Benefits of Big Data for Banking Industry

Big data, along with other emerging technologies such as artificial intelligence (AI), machine learning, and robotics, is helping to transform various industries across the globe, including the banking sector.

Big data is a result of the need to process data sets that have become too large and complex for traditional tools to handle. By aggregating large amounts of data from various sources and leveraging cloud computing, big data is a powerful tool for business decision-making, providing insights and behaviors that traditional business intelligence (BI) cannot match.

Despite the advancements in operations and service delivery in the financial and banking industry over the past decade, many banks still practice traditional methods and have yet to fully utilize the information within their own databases. This valuable data, if analyzed and used correctly, can provide valuable business intelligence that can give banks a competitive edge and create products and services that truly meet the needs of the market. However, this is set to change as the banking sector prepares to process immense volumes of data. Some experts predict a seven-fold increase in data volume before 2022. Big data is a significant step in the development of the banking industry and will propel it into the 21st century. Here are a few benefits of big data for the banking industry:

1. Fraud Detection and Prevention: Banks and financial services firms use analytics to differentiate fraudulent interactions from legitimate transactions. By applying analytics and machine learning, they can define normal activity based on a customer’s history and distinguish it from unusual behavior indicating fraud. This allows for immediate action to be taken, such as blocking irregular transactions, which stops fraud before it occurs and improves profitability.
2. Enhanced Compliance Reporting: Banks now have access to millions or even billions of customer needs, and big data allows them to cater to these needs in a more meaningful way. Cloud-based analytics packages can sync in real-time with big data systems, creating actionable insight dynamically. This will allow banks to earn more revenue through cost reduction and provide customers with exactly what they are looking for.
3. Customer Segmentation: Banks are under pressure to change from product-centric to customer-centric businesses. Big data enables them to group customers into distinct segments, which are defined by data sets that may include customer demographics, daily transactions, interactions with online and telephone customer service systems, and external data, such as the value of their homes. Promotions and marketing campaigns can then be targeted to customers according to their segments.
4. Personalized Marketing: Customer segmentation can further be used to create and deliver new schemes and plans aimed directly at the specific requirements of their customers. By analyzing past and present expenses and transactions, a bank can understand how to get the highest response rate from their clients. This will create opportunities for personalized product offerings, catering to an untapped niche of personalized services, allowing banks to create more meaningful client relationships.
5. Customer Next Banking Need Prediction: By consolidating or linking all customer previous purchase data across all silo databases and records from different types of accounts and transactions, banks can predict their customers’ next banking needs, providing relevant and valuable insights.

In summary, big data is a powerful tool that can greatly benefit the banking industry by enabling better fraud detection and prevention, enhanced compliance reporting, improved customer segmentation, personalized marketing, and customer next banking need prediction.

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Original post create 2017-Nov-7, last rewrite and update 2023-Jan-9.