The application of statistics and computation to the real world has introduced a new paradigm; data science. Data science is a process by which everything from the media to business goes through an entire transformation into more efficient and productive activities. For example, it’s more efficient in the financial world as it has spawned a new financial era by the name fintech.
It is rapidly altering the financial landscape by fostering the application of complex calculations and big data to financial decision-making. Cane Bay Partners CEO is pushing for data science in businesses after knowing how effective it is in handling big data at work. The following are the roles data science in Fintech plays.
Risk assessment
In financing, weighing and evaluating risks is a crucial activity. This process starts from the creation of working capital loans to investment decisions. Data science has become the backbone of it all, allowing Fintech to grow quickly and have more precise credit risk decisions. The preciseness of the evaluation helps to open a new client base as well as lower the credit risk. Data science gives online lenders and others to precisely determine the creditworthiness of a person through evaluating approximately 15,000 data points.
Payments and purchasing habits
For a while now, data science is seen to have the potential of allowing customer’s payment history and buying history to undergo an accurate evaluation procedure at the granular level. Granular evaluation is essential in opening doors for precise prediction models that determine the expected behaviors from then onwards. The evaluation process varies from the basic analytical scores from one month to another and more complex calculations, such as those that use payment records and spending habits. This is important, especially when targeting marketing, loyalty rewards, and other active client interface forms.
Determine lifetime customer value
Data science is necessary for allowing fintech companies to get deep into and clarify the lifetime value of customers. It doesn’t only view customers as one-time transactions, but the application of FintechFintech gives room to an entire potential lifetime purchase volume that goes through an entire evaluation process.
There is the creation of opportunities for upselling and targeted marketing. This is based on where the customers are on the model. Fintech is known to use metrics more than social media as it gets immediate feedback that helps in building a lifetime value model. In this case, with a good knowledge of a customer’s lifetime value, it becomes easy for the correct application of resources to the customers in the future.
Asset management
Data science has the potential of providing organizations with the power to crunch massive amounts of data used to create asset management models. This ensures higher risk-adjusted returns for clients without problems. The use of data science to FintechFintech is continuously creating a new wave of Robo-advisors helping individual investors. Robo-advisers help to remove the emotions inherent among humans when it comes to making decisions. The Robo-advisers’ decision-making process always considers data points and historical trends essential in developing scientifically sound asset allocation decisions in the entire investable asset.
Fraud detection and prevention
Today, data science is seen to radically foster the entire process of fraud detection and prevention. Fraud prevention is considered among the highly ranked fintech companies’ highly ranked priorities, where many resources are directed towards this direction. Early warning systems using data science have been designed to provide efficient predictive abilities with that in mind. It is important to note that without data science, there is the likelihood that FintechFintech wouldn’t be in existence. Data science helps fintech firms to make efficient decisions about their operations.