Both data science and financial technologies are incredibly complex practices, but it is not always obvious how the two are linked. In fact, the fintech industry would not be nearly as complex and as personalized as it is today without data science and the practice of machine learning, AI, and the development of algorithms.
A resource like Cane Bay Partners can be invaluable when trying to grasp concepts like machine learning, data science and the history of fintech. That said, here is a brief overview of several ways in which fintech is enriched and impacted by data science.
1. Aids In the Development of Personalized Products
One of the reasons people in their 20’s and 30’s are so drawn to fintech is the ability for their chosen products to be personalized. For instance, millennials and their younger counterparts are moving away from using traditional banking services in favor of mobile banking apps like Chime or Dave. Those apps,
though not full-fledged banking institutions themselves, offer more flexibility with regards to day to day financial planning as well as personalized options for each customer.
Chime offers to cover overdrawn accounts up to a certain amount of money depending on how much cash a customer has in their spending accounts, for example. If a customer only has a few hundred dollars in the account, the algorithm offers them only about 20 to 30 dollars of backup on overdrafts. If they regularly deposit thousands of dollars into their spending accounts, however, the overdraft backup might increase to well over a hundred dollars. All of this is controlled by artificial intelligence with very little human oversight.
2. Aids In Market Research
AI and machine learning help fintech companies understand exactly what their customers want. In addition to building apps that cater to multiple customer subsets at once, these companies develop algorithms and automated data collection in order to learn exactly what each kind of customer wants.
These data collection patterns are built into the apps. They collect data via automated surveys and other customer experience feedback. The apps also have built-in monitors that relay information about which features of the app are being used the most, as well as the demographic of each customer. The demographics and other customer data are then linked to their actions through automated machine learning, and they are presented to their developers in datasheets and reports that are easy to track and use for future app development.
3. Aids In Fraud Detection
Perhaps one of the most important ways in which data science plays a role in fintech is in terms of fraud detection. Credit score tracking services as well as cash flow apps all rely on automated fraud detection services. These fraud monitoring systems are easily built into their respective apps, offering useful data to customers and service providers alike. They help keep the customers safe while teaching fintech service providers what types of security they need to implement into their apps in order to provide maximum safety.
Simply put, the financial technology sector would not be as useful or as complex as it is today if it weren’t for data science. Automated systems collect useful data, implement customer satisfaction, and alert users and developers to security breaches almost as soon as they happen. Without data science, fintech would be unusable and unattractive to the throngs of customers that use its various iterations today.
