Despite the challenges, data science and the insights that it offers marks it as a universal fuel that can propel banking business to the top.
FREMONT, CA: As the significance of big data grows, its impact on the banking industry is becoming more apparent. It all sums up a total of 2.5 quintillion bytes of data that the global population produces daily as per reports — this open endless opportunity for the most progressive financial institutions to capitalize on data. While digital banking is used almost the half population, banks have enough data at hand to rethink the way they operate to become more efficient, customer-centric, and profitable. Below are common use cases for big data in banking.
• Personalized Customer Experience
Today customers are looking for a more personalized, curated experience. Just like any other businesses in different domains, banks use big data to get to know their customers and as a result find new ways to cater to them, connect with them in a more meaningful way and deliver more value. Big data can give valuable insights into customer behavior and help banks optimize customer experience accordingly. With a complete customer profile in hand, banks can predict and prevent churn. By analyzing the data about previous transactions, banks can also identify accounts that are most likely to close shortly.
• User Segmentation and Targeting
Leveraging big data can better understand customer's needs, pinpoint issues in product targeting, and find the best way to fix problems. Banks can also use social listening, sentiment analysis to source actionable insights from user activity on social network platforms.
• Business Process Optimization
As a result of advanced automation among banks, they can experience significant cost savings and reduce the risk of failure by avoiding the human factor from critical processes. Companies can use artificial intelligence and machine learning to optimize business processes. Data based automation initiative powered by cloud network can reduce the time needed to review documents, which previously requires hours of work.
• Security and Risk Management
Banks rely on big data and AI to identify fraud and prevent malicious activities among its employees. The bank analyzes a vast amount of data to identify individual behavior patterns and reveal potential risks. Data science uses real-time machine learning and predictive analysis to process big data to pinpoint fraudulent behavior and minimize financial risk for online banking service providers.
Banks need to rethink their operations and adopt data-driven initiatives if they want to stay competitive and relevant in the market. It helps to explore opportunities for those who strive to emerge victoriously.