Thread by @SAH16928046

I am into risk management.
Most of the risk managers are now required to have an advance background in operating technological applications such online trading and price data terminals (Bloomberg/Reuters, etc),

FINTECH, Crypto Assets, Digital Marketing based vendor systems, DLT(Distributional Ledger Technologies) - Blockchain, AI / ALGO based trading in financial markets, Derivatives and Risk Pricing Engines and other Software Computational Programs,

Risk MIS/ERP Project Management Tasks, 4GL Fourth Generation Languages, Data Warehousing, BI, MI, SQL, NoSQL, and so on etc.

The #GFC Global Financial Crisis happened because risk managers, quants and other trading bigwigs had limited to no understanding of DS, ML, AI, and Deep Learning Models back in 2007.

Heavy reliance on blindly using Black Box Models drove Analytics, drawn from a wide variety of academic disciplines such as the Statistical Learning Model Theory, Entropy, Informatics, Mathematical Physics, QFRM, and Stochastic Processes as done in Actuarial Sciences, and so on.

We must learn to distinguish between DS/ML and Basic Statistics and #Probability Models.
#Statistical #Learning and #Machine #Learning Model Theories are not the same!
The computational modelling and data preparation assumptions are entirely different.