Role of Data Science in the FinTech Industry


Data science has helped transform the banking and financial industry by ensuring that both customers and banking officials can effectively carry out their specific transactions. For instance, when customers use PayPal, Amazon Pay, and Credit cards to make online payments, their banks and business organizations use FinTech to ensure the transaction becomes successful. Apart from that, FinTech depends on predictive analytics, artificial intelligence, and machine learning for effective functioning. Continue to read this article to understand the role that data science has played in Fintech.

Fraud detection

Currently, financial experts use data science techniques to identify fraud when undertaking financial transactions. Detecting fraud is based on specific rules, including flags triggered by Ip addresses used and location. However, instead of relying on a finite number of transactions, people can improve the process by relying on machine learning techniques such as naïve Bayes classifier, and logistic regression. Such methods determine the probability of a transaction being fraudulent according to historical transaction data. That helps to enhance accuracy, and one can employ it on live data, assisting FinTech organizations in undertaking effective actions.

Asset management

Data science provides major organizations with the ability to collect substantial data to develop asset management models to earn increased risk-adjusted returns from their customers. Currently, applying data science to FinTech has enabled the establishment of rob-advisors focused on individual investors. In this case, Robo-advisers help to eliminate emotions inherent within the process of making decisions people decisions. Also, Robo-advisors’ decision-making process considers historical trends and data points to undertake scientifically sound decisions of asset allocation with the spectrum of the investable asset. Such diversifications offer limited risk and enhanced strategies for managing investments.

Loyalty programs, customer retention, and marketing

FinTech firms gather substantial amounts of data from their users that often remain unused unless they use it for financial analysis. However, such customer information, starting from personal information, transaction data, and social media presence, can boost marketing efforts. In particular, they can offer personalized and contextual discount offerings or product advertisements to enhance the customer churn rate. For instance, Cane Bay Partners is one of the global organizations that build algorithms to predict the additional products the customer might prefer to buy based on their previous purchase behaviors.

Further, FinTech can use diverse metrics such as social media feeds in directing feedback through customer surveys to establish a lasting value model. Instead of categorizing clients into one-time transactions, the use of Fintech enables organizations to evaluate the whole potential of a long-term purchase volume. When organizations understand the customer’s long-term value, they get an opportunity to effectively apply resources to people with a higher probability of bringing more value in the future. In that way, they can optimize their marketing costs.

Revenue and debt collection

Data sciences allow financial experts to use powerful predictive models to optimize their debt and revenue collection. For instance, it is currently possible to predict a probability of making timely payments at the time of purchase, making revenue collection more transparent. Similarly, one can apply predictive modeling and behavioral economics insights to enable a more successful debt collection after the due date has elapsed. In summation, organizations need to know that it is a delicate undertaking to determine the optimal strategy of approaching debtors, meaning they should not leave it to random guessing.

Risk assessment

The evaluation of risks is one of the critical aspects of the financial sector. In this case, risk evaluations encompass several aspects, starting from making investment decisions to establishing working capital loans. Fortunately, data science forms a backbone that enables Fintech to establish more precise and faster credit risk decision-making processes. Notably, the preciseness of an evaluation reduces credit risk and opens a new client base. For instance, data science enables online money lenders to determine the creditworthiness of the loan applications accurately. In most cases, traditional data, including credit scores and word usage, is used to develop a credit risk model.

Share this post:

Recent Posts