ApllicationofMachineLearninginBankingriskmanagement.docx

The application of machine learning in Banking risk management (building on the following keystone papers)

Keystone papers

Bracke, P., Datta, A., Jung, C., & Sen, S. (2019). Machine Learning Explainability in : An Application to Default Risk Analysis. Bank of England Working Paper. 816
Kou, G., Chao, X., Peng, Y., Alsaadi, F. E., & Herrera-Viedma, E. (2019). Machine learning methods for systemic risk analysis in financial sectors. Technological and Economic Development of Economy, 25(5), 716-742.
Leo, M., Sharma, S., & Maddulety, K. (2019). Machine Learning in Banking Risk Management: A Review. Risks, 7(1), 29. MDPI AG.

You should use the following guideline that is designed to give you a clear picture of what is expected from you. This is just a guideline.
1. Title page, Table of Content, Abstract (150 – 250 words), Keywords
2. Introduction (750 – 1000 words) 
3. Review/Bibliography (1,000 – 1,500 words) 
4. Method (2,500 – 3,500 words)
5. Findings/Results  (1,000 – 1,500 words)
6. Discussions (500 – 750 words) 
7. References (minimum of 50 references and must not be older than 2 years.
Appendices
30-35 pages (excluding title, abstract, table of content, appendix, and reference pages)

T

he application of machine learning in Banking risk

management

(

building on the

following

keystone papers)

Keystone papers

Bracke, P., Datta, A., Jung, C., & Sen, S. (2019). Machine Learning Explainability in :

An Application to Default Risk

Analysis. Bank of England Working Paper. 816

Kou, G., Chao, X., Peng, Y., Alsaadi, F. E., & Herrera

–

Viedma, E. (2019). Machine learning

methods for systemic risk analysis in financial sectors.

Technological and Economic

Development of Economy,

25(5), 716

–

7

42.

Leo, M., Sharma, S., & Maddulety, K. (2019). Machine Learning in Banking Risk Management:

A Review.

Risks,

7(1), 29. MDPI AG.

You should use the following guideline that is designed to give you a clear picture of what is

expected from you.

This is just a guideline.

1.

Title page, Table of Content, Abstract

(150

–

250 words),

Keywords

2.

Introduction

(750

–

1000 words)

3.

Review/Bibliography

(1,000

–

1,500 words)

4.

Method

(2,500

–

3,500 words)

5.

Findings/Results

(1,000

–

1,500 words)

6.

Discus

sions

(500

–

750 words)

7.

References

(minimum of 50 references and must not be older than 2 years.

Appendices

30

–

35 pages (excluding title, abstract, table of content,

appendix,

and reference pages)

The application of machine learning in Banking risk management (building on the following
keystone papers)
Keystone papers
Bracke, P., Datta, A., Jung, C., & Sen, S. (2019). Machine Learning Explainability in :
An Application to Default Risk Analysis. Bank of England Working Paper. 816
Kou, G., Chao, X., Peng, Y., Alsaadi, F. E., & Herrera-Viedma, E. (2019). Machine learning
methods for systemic risk analysis in financial sectors. Technological and Economic
Development of Economy, 25(5), 716-742.
Leo, M., Sharma, S., & Maddulety, K. (2019). Machine Learning in Banking Risk Management:
A Review. Risks, 7(1), 29. MDPI AG.

You should use the following guideline that is designed to give you a clear picture of what is
expected from you. This is just a guideline.
1. Title page, Table of Content, Abstract (150 – 250 words), Keywords
2. Introduction (750 – 1000 words)
3. Review/Bibliography (1,000 – 1,500 words)
4. Method (2,500 – 3,500 words)
5. Findings/Results (1,000 – 1,500 words)
6. Discussions (500 – 750 words)
7. References (minimum of 50 references and must not be older than 2 years.
Appendices
30-35 pages (excluding title, abstract, table of content, appendix, and reference pages)