Human-in-the-loop and Interest cover: Difference between pages

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''Information technology - software - machine learning - natural language processing.''
''Financial ratio analysis - long-term solvency ratios.''


(HITL).
'''1.'''


In the context of machine learning, human-in-the-loop refers to one or both of the following roles undertaken by a human:
From a whole-firm perspective, interest cover is the ratio of Profit before interest and tax ÷ Interest payable.


*Manual labelling of training data.
 
*Validating or feeding back to the model on the quality of its outputs.
Interest cover measures the safety or sustainability of the future debt servicing flows, from the perspective of the lenders. 
 
The greater the interest cover ratio, the greater the likelihood that the firm paying the debt interest (and other debt servicing costs) will continue to be able to service the debt in the future. 
 
So a higher cover ratio is associated with lower risk for the debt investors.
 
 
In the theoretical situation where the cover ratio fell below 1.0, the interest would be said to be ''uncovered'' and the debt would not be sustainable at its previous level unless there was a recovery in the firm's operating profitability.
 
In practice lenders want much higher minimum interest cover ratios than 1.0, such higher minimum usually stipulated in the related loan documentation. 
 
So the borrower in this situation would be likely to be already in breach of a related borrowings covenant.
 
 
Also known as the Interest cover ratio or TIE (times interest earned).
 
 
'''2.'''
 
An analogous measure, in relation to an individual tranche or class of debt (rather than to the whole firm).




== See also ==
== See also ==
* [[Artificial intelligence]] (AI)
* [[Cost of financial distress]]
* [[Artificial super-intelligence]]  (ASI)
* [[Covenant]]
* [[Bot]]
* [[Cover ratio]]
* [[Chatbot]]
* [[Cross acceleration]]
* [[ChatGPT]]
* [[Gearing]]
* [[Data]]
* [[Interest rate risk]]
* [[Data labelling]]
* [[Long-term solvency ratio]]
* [[Deep learning]]
* [[Profit before interest and tax]]
* [[Generative pre-trained transformer]]  (GPT)
* [[Uncovered]]
* [[GPT-4]]
* [[Information technology]]
* [[Language model]]
* [[Large language model]]  (LLM)
* [[Machine learning]]
* [[Natural language]]
* [[Natural language processing]]
* [[Neural network]]
* [[Reinforcement Learning from Human Feedback]]  (RLHF)
* [[Robotics]]
*[[Software]]
* [[Software robot]]
*[[Supervised learning]]
*[[Unsupervised learning]]


[[Category:Accounting,_tax_and_regulation]]
[[Category:The_business_context]]
[[Category:The_business_context]]
[[Category:Identify_and_assess_risks]]
[[Category:Corporate_finance]]
[[Category:Manage_risks]]
[[Category:Long_term_funding]]
[[Category:Risk_frameworks]]
[[Category:Treasury_operations_infrastructure]]
[[Category:Risk_reporting]]
[[Category:Technology]]

Revision as of 13:55, 29 April 2020

Financial ratio analysis - long-term solvency ratios.

1.

From a whole-firm perspective, interest cover is the ratio of Profit before interest and tax ÷ Interest payable.


Interest cover measures the safety or sustainability of the future debt servicing flows, from the perspective of the lenders.

The greater the interest cover ratio, the greater the likelihood that the firm paying the debt interest (and other debt servicing costs) will continue to be able to service the debt in the future.

So a higher cover ratio is associated with lower risk for the debt investors.


In the theoretical situation where the cover ratio fell below 1.0, the interest would be said to be uncovered and the debt would not be sustainable at its previous level unless there was a recovery in the firm's operating profitability.

In practice lenders want much higher minimum interest cover ratios than 1.0, such higher minimum usually stipulated in the related loan documentation.

So the borrower in this situation would be likely to be already in breach of a related borrowings covenant.


Also known as the Interest cover ratio or TIE (times interest earned).


2.

An analogous measure, in relation to an individual tranche or class of debt (rather than to the whole firm).


See also