7 Ways How Data Science Fuels The FinTech Revolution

7 Ways How Data Science Fuels The FinTech Revolution

7 Ways How Data Science Fuels The FinTech Revolution
Here are 7 ways how data science is at the core of the current transformation of the financial sector.

Michael Lewis’ bestseller “Flash Boys” has brought the issues of high frequency trading into the realm of public discussion. The book illustrates in a thrilling manner how entire investment banks are ruled by the power of algorithms and data science, spotting trading patterns and windows of opportunity in real-time fashion. However, in a fiercely competitive environment like trading, data science approaches have already matured. Today, even physical details such as the location of servers in relation to the geographic location of the stock market could have an impact on the success of specific trades. In other areas of the financial sector, however, data science still empowers a revolution. Here are 7 ways how data science is at the core of the current transformation of the financial sector.

  Analysis and prediction of transaction volumes is key to enhance product value for customers. Data science enables better classification of payment records and thus allows banks to tailor additional services to their client’s needs. This may vary from simple analytical features (How much did you spend on groceries last month?) to more advanced features such as the integration of payment records and personal data to allow for recommendation, loyalty rewards and other forms of proactive engagement. Generally, data science facilitates the holistic analysis of customer behaviour across all channels of engagement.

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  With the vision to make “credit accessible for more people”, various FinTech startups are on the rush for clients and VC money. Their value proposition boils down to a “faster and more accurate credit risk evaluation” process than at traditional banks, which enables them to reach a broader client base and minimize credit default rates. Reliably assessing the creditworthiness of an individual not only requires the consideration of various data sources (some start-ups claim to include more than 15.000 data points, as obscure as “how fast does one type when filling out the credit application online”) in a robust model, but also the calibration against training data (historical credit data etc.). The more predictive power one can accumulate, the better the business case.

  Data science enables the utilization of powerful predictive models in order to optimize revenue and debt collection. Already at the moment of purchase it is possible to predict a probability of timely payment, thus making revenue collection more transparent.

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