Fintech, or “financial technology” is a term used to describe new technology that can help better the financial components of companies, business owners, and consumers with certain algorithms and software. Data science and fintech go hand in hand. In other words, there are several ways in which fintech can utilize data science. Here are some of the key roles that data science plays in fintech.
Ways How Data Science Fuels The FinTech Revolution
Fraud detection is one of the key components of fintech that financial consulting companies such as Cane Bay Partners provide. It is an important cornerstone of any good business model, used to be done manually before fintech came about. Now, there are ways to utilize data analytics from past fraudulent transactions in order to predict future instances of fraud. Data science and machine learning are among some of the ways to pool this data for use in the future.
Robo-advising is an algorithm-driven platform of using online surveys to compile information on a client’s demographics. Information such as a client’s financial goals, status, and risk capacity is collected and processed so that their financial consulting advice will be relevant and useful to the client.
Creating Insurance Products
Data science is widely used by insurance companies in order to manage their risk and ensure that they continue to profit from their business. In addition to separating fraudulent from non-fraudulent transactions, these companies can also use data science to aid in customer retention and acquisition, marketing, credit scoring, and the creation of new insurance policies.
Gaining and Retaining Customers
Customer acquisition and retention is something that benefits every type of business. Data science collects information from consumers’ profiles in order to create a personalized experience based on their previous behaviour. By looking into what previous items a person has purchased, it can more easily detect what they may want to purchase in the future. This increases the likelihood that a customer will want to return somewhere to give them business.
When an individual uses a company to check their credit score, something called logistic regression is used in order to determine whether a borrower will be good or bad. This allows the company to take an individual person into account rather than relying purely on stereotypes based on their demographics. This is an incredibly important component to companies that offer credit rating and credit scoring services.
Predicting Trends With Trade, Blockchain, and Cryptocurrency
It is incredibly important to be able to predict the direction a trend will run. Data science can be used to find trends and, conversely, risks in investment. Companies, customers, banks, and other related organizations can use this data in order to make more educated decisions about their purchases and investments so that they may invest more wisely.
While there are countless ways that data science can assist in the field of fintech, these are some of the most prominent. It is clear that this advanced field is one that benefits highly from the things that data science can provide.