In an ever-evolving digital world, industries across all domains are stepping up to enhance their customers’ experience. It is especially true in the financial service industry, where everything from banking, payments, trading to wealth management has gone online. Increasingly, more fintech platforms are using data analytics to understand consumer behavior and market trends with advancements in technology. It helps them improve their services and products, thereby meeting customer demands better.
Increasing use of smartphones and internet penetration creates heaps of data every day. It consists of structured and unstructured data, also known as a data lake or big data. Leveraging this, the fintech industry gains data-driven insights that help them make strategic decisions for enhanced customer experience. A report by Mckinsey states that data-driven organizations are 23 times more likely to retain customers and are 19 times more likely to bear profits.
Let’s dive deeper to understand 6 major ways in which big data analytics improves customer experience.
Enhanced view of customer profiling
Knowing the customer segment well is the first step in ensuring customer satisfaction. This is where big data analytics steps in. Fintech players can utilize big data to understand their customers’ wants and needs by creating customized consumer profiles. Details like demographics, risk perception, employment type, financial status, behavioral patterns, etc., can be considered while creating segments. As a result, it helps the fintech segment and customizes its offerings and services according to specific customer demands.
Better risk assessment
One of the biggest factors while participating in the fintech industry is managing risks. Using big data analytics helps in combining data from multiple sources and determine the potential risk factors. The risk assessment analysis further helps the fintech devise strategic plans to rule out the risk related to certain situations or market trends. Meanwhile, big data also minimizes the threat posed by unethical hacking practices by facilitating time-to-time risk assessment.
Fraud is an oddly common problem in the financial services segment, be it online or offline. To rule this out, big data analytics can help the fintech industry develop a fraud detection system. By keeping a complete record of data, including location, history, device, transactional pattern, data analytics can raise a red flag in case of unusual activity. This helps in developing a customer-friendly secularized fintech ecosystem.
Along with this, big data analytics also facilitates risk assessment to ensure improved security. For instance, a credit risk management analysis combines the data from multiple sources to ascertain a CIBIL score that takes an individual’s credentials and financial behavior into account. An accurate analysis of the data helps the fintech organizations determine whether the applicant will be able to repay the loan. This helps in assuring financial certainty while ruling out any bad lending decisions.
Forecasting future market trends
By gaining a deeper understanding of the past and present trends, users can even get a glimpse and form predictions about the surge or dip in the market. It helps the investors and traders make well-informed trading decisions, thereby improving their experience. Besides this, predictive analytics help streamline fintech operations like optimizing cash flow, offering competitive rates, etc., for improved customer retention.
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