The Relationship Between
Fintech and Data Science

Technology innovation in the banking and financial services sector is currently in high gear and has become a favorite of venture capitalists. Most people are using their credit cards or PayPal to purchase items online. As such, the buyer and the seller benefit from fintech or financial technology.

But fintech has disrupted payments and other areas such as security settlement, insurance, consumer finance, cryptocurrencies, and investments. As Cane Bay Partners demonstrates, fintech companies access superior solutions and simplify their financial decisions by employing predictive analytics, artificial intelligence, and data science.

How the Various Fintech Services Use Data Science 

Acquisition and Retention of Customers

Financial institutions often use data at their disposal to determine what the customers want and in what manner. They use the collected data to create a customer profile and customize the offers and improve the customer experience. Banks do this by using an algorithm to foretell the services that clients would be interested in. What kind of products would interest the customers?

Insurance Products

Risk management is critical in the insurance sector if it is to remain profitable. The claims department uses data science to guarantee a reasonable margin for the business. In particular, they use data science to create new insurance products and marketing, customer retention, and credit scoring.

Risk Analysis

Your creditworthiness determines if a particular financial institution gives you a loan or not by using your past credit details to get the credit score. But doing this has not always been easy. In the past, acquiring a credit score involved complicated statistical models, which was no mean feat.

Faster and accurate credit risk evaluation is an important value proposition for startups willing to capture more customers and maintain a competitive edge. A faster credit risk evaluation requires various data sources to enhance the predictive power.

Fortunately, current algorithms use more variables and large data sets for a more accurate and faster evaluation process. Many applications can examine a large number of data points and provide the results within seconds.


The algorithm-driven Robo-advisors provide automated investment services and financial planning. Everything is technology-driven with minimal or no human intervention.

The first process of these platforms is usually to collect information such as the client’s financial status, financial goals, and risk capacity. The Robo-advisors then use this information to invest in asset classes or instruments best suited to the client.

Debt Collection

The use of powerful predictive models allows businesses to predict the chances of timely payments at the time of purchase. This helps with revenue collection and budgeting.

Once the clients surpass the due dates, the business can use predictive modeling and behavioral economics for successful debt collection. How you approach debtors is a sensitive issue, and the company should be careful how they handle it. That is where data science comes in.

Fraud Detection

Unlike in the past, where institutions set the rules for fraud detection, it is now easier with big data and data analytics. You can predict or flag fraud thanks to machine learning techniques and data analytics.


Issues of compliance can determine whether the business data remains secure or runs into trouble with the law or not. The tracking or implementation of compliance behavior and the design of processes to flag or warn in case of non-compliant behaviors is critical. This is especially so for businesses in the financial and technology sectors.

But compliance issues go hand in hand with service quality and cut across customer retention and acquisition, and that is why it is important. The financial and investment sector is quite expansive, and it continues to grow. Thankfully, machine learning and data science innovation make the sector more efficient and transactions quicker.