Financial Data Science Applications

Financial Data Science Applications

Finance was one of the first industries to develop and apply data science methods. What is financial data science? Kaplan Schweser defines financial data science as combining “the traditions of econometrics with the technological components of data science. Financial data science uses machine learning, predictive and prescriptive analytics to provide robust possibilities for understanding financial data and solving related problems.”

Finance Applications

Data science has been applied to many areas of the finance industry. One that people interact with every day (but may not realize it) is financial fraud prevention. If your credit card information is stolen, the company will flag the charge and alert you about a possible fraudulent purchase. But how does the company know whether the credit card purchase is one being completed by the actual owner of the credit card or if it is fraud? Machine learning algorithms have become an integral part of this area of finance because they can quickly handle variety of large quantities of data and effectively predict credit card fraud.

When many people hear “financial data science,” the first thought that comes to mind is algorithmic trading. Algorithmic trading utilizes the power of data science algorithms to identify profitable trading strategies. Algorithmic trading has been applied to many financial institutions including hedge funds, pension funds, and even banks. According to, “more than 60% of the top-performing hedge fund mangers made use of algorithmic trading.”

While many applications of financial data science exist, one final example is related to loan or credit card approval. This application is often used as an easy introductory model in financial data science courses and slightly more complicated models are applied by financial corporations. The goal of these models is to predict whether a loan or credit card should be approved when an individual applies for the loan. Financial institutions make money from the interest earned on the loan while the primary risk for the company is that the individual will not pay back the money that they are lent. Various datasets and data science models such as logistic regressions, decision trees, and random forests can be used to help the company decide whether the loan should be approved.

Financial Data Professional® (FDP) Charter

With the continued growth in financial data science, standardized professional exams have been created to provide individuals with the applicable skills and knowledge a way to designate themselves as masters of the content. One such designation is the FDP Charter. The FDP institute, FDP exam, and FDP charter are run by Chartered Alternative Investment Analyst Association. For more information about the FDP institute and the exam, check out their website at: