How Predictive Analytics Can Help in Improving Fintech

 In Blog

Over the years, Fintech and customer expectations from it have increased manifold. The total value of worldwide Fintech investments is expected to reach $46 billion by 2020. This is driven by significant technology advancements such as smartphones and wearables as the surge in social media usage and the generational shifts driven by millennials. To meet these expectations, companies are making use of modern technology to innovate financial services delivery and completely transform their bottom line. But a new wave of financial management solutions – comprising predictive analytics, machine learning, Artificial Intelligence (AI) and Augmented and Virtual Reality (AR/VR), is set to take the industry to a whole new level.

'Terminator' of Artificial Intelligence

Predictive Analytics in Fintech

In the rapidly changing financial industry, predictive analytics enables organizations to discover trends, answer key business questions, and make data-driven business decisions. It enables companies to uncover critical customer insights, fuel initiatives to improve customer satisfaction and loyalty, and drive improved efficiency and profits.
Here are 5 ways predictive analytics can help in improving Fintech:

  • Get insights into consumer behavior:Predictive analytics can enable financial companies to gain extremely valuable insights into consumer behavior. When armed with this information, banks can get the opportunity to significantly impact the success of their marketing efforts to both new and existing customers and also make better, data-driven decisions to meet their requirements. With insight into customer behavior, financial companies can provide intelligent recommendations, target the right customer with the right offer at the right time based on their distinct needs and provide better value. Financial services company Allianz uses analytics to analyze accident data, predict the future likelihood and adapt products almost immediately.
  • Improve customer segmentation:With so many financial products and services being offered in the market today, knowing which product or service will suit which customer segment is a herculean task. With predictive analytics, companies can uncover previously hidden patterns and generate more in-depth and behavior-based customer segments. The result is more precise segmentation that can help companies to target specific groups with financial products that will best resonate with them. It is a win-win situation as customers are offered more relevant products and services and financial institutions can improve their bottom line.
  • Unearth macro-economic trends:Financial institutions across the world possess humongous amounts of operational, transactional, and balance data that holds critical information about macro-economic trends. Although this information can be valuable for investors and policy-makers, companies have not been able to utilize this data due to technology roadblocks. Predictive analytics can gather data from different departments including mortgage, consumer banking, personal credit and insurance and help companies derive critical value from the data especially around global trends, industry dynamics, market conditions and the regulatory environment. Such insights can enable companies to optimize their operations and improve their business process efficiency. For instance, predictive analytics is enabling global investment bank Goldman Sachs’ teams to get answers to millions of complex questions around global market scenarios in seconds.
  • Effectively manage risk: Every day, financial institutions deal with millions of customers, applications, and services that need to be checked and verified for authenticity and fraud. Although traditionally, banks have consulted third-party risk scoring companies, the occurrence of risks has costed companies billions of dollars in losses, some of which get passed on to the customers. However, with predictive analytics in Fintech, companies can store and analyze multiple data streams, get end-to-end visibility into all their operations and efficiently isolate and minimize risks across their organization. Companies can thus make better decisions and control risk by authorizing individuals, updating policies, and categorizing patterns of fraud. Over time, the accumulated data can detect subtle, high-risk behavior patterns unseen by risk analysts, allowing companies to operationalize security and privacy measures such as encryption and user authentication.
  • Meet compliance standards: With fraud and financial crisis looming large over the financial industry, it becomes imperative for companies to adopt strict standards. However, with organizations being bombarded with thousands of new regulations and changes to existing regulations each year, meeting compliance standards is not easy. According to a PwC report, 54% of companies consider data storage, privacy and protection as the main regulatory barrier to innovation. In the age of regulatory volatility, predictive analytics in Fintech enables companies to get more direct and secure access to compliance data, conduct better internal audits, identify required or redundant rules and regulations, improve the accuracy of audit selection and reduce the risk of getting penalized by financial regulators. Multinational investment bank JP Morgan uses predictive analytics to visualize and anticipate market trends and reduce the cost and complexity of compliance.

Improve Efficiency

In a highly volatile financial industry, predictive analytics is a powerful force reshaping the way companies are functioning. Predictive analytics in Fintech can enable organizations to positively transform their operations: from insights into customer behavior to improved customer segmentation, insight into macro-economic trends to effective risk management and adherence to compliance – the opportunities to improve business efficiency are myriad. So, harness these capabilities, capitalize on the opportunities and become a front-runner in the market.

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