While most businesses may not have adopted big data, data science and analytics are revolutionizing the fintech industry. The ability to process large amounts of data and derive meaningful insights through analytics has an immense impact on fintech. Not only does it help in making smart business decisions, but it also plays a key role in boosting efficiency and customer experience. Note that analytics and artificial intelligence go hand in hand. To help you understand how powerful analytics are in fintech, read on to find out their impact in fintech.
1) Customer Orientation
Big data collects numerous information, including customer details such as age, customer behavior, sex, location, and wealth. Fintech companies use analytics to evaluate the data and establish accurate customer profiles. With specific customer profiles, fintech companies can develop customer segmentation strategies that improve their ability to meet customer expectations and needs.
Financial institutions can then tailor their services and products for the different customer segments. By personalizing their services to suit customer needs in their various categories, fintech can achieve better customer satisfaction. With proper customer orientation, fintech companies can achieve higher customer retention.
2) Improved Security
The increased use of online banking and internet transactions predisposes fintech companies to fraud. They are also at a high risk of cybersecurity breaches, making security one of the most critical aspects of their operations. Data science and analytics play key roles in ensuring security by identifying fake behavior that may lead to fraudulent transactions and cybersecurity risks.
Analytics can monitor customer spending behavior and online patterns, making it easy to identify fake or unusual behavior. In case of abnormal behavior in a customer’s account, the financial institution can immediately contact the account owner for clarification, thereby preventing and minimizing fraud cases. The use of analytics in fraud detection systems helps protect customer’s money and inspires confidence in the company.
3) Better Risk Assessment
While risk management is critical for all businesses, it is especially crucial in the fintech industry. When lending money or providing credit services, financial institutions require impeccable risk assessment and analysis to avoid defaulting customers and bad payers. Fintech companies use big data and machine learning to collect as much relevant information as possible from various sources to help establish whether a client qualifies for the loan.
Financial institutions can track a customer’s spending over time and use the information to predict their future spending, as well as any changes in their creditworthiness. While analytics may not entirely protect fintech businesses from risks, they minimize their chances of occurrence and halt any further progress towards risky paths. With predictive analytics, banks and credit rating companies have more certainty about whether the money they lend will fetch returns. They can avoid bad debts easily.
4) Robo Advisors
Analytics has made it possible for financial institutions to automate most of their services, including provisions of financial advice. Robo advisors refer to platforms that provide investors with automated and algorithm-driven investment services and insights. Usually, the services leverage technology and big data to provide investment insights and information. It, therefore, uses minimum human intervention. While having a human financial advisor may add a personal touch to interactions with clients, data-driven insights are of greater importance when making investment decisions.
For Robo-advisors to be effective, they have to collect information on the investor. These include details such as financial goals and status, as well as risk capacity. This allows them to provide relevant financial advice that suits the client. In some cases, Robo-advisors automatically invest customer’s assets in instruments that match their goals and needs. Besides providing financial information, Robo-advisors also answer investor’s queries and automate paperwork.
5) Excellent Customer Service and User Experience
Big data analytics enable financial institutions to provide better customer service and user experience. Customers don’t have to wait in long lines, deal with endless paperwork or wait for hours to speak to managers or financial advisors. With big data, fintech companies can collect crucial information about customer’s banking activities, identify any anomalies and errors, and offer the best customer support.
Besides quick solutions to issues, clients also get relevant and appropriate service/product recommendations based on their spending behaviors. With automation and Robo-advisor services, customers enjoy fast feedback to their queries and concerns, as well as accurate insights and information. Since analytics allow fintech companies to predict customer needs and expectations, they can offer more efficiency and achieve customer satisfaction.
The above information gives you a clear picture of how fintech applies analytics to enhance its operations and results. By using the vast amount of data as an asset, fintech companies get to fuel change and growth in the industry. With the right intelligent data analytics platform, you can derive valuable insights on the factors responsible for your metrics and make the necessary adjustments to achieve your business goals.