Content Summary
A revolution has begun powered by data science, which is yielding significant improvement in many sectors of businesses. With the proliferation of smartphones, the internet, and other devices, companies and government agencies have recognized the massive potential of using data science information to drive real value for customers and improve efficiency.
Players in financial technology (fintech) companies have since known the benefits of using data science to manage, grow and build their businesses with advancements in technology in areas like machine learning fields, big data, and artificial intelligence. Fintech firms now have a whole new set of opportunities for the type of products they offer their customers and the available money they have to offer. Cane Bay partners reckon that fintech firms can use data science and artificial intelligence to speed innovation. This is how data science is applicable in fintech.
Transactional
Fintech uses data science to analyze a large volume of past records. This application helps them o create behavioral patterns and use them to anticipate the future trends of customers. The most useful application is for determining a person’s spending behavior. The app can measure each client’s payment behavior, and then it splits them into different categories and then sends warnings when a customer surpasses a certain threshold.
Another use is on credit cards. It can be integrated with payment channels to automate and streamline different steps, therefore saving the clients their valuable time and ensuring that they pay on time, and increase their credit score.
Particular attention focuses on fraud prevention and the evaluation of the transaction. Also, through pattern analysis, fintech firms can use these applications to prevent identity theft. By just looking at the metadata connected to each customer transaction, they can detect any misbehavior. Any sudden change of customer transfer amount, geolocation, the system will trigger additional verification measures.
Investments
When it comes to the investment sector, the most critical issue that financial sectors pay close attention to is the risk evaluation before building a portfolio or giving out credits, and therefore, Fintech firms have come out with numerous solutions to these challenges. For instance, through machine learning algorithms, they can use large volumes of data regarding current transactions to teach robot advisers how to create hedge portfolios automatically and how to minimize losses from occurring. These approaches reduce expenses because scenarios can be generated and tested in seconds without undergoing a real investment.
It is worth noticing that, in as much as most decisions are centered on patterns derived from numbers, data science offers the opportunity for fintech to use sentiment analysis as an innovative way to assess assets since the market feeling on companies can meaningfully influence stock variation.
Client retention
Having the correct data about a customer can help a firm do the estimation of the exact lifetime value of each one of them and even define their journey. This is so useful for a company to use it to direct promotional activities and marketing resources proficiently. Additionally, suppose clients show signs that they intend to end their relationship by making a late payment or not responding to communication. In that case, it could indicate that they need some additional reasons to continue working with the company.
Although the data that companies usually their customers provide may not all be useful from a financial viewpoint, it can still be used to learn about customer behaviors, preferences, and things that may influence them to make a buying decision. Therefore, it can be used by the company for marketing purposes. So you should use each data collected from a customer to its full potential.
