How Data Analytics is Improving Fintech Industries?

Financial analytics is a discipline that seeks to determine a company's performance by using analytics of financial data, in comparison to its peers. The objective is not to predict future results, but to provide accurate and factual insight for shaping business strategy. Financial analysis can be used by companies that are analyzing their operational costs, such as prices, expenses, and profits. For example: measuring performance against KPIs and completing analyses that enable organizational leaders to make intelligent decisions about win-win choices later on

Why Is Big Data so Important for Fintechs?

The advent of big data technology has given fintech companies a competitive edge in the market, while large banks struggle to adapt. This new field of fintech banking is gaining momentum and showing promise to disrupt traditional models. By harnessing the power of real-time data on customer behavior, emerging fintech are developing increasingly sophisticated risk assessments and applying these results to develop aggressive strategies at a fraction of the cost.

Fintech may deal with big data, but so do other industries. Big data has historically been used by companies that process large volumes of transactions, like banks and insurance companies. The industry needs to be agile to stay competitive. Companies need to be able to process and analyze large sets of data to make better decisions and create personalized experiences for customers.

The Criticality of Big Data for Fintech

Fintech companies have been exploiting the benefits of big data for predicting customer behavior and then developing complex risk assessments that differentiate them from legacy financial players. New-age fintech companies are using data analytics to create a comprehensive view of customer behavior, ensuring they are able to develop more knowledge about their customers, reduce fraud and achieve better risk management. The velocity of real-time information allows fintech startups to disrupt traditional lending services while simultaneously adapting to changing market conditions.

Fintech can currently process vast amounts of data faster than legacy players. Fintech is well-placed for faster decision-making, creating bespoke customer experiences and spotting and solving a new problem area for the customer. As a result, they are able to create incremental value by differential growth between them and the legacy players

How Can Big Data Help Fintechs?

The applications of big data analysis have four main benefits for emerging fintech:

Customer Orientation: Big Data is helping fintech personalize its services to better meet the needs of its individual customers. These technologies provide detailed user profiles and accurate customer segmentation strategies to tailor their business models. Sophisticated modeling techniques can provide personalization services that consider an individual’s perception of risk, age, gender, wealth, location, and even relationship status.

Improved Security: Fintechs are using big data to help them develop reliable fraud detection systems and safeguard the security of their customers' accounts. They have been able to use large volume of data from within and outside their financial institutions, such as credit card numbers or bank account records, to spot any unusual transaction. This is a great help in detecting fraudulent activity, which could otherwise go unnoticed by employees or even customers.

Better Risk Assessments: Traditional financial institutions use data to make risk assessments. But big data analytics serivce can help fintechs identify hidden issues and clear up inconsistencies. The increased availability of customer input increases the accuracy of risk assessments, leading to better rates for customers and higher yields for investors.

Unbeatable Customer Service: Fintech is changing the way we deliver customer support and experience. Today, there are banks where you can have a live chat with a customer care representative and get an answer to your question within seconds, instead of having to wait hours. Fintechs use big data analytics and machine learning to process huge volumes of information and help their customers in real time.