5 Ways to Apply Data Science to Fintech
Data is said to be one of the currencies of the future, and its relevance for any type of business is such that a company might lose its share in the market, and even its entire presence in the upcoming years if they neglect the role of data shaping the future of the industry as we know it.
The area of Fintech, short for financial technology, has been largely transformed by data science, disrupting most of the characteristics of financial services, such as cryptocurrencies, insurance, consumer finance, and investment. Whether you just enrolled in an online Fintech program or are a veteran in the field of financial technology, the following list is likely to draw the attention of anyone with a career in Fintech.
Risk analysis: finance is all about evaluating risk, and data science has accelerated and improved this process in Fintech. Treating large data sets in a fraction of the time that it would have taken traditional loan risk evaluation models in the past, and achieving this with impressive accuracy allows the company to make safe risk allocation decisions through enhanced credit risk models. Furthermore, said models take into consideration factors that would otherwise be hard to weigh, offering a more precise estimation of an individual’s creditworthiness.
Fraud detection: one of the most commonly discussed topics in MBA finance and other Fintech courses in India and around the world is the role of data science in fraud detection within Fintech applications. Just the fact that data science allows firms to examine transactions in real-time is a tremendous step forward, given the amount of data that needs to be monitored. If on top of that, we consider that it is possible to identify the transactions that do not follow the trends, we will understand that the fraud detection and prevention process is faster and safer now that data science has been closely adopted by Fintech firms.
Lifetime customer value attribution: if you are in the process of building your career in Fintech, you will agree that for most business models in the world of financial services, the customer lifetime value is an essential parameter. Data science is a particularly powerful ally for creating connections between all sorts of interaction data, which allows Fintech firms to have sound and accurate predictions about customers who can be offered particular services or those who are more likely to quit their current ones.
Asset management: the amounts of data that can be included within asset management models thanks to data science have given rise to the creation of robot advisors. This figure consists of algorithms that create a profile of the customer, which includes his/her financial status, risk capacity, and objectives, and makes decisions based on the combination of these factors and the financial instruments offered by the institution. Taking into account historical data points and trends, while avoiding the emotional parameter involved in the decision-making process carried out by humans, Robo advisors are the future of asset management.
Insurance services: many online Fintech programs have modules dedicated to the branch of insurance services, as they combine applications such as risk assessment, tailored marketing, lifetime customer value attribution, fraud detection, and prevention, claims prediction, among many others.
Discussing these topics has intensified your interest in the field and you are now looking for Fintech courses in India, you will be glad to know that Imarticus has a broad variety of financial technology courses that cover the trending themes and much more. Visit Imarticus’ portal and find out!
