Thursday, December 29, 2016

Machine Learning and Risk Modeling

Machine Learning is offering an interesting possibility to redefine the way current risk modeling occurs in large financial institutions ( banks, insurance companies ). Regulatory and product oriented risk calculations are the crucial part of financial organizations activities ( probability of default for customers, organizations, countries, bank capitalization requirements, credit scores etc.  ). Current practices are a mix of alchemy, crossing your fingers and wishing for the best, some math and the lack of backtesting to check how models are actually performing. Models take long time to develop and execute, with results everybody pretends they believe in.

Machine Learning has a potential to completely redefine ( for the better ) the way risk modeling is done.


Existing resource and time consuming  processes could be replaced with a new Machine Learning paradigm where programs/models change far less frequently. Each new model development iteration doesn't necessarily need to imply a brand new model development ( or model tweaking ) and deployment.  Getting a new, updated model would just require running the existing neural network model with the newly available data. Classic model development lifecycle is replaced with  new techniques that must be learned ( developing, testing neural network models, determining hyperparameters, dealing with bias and variance i.e. preventing under/over fit etc. )
Regulatory aspect also needs to be taken care of, as regulators need to be on board with the proposed model changes ( for capitalization related risk calculations ).

While new ( supervised ) Machine Learning based paradigm will not save us from outliers ( Black Swan events ), it will definitely be more accurate, easier to deploy and maintain than the existing one. Thus we think it is high time even for mainstream financial organizations to start establishing foothold in self-programming world.       

Thursday, December 22, 2016

Forget About AI - Machine Learning Is What Matters

IT, like any other mature industry,  shows less and less capacity for the true and radical innovation. This is proven yet again with the latest waves of AI noise ( skyrocketing NIPS attendance, mediocre Salesforce Einstein software, crazy prices for companies repackaging old Machine Learning concepts).

What is a typical (or major ) financial institution to make and do about the latest craze ?

AI is a wide discipline with many, typically siloed i.e. disparate problem areas - it offers domain specific solutions to diverse, often unrelated sets of problems ( self-driving cars; recommendation systems; speech recognition ). It is also completely empirical ( result driven ), with no theoretical foundations or explanations why artificial neural network work the way they do, for example. (" But it works " - G. Hinton  51:00).

We remember quite well noises, notions and semi-flops of the past ( CASE, OODBMS, Y2K, Hadoop ). Even the Cloud made limited direct inroads to the standard enterprise landscape.  Cloud didn't became mainstream replacement for on premise hardware and software, as majority of mission critical corporate systems still keep data and run in house.

Oracle CEO Larry Ellison Bashes 'Cloud Computing' Hype ( please note Ellison repented since this 2009 cloud bash episode ).


Consequently we think that, once AI smoke clears and mirrors are gone, all that will be left will be good old Machine Learning ( lucratively renamed Deep Learning ), which will hopefully gain some foothold in more forward thinking ( or more adventurous ) financial establishments.