IBM Big Data and Analytics at work in Banking

Summary

Banks face several challenges in returning to pre-2008 profit margins, including low interest rates, tighter regulations, and reduced asset performance. However, leveraging big data and analytics offers solutions. By analyzing diverse customer data, banks can personalize offers, improve customer service, and identify new revenue streams. A customer scenario highlights how predictive analytics can anticipate needs, such as offering extended credit or targeted promotions based on spending habits. Ultimately, big data enables banks to enhance customer experiences, deliver relevant services, and drive both customer satisfaction and financial growth.

Takeaways

• 😀 Big data and analytics help banks overcome challenges like reduced profit margins, instability in financial markets, and tighter regulations.
• 😀 Banks can use big data to generate new revenue streams through personalized offers, targeted cross-selling, and enhanced customer service.
• 😀 Big data allows banks to analyze a wide variety of customer information, including spending patterns, credit scores, and social media activity.
• 😀 Real-time analysis of customer data enables banks to offer products and services at the point of decision, improving customer engagement.
• 😀 The ability to perform deeper data analysis helps banks create segment-of-one marketing, targeting individuals with tailored offers.
• 😀 Predictive analytics allow banks to anticipate customer needs, such as offering credit increases before a customer seeks external credit.
• 😀 Banks can use big data to identify fraud risks and send alerts to prevent unauthorized transactions.
• 😀 Personalized customer offers, such as discounts and financial product recommendations, are based on detailed customer insights.
• 😀 Big data capabilities enable banks to suggest new products like home equity lines of credit or overdraft protection based on customer needs.
• 😀 Big data allows banks to develop new services that align with customers’ daily lives, improving their financial management and satisfaction.
• 😀 By comparing a customer's spending to peers, banks can offer insights that encourage better financial decisions and strengthen customer relationships.

Q & A

What are some of the main challenges banks face in returning to pre-2008 profit margins?

Banks face challenges such as reduced interest rates, instability in financial markets, tighter regulations, and lower performing assets.

How can big data and analytics help banks generate new revenue streams?

Big data and analytics enable banks to offer personalized offers, targeted cross-sell opportunities, and improved customer service by analyzing large volumes of customer data.

What types of customer data can banks analyze using big data and analytics?

Banks can analyze a variety of customer data, including spending patterns, behavior, channel usage, product portfolio, bank interactions, credit information, social media activity, and customer profitability.

How does big data provide 'deeper insight' into customer behavior?

Big data allows banks to dive deeper into customer information and behavior, enabling more precise segmentation and the ability to personalize marketing efforts at an individual level.

How does 'faster insight' help banks with customer interactions?

Faster insight allows banks to perform real-time analysis of customer data, enabling them to deliver offers at the point of decision, making interactions more timely and relevant.

What is an example of big data and analytics in action in the transcript?

An example is when a bank analyzes Peter's recent household purchases, social media activity, and financial data to predict that he will make a large home appliance purchase. The bank then offers him an extended line of credit to seize the opportunity before he turns to a retailer.

How does the bank use big data to offer relevant services to customers like Peter?

The bank uses big data to offer tailored services such as credit limit extensions, personalized offers based on purchase behavior, and fraud alerts, enhancing customer experience and loyalty.

What is the role of predictive analytics in the bank's decision-making process?

Predictive analytics helps the bank anticipate future customer behavior, such as Peter's potential purchase of a stove, allowing the bank to proactively offer financial products like credit extensions or warranties.

How does the bank ensure the security of Peter's account while he makes purchases?

The bank sends an alert to Peter, asking him to verify any large purchases, helping to prevent fraudulent charges on his account.

How does the bank use big data to suggest other financial services to Peter?

The bank analyzes Peter's financial condition, spending patterns, and external data sources to suggest services such as a home equity line of credit, overdraft protection, and a smart sweep service.