Open Access Open Access  Restricted Access Subscription Access

Calculation of Risk Weighted Assets (RWA) via Machine Learning Technique


Affiliations
1 Senior Business Analyst, Foray Software Private Limited, Hyderabad, Telanagana-500081, India

   Subscribe/Renew Journal


RWAs have limited literature and confidence in reported RWAs is ebbing. Market participants question the reliability and comparability of capital ratios, and contend that banks may not be as strong as they are portrayed by risk-based capital ratios. With this paper, an altogether new idea was proposed to calculate RWA across banking industries. Machine learning approach will strengthen its calculation engine and make it more robust over time. It will predict the required RWAs on the basis of historical data and expectation feeds. As we know, machine learning is the vital concept of 21st century and can be harnessed in finance domain for evaluation of various risk attributes dynamically and can be tested in normal as well as stressed conditions.

Keywords

A-IRB, Deep Learning, F-IRB (IRB – Internal Rating Based Methodology), ML (Machine Learning), Risk Weighted Assets (RWA)

No Classification

Manuscript received October 5, 2017; revised October 14, 2017; accepted October 15, 2017. Date of publication November 6, 2017.

User
Subscription Login to verify subscription
Notifications
Font Size

Abstract Views: 233

PDF Views: 0




  • Calculation of Risk Weighted Assets (RWA) via Machine Learning Technique

Abstract Views: 233  |  PDF Views: 0

Authors

Prabhat Kumar
Senior Business Analyst, Foray Software Private Limited, Hyderabad, Telanagana-500081, India

Abstract


RWAs have limited literature and confidence in reported RWAs is ebbing. Market participants question the reliability and comparability of capital ratios, and contend that banks may not be as strong as they are portrayed by risk-based capital ratios. With this paper, an altogether new idea was proposed to calculate RWA across banking industries. Machine learning approach will strengthen its calculation engine and make it more robust over time. It will predict the required RWAs on the basis of historical data and expectation feeds. As we know, machine learning is the vital concept of 21st century and can be harnessed in finance domain for evaluation of various risk attributes dynamically and can be tested in normal as well as stressed conditions.

Keywords


A-IRB, Deep Learning, F-IRB (IRB – Internal Rating Based Methodology), ML (Machine Learning), Risk Weighted Assets (RWA)

No Classification

Manuscript received October 5, 2017; revised October 14, 2017; accepted October 15, 2017. Date of publication November 6, 2017.




DOI: https://doi.org/10.17010/ijcs%2F2017%2Fv2%2Fi6%2F120439