Applications of Machine Learning to the Finance Industry
Background: Being highly data-driven, finance is one of the initial industries that began applying data science methods to optimize functioning. Some of these applications include, optimizing the interest rate gap between the interest charged for loans, and the interest paid for deposits to maximize long-term profitability; or using machine learning to target the marketing of new financial products its customer base based on various customer attributes such as age, gender, wealth, credit health, etc; or using various analytics method to verify the identity of a customer for the purpose of ascertaining permissions and granting access to services; and finally to aid in immediate and accurate credit card and online transaction fraud detection using sophisticated, real-time machine learning systems.
In the past few years credit card fraud and identity fraud have been rising rampantly and have cost tens of billions of dollars. Banks are starting to use very sophisticated methods to deal with such crises; they often work with companies such as ID Analytics, a leading fraud analytics solution provider based in San Diego. The methods used for credit card fraud detection have evolved significantly over the past few years. Some years ago banks would employ expert rule systems, that would flag and potentially block transactions that were likely to be fraudulent. These systems were rules based on experiences of credit card fraud detection officers, for e.g. if the transaction size is X times bigger than the average transaction it is likely fraudulent. While such approaches did catch some fraud, it was not utilizing big data’s true potential. Also, such methods were based on anecdotal and isolated evidence of officers and may not be generalizable to the entire population. Hence, companies such as ID Analytics came up with systematic, and sophisticated methods that would analyze the data thoroughly, and build relevant variables that they feed into sophisticated non-linear machine learning models such as Boosted Trees, Support Vector Machines, and more recently even Neural Networks to help classify transaction as fraud or not fraud. Such methods are much more reliable and have lower false positive rates.
Another exciting application of data science methods to the finance world, is in the form of algorithmic and financial trading. There has been a surge in the number of hedge funds that employ sophisticated machine learning models to determine their trading strategies and a lot of them have been very profitable in doing so. One such example is Cerebellum Capital that is a machine-learning investment management firm based in San Francisco. The firm’s platform automates much of a data scientist and financial traders’ role.
Hence, we can see that data science has impacted the financial world significantly and continues to do so every day. It appears that some of the new and upcoming innovations of machine learning and artificial intelligence are to the venture capital world where venture capital firms are using text analytics, sentiment analysis, etc. to determine which companies they should invest their capital into. What’s next remains to be seen, however, the impact of advanced analytics methods to the finance industry is only going to grow.