guest lectures from industry practitioners. Prior to attending lectures, students are asked to watch a summary video available via the learning management system.
Tutorials
Tutorials provide an interactive opportunity by group-based discussions and problem solving to extend and apply the material taught in lectures. The tutorial for a given week is based on the lecture material from the previous week. Tutorials commence in Week 2, and students are expected to prepare for them by completing the problems independently at home applying the skills and knowledge obtained in the prior lecture. The students then receive feedback in the tutorials on the quality of the solutions as well as strategies on how to improve their skills.
Market relevant projects
Students will participate in two in-class projects. In the first project, students will analyse metadata from real mortgage portfolios and make bank lending decisions. Furthermore, students will discuss the application of their model in the context of mortgage underwriting of commercial banks and discuss professional standards mandated by prudential regulators. Students will write a report on their findings and receive feedback during the writing phase and as an outcome of the assessment. In the second project, students will analyse loan-level performance data, build probability of default models for Basel III and IFRS 9 compliance. Students will validate and critically assess the robustness and interpretation of model outputs. Students will write a report on their findings and receive feedback during the writing phase and as an outcome of the assessment.
The learning management system
The learning management system is used to disseminate learning resources, including the subject outline, lecture slides, tutorial exercises, assignment briefs, announcements, and any supplementary information. Students are responsible for checking the system regularly. All announcements posted will also be emailed to students, to ensure that they stay informed.
Content (topics)
- Loan contracting with between lenders and borrowers
- Credit assessment
- Bank capital requirement and loan pricing
- Credit risk management
Assessment
Assessment task 1: Assignment (Group)
| Objective(s): | This addresses subject learning objective(s): 1, 2 and 3 |
|---|---|
| Weight: | 30% |
| Length: | Maximum 5 pages |
| Criteria: | Students will write a report on their findings, which will be graded on the following criteria:
|
Assessment task 2: Assignment (Individual)
| Objective(s): | This addresses subject learning objective(s): 4, 5 and 6 |
|---|---|
| Weight: | 60% |
| Length: | Maximum 5 pages. |
| Criteria: | Students will write a report on their findings, which will be graded on the following criteria:
|
Assessment task 3: In-class quizzes (Individual)*
| Objective(s): | This addresses subject learning objective(s): 1, 2, 3, 4, 5 and 6 |
|---|---|
| Weight: | 10% |
| Criteria: |
*Note: Late submission of the assessment task will not be marked and awarded a mark of zero. |
Minimum requirements
Students must achieve at least 50% of the subject’s total marks.
Required texts
Roesch, D. and Scheule, H., 2020. Deep Credit Risk: Machine Learning with Python. Kindle Direct Publishing,
ISBN-13: 979-8617590199
References
Davis, K, Bank and Financial Institution Management in Australia, www.kevindavis.com.au/BankingBook/Chapter%20List.htm
Westpac, Annual Report, 2022, http://www.westpac.com.au
Basel Committee on Bank Supervision: www.bis.org
Australian Prudential Regulation Authority: www.apra.gov.au
Additional handouts will be provided on Canvas