Jyoti Nigania

Hi,i am writing blogs for our platform House of Bots on Artificial Intelligence, Machine Learning, Chatbots, Automation etc after completing my MBA degree. ...

Hi,i am writing blogs for our platform House of Bots on Artificial Intelligence, Machine Learning, Chatbots, Automation etc after completing my MBA degree.

RPA is Meant to Automate the Routine Task
yesterday

How AI is going to change the lives of Visually Challenged People?
yesterday

How Organizations can get Best Out of Data Scientists?
yesterday

Understanding the Present Scenario and Future Outlook of Artificial Intelligence
2 days ago

Understanding the Past or History of AI
4 days ago

Scope of AI and Machine learning in India
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Skills Required To Become Data Scientists
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How to Learn Mathematics for Machine Learning?
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Difference between Artificial Intelligence, Machine Learning and Deep Learning
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Differentiating between Data Science, Big Data and Data Analytics
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How Machine Learning in Finance is different from other fields?

Aug 8, 2018 | 252 Views

How Machine Learning in finance is different from other fields?

Answered by Igor Halperin on Quora:
Machine Learning plays an integral role in many areas of financial services like from approving the loans, managing assests, minimizing the risk and many more. Machine Learning plays a vital role in fraud detection and protects and thus protect the consumer from the fraudlent activities.

The main differences stem from differences in data. In finance, data are very noisy, and often non-stationary. "Signals" cannot be split from "noise" in any unique way, as a matter of principle. This is very different from, say, image processing, where the level of noise can be controlled, at least in principle. Also, the notion of non-stationary data is non-existent for image processing. Because of a pronounced role of noise, some ML models, for example non-probabilistic models, are not very useful in finance.

The other difference is the amount of data. Many interesting problems of finance are problems with small-to-medium datasets, which makes applications of data-hungry methods such as deep learning problematic. Therefore, in finance enforcing some prior knowledge is often necessary, via depending on a method used choices of regularization, Bayesian priors, or other general principles such as analysis of symmetries.

One more important difference is that the "true" state space in finance is not well defined. There are so it is called black swan events-things that are outside of financial models, for example political risk, that nevertheless might have severe impact on security prices. There is a difference between uncertainty and probability risk. Most ML models as well as most of classical financial models deal with probabilistic systems with a well defined state space they do not admit black swans. They are models of risk but not models of uncertainty.
  • Low signal: noise ratio 
  • Nonstationarity
  • Limited data
Consider that any one of these creates challenges for standard ML practice, and each compounds the problems the others create or the overfitting, sample bias, data snooping.
There are other issues depending on the application. Hence machine learning is different in finance from the machine learning in other fields. Using machine learning, systems can detect unique activities or behaviors and flag them for security teams. The challenge for these systems is to avoid false-positives situations where "risks" are flagged that were never risks in the first place. 

Source: HOB
Jyoti Nigania

Hi,i am writing blogs for our platform House of Bots on Artificial Intelligence, Machine Learning, Chatbots, Automation etc after completing my MBA degree. ...

Full Bio 

Hi,i am writing blogs for our platform House of Bots on Artificial Intelligence, Machine Learning, Chatbots, Automation etc after completing my MBA degree.

RPA is Meant to Automate the Routine Task
yesterday

How AI is going to change the lives of Visually Challenged People?
yesterday

How Organizations can get Best Out of Data Scientists?
yesterday

Understanding the Present Scenario and Future Outlook of Artificial Intelligence
2 days ago

Understanding the Past or History of AI
4 days ago

Scope of AI and Machine learning in India
26679 views

Skills Required To Become Data Scientists
14028 views

How to Learn Mathematics for Machine Learning?
12096 views

Difference between Artificial Intelligence, Machine Learning and Deep Learning
10140 views

Differentiating between Data Science, Big Data and Data Analytics
8769 views