The promise of ML¶

Machine learning can generate solutions to problems at scales that are cost-prohibitive otherwise. For example, the earliest (and ongoing) waves of ML in the finance space includes

1. Robo-advising (Acorns, Betterment, Wealthfront, etc.)
2. Manage risk (loans and insurance) to reduce write-offs and lower costs for consumers
3. Prevent and detect fraud (external and internal)
4. Investment choices - stocks, real estate (where to put factories, banks, etc)
5. Improve advertising offers to credit customers

Accenture thinks AI will add $140B of value to financial service firms alone via cost and productivity savings by 2025.

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Don't you want to capture a little of that?

This subsection is about setting the stage so we can start to apply our skills to the types of problems whose solutions will make big impacts.

{seealso}
1. [Principles of good data analysis, by Greg Reda](http://www.gregreda.com/2014/03/23/principles-of-good-data-analysis/)
2. [Chapter 3 of Data 100](https://www.textbook.ds100.org/ch/04/modeling_intro.html)
3. [How Big Investors Cash in on Alternative Data](https://www.bloomberg.com/news/articles/2019-11-09/how-big-investors-cash-in-on-alternative-data-quicktake)