How Big Data Analytics are Used in the Banking Industry
Business + ManagementBankingdata analyticsposted by Shannon Flynn December 28, 2020 Shannon Flynn
Banking institutions need to use big data to remodel customer segmentation into a solution that works better for the industry and its customers. Basic customer segmentation generalizes customer wants and needs without addressing any of their pain points. Big data allows the banking industry to create individualized customer profiles that help decrease the pains and gaps between bankers and their clients. Big data analytics allows banks to examine large sets of data to find patterns in customer behavior and preferences. Some of this data includes social media behavior.
- Demographic information.
- Customer spending.
- Product and service usage — including offers that customers have declined.
- Impactful life events.
- Relationships between bank customers.
- Service preferences and attitudes toward the banking industry as a whole.
Providing a Personalized Customer Experience with Big Data Analytics
Banking isn’t known for being an industry that provides tailor-made customer service experiences. Now, with the combination of service history and customer profiles made available by big data analytics, bank culture is changing.
Profiling has an invasive ring to it, but it’s really just an online version of what bankers are already doing. Online banking has made it possible for customers to transfer money, deposit checks and pay bills all from their mobile devices. The human interaction that has been traditionally used to analyze customer behavior and create solutions for pain points has gone digital.
Banks can increase customer satisfaction and retention due to profiling. Big data analytics allows banks to create a more complete picture of what each of their customers is like, not just a generic view of them. It tracks their actual online banking behaviors and tailors its services to their preferences, like a friendly teller would with the same customer at their local branch.
Artificial Intelligence’s Role in Banking
Nothing will ever beat the customer service you can receive in a conversation with a real human being. But human resources are limited by many physical factors that artificial intelligence (AI) can make up for. Where customer service agents may not be able to respond in a timely manner to customer inquiries depending on demand, AI can step in.
Chatbots enable customers to receive immediate answers to their questions. Their AI technology uses customer profile information and behavioral patterns to give personalized responses to inquiries. They can even recognize emotions to respond sensitively depending on the customers’ needs.
Another improvement we owe to AI is simplified online banking. Advanced machine learning accurately pulls information from documents uploaded online and on mobile apps. This technology is the reason why people can conveniently deposit checks from their smartphones.
Effective Fraud Prevention
Identity fraud is one of the fastest growing forms of theft. With more than 16 million identity theft cases in 2017, fraud protection is becoming increasingly important in the banking industry. Big data analytics can help banks in securing customer account information.
Business intelligence (BI) tools are used in banking to evaluate risk and prevent fraud. The big data retrieved from these tools determines interest rates for individuals, finds credit scores and pinpoints fraudulent behavior. Big data that’s analyzed to find market trends can help inform personal and industry-wide financial decisions, such as increasing debt monitoring rates.
Similarly, using big data for predictive purposes can also help financial institutions avoid financial crises before they happen by collecting information on things like cross-border debt and debt-service ratios.
The Future of Big Data Analytics
The banking industry can say goodbye to their outdated system of customer guesswork. Big data analytics have made it possible to monitor the financial health and needs of customers, including small business clients.
Banks can now leverage big data analytics to detect fraud and assess risks, personalize banking services and create AI-driven customer resources. Data volume will only continue to increase with time as more people create and use this information. The mass of information will grow, but so will its profitability as more industries adopt big data analytic tools.
Big data will continue to aid researchers in discovering market trends and making timely decisions. The internet has changed the way people think and interact, which is why the banking industry must utilize big data to keep up with customer needs. As technology continues to improve at a rapid pace, any business who falls behind may be left there.
About the author: Shannon Flynn is a tech writer and Managing Editor for ReHack.com. She covers topics in biztech, IoT, and entertainment. Visit ReHack.com or follow ReHack on Twitter or to see more of Shannon’s posts.