Financial technology, also known as fintech, was initially trendy among venture capitalists and has since grown in popularity. It has become the new norm for banking and other online financial services. Whenever you use a credit card or PayPal to make an online purchase, you, the shop, and your financial provider are all using fintech to complete that transaction possible.
While it started simpler, it has evolved to become involved with almost all aspects of finances. The technologies that have allowed this development include machine learning, predictive analytics, artificial intelligence, and data science. Here is more about how data science is used in several fintech applications.
Robo-Advisors
Robot advisors advise clients on investing and financial planning decisions based on detailed algorithms. Data science comes into play because the entire process is automated, and the algorithms produce the final investment decisions. Therefore, no humans intervene and bring bias into the decision.
Instead, the robot begins the process by creating a client profile based on their financial status, goals, and potential risks. Then it processes the data to provide advice or automatically invest their funds into the best option. That eliminates any bias that could have otherwise affected the investment decision.
Risk Analysis
Since Cane Bay cares about its customers, it uses data science and machine learning to gather information on clients and their credit history when making financial decisions. Likewise, many companies use fintech when making decisions about their clients because it allows them to identify who can bring a profit and who is a potential risk without bias.
Fraud Detection
Many companies, such as Cane Bay Partners, have also begun to use data science to identify fraudulent transactions. Previously, fraudulent transactions were identified manually following a set of rules. However, data science is now being used to determine behavior patterns in a way that allows fraudulent transactions to be predicted and identified easier. In addition, machine learning may also be brought into fraud detection to help automate the process.
Customer Acquisition
Banks and other financial institutions can use data science to determine customers’ preferences and provide them with products, programs, and events that will catch their interest, giving them a highly personalized experience. For instance, a bank might offer customers certain products based on past purchases. Similarly, these institutions can tailor their advertisements to market programs to specific age groups or other demographic groups to acquire more customers.
Insurance Products
The insurance industry has also embraced data science as part of fintech. Many insurers use data science to analyze potential risks and ensure their company stays profitable. They will also use them to ensure no fraudulent claims are made on anyone’s insurance policy. Insurance companies might also use fintech for credit scoring, marketing, customer acquisition, new product offerings, and customer retention.
There are plenty of examples of data science being used in fintech. Thankfully, this has made the virtual financial world safer for customers and allows for more non-biased decisions. That will enable businesses to remain profitable and for consumers to shop online with peace of mind.
