Analytics closely resembles statistical analysis and data mining, but tends to be based on physics modeling with extensive computation.

Banking example

A common application is portfolio analysis. In this, a bank or lending agency has a collection of accounts, some from wealthy people, some from middle class people, and some from poor people. The question is how to evaluate the whole portfolio.

The bank can make money by lending to wealthy people, but there are only so many wealthy people. The bank can make more money by also lending to middle class people. The bank can make even more money by lending to poor people.

Note that poorer people are usually at greater risk of default. Note too, that some poor people are excellent borrowers. Note too, that some poor people will eventually become rich people, and will reward the bank for loyalty.

The bank wants to maximize its income, while minimizing its risk, which makes the portfolio hard to understand.

The analytics solution may combine time series analysis, with many other issues.

See also