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.


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4 comments on “Applications of Machine Learning to the Finance Industry”

  1. One of the most fascinating and simultaneously terrifying developments due to the growth of AI is the higher risk of a country’s labor force getting replaced by machines. Artificial intelligence may bring about a “gold rush”; however, one must also acknowledge the potential for increased unemployment within a country due to labor replacement. Will the sudden increase in AI yield a net benefit (due to increased corporate efficiency)? Will it be detrimental (due to increased unemployment, and a potential for increased crime)?

    1. I dont see it that critical. When the machine swere inventend, everbody thought there wont be any work for the people, but just their work has changed.
      When the computers started, everybody thought they will losse their jobs and that did not happen, just the qualifications changed.
      And it will be the same, jobs start vanishing and new jobs come, it was in the past like that and will be in the future like that;-)

  2. Thank you very much for the fascinating article. And you are right that mainly Finance one of the industries going quickly into Information technology in general. They have been the first one on digitalizing their assets from the 70s
    I doubt that the investment decisions in the VC-parts may be more difficult with the text analysis and hardly solvable, it is just too abstract.
    But for already established companies with revenue, profit and robust numbers over the last few years can be a lot easier analyzed and there their value and expected growth to be calculated more automatically.
    And one a lot more interesting use-Case where AI may disrupt the business is on the Wall-Street and the prediction of stock-values. People work on that for years already and have not yet been successful.


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