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.       

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