Risk Modelling with
modelling is another major use case that’s energized by Hadoop. We think we
will find that it closely resembles the fraud detection model use case in which
it acts like a model-based discipline. The greater the amount of data volume we
have, more we can “connect the dots,” and the more often our output will yield
better risk-detection models.
“risk” can be used in various contexts and lot of definitions can be drawn from
it. For instance, client churn prediction is the risk of a user moving to a
competitor; the risk of a loan book impact to the risk of default; risk in health
care spans the gamut from outbreak containment to food safety to the
possibility of reinfection and much more.
financial services sector (FSS) is now investing heavily in Hadoop-based risk modelling.
This sector seeks to increase the automation and accuracy of its risk
assessment and exposure modelling. Hadoop provides the users the opportunity to
expand the data volume sets that are utilized in their risk models to include
under-utilized sources (or sources that are never utilized), such as electronic-mail,
instant messaging, social networking, and interactions with customer service representative
and various other data sources. Risk predictive modelling in FSS pop up almost everywhere.
They’re used for customer churn prevention, trade manipulation modelling,
corporate risk and exposure analytics, and more.
an organization issues an insurance policy for natural disasters at home; one
problem is clearly identifying how much money is potentially at risk. If the insurer
fails to reserve money for the pay-outs, then the regulators will intervene (which
the insurer doesn’t wanted ); if the insurer puts more money into its reserves
for paying out future policy claims, then they are not able to invest your
premium money to make a profit (which insurer doesn’t want too ). We know many
organizations that play “blind” to the risk they face since they are not able
to run an adequate amount of catastrophic simulations pertaining to variance in
wind speed and precipitation rates (among other variables) as these are related
to their exposure. Quite easily, these organizations face challenges in stress-testing
their risk-predictive models. This ability to manage in more data volumes — for
example, weather patterns or the ever-changing socio-economic distribution of
their client base — gives them a lot more flexibility and business insight when
it comes to create better risk models.
and stress-testing risk predictive models is an ideal task for Hadoop. These
operations are sometimes computationally expensive and, when we are building a
risk model, it is likely impractical to run against a data warehouse