Top Use Cases For Banking Analytics

In today’s post fnancial crisis environment, banks face a costly and complex web of challenges that have become their new normal. Customers are now more savvy, demanding and less loyal. New regulations continue to emerge that require more granular and frequent demonstrations of governance and control. The ability to understand risk well enough to act on insights remains a challenge. And capital remains scarce, requiring a tighter focus on operational e ciency and decisions that are both risk-informed and capital adjusted. The competition for proftable returns in this marketplace is extreme.

 

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Data Analytics will be the key for banks moving forward. Determining customer proftability is not a simple endeavor for most  nancial institutions. Disparate data sources and disconnected systems make it difcult to aggregate client account level information for key dimensions like product, line of business, geography and to gain actionable insight from structured and unstructured data related to marketing, sales and service interactions. These obstacles can prevent banks from determining what makes a customer pro table in the long term, identifying their most valuable customers, developing account retention strategies, and raising the lifetime value of the rest of their customer base.

 

 

 

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12 Top Banking Analytics Use Cases 

 

  1. Behavior-based pricing - Use insight on individual customers to make pricing decisions.
  2. Life event marketing  - predict upcoming life events of a customer for target marketing, offers, and contacts.
  3. Cross-sell/up-sell - leverage insights on customer life & financial events, peer comparisons to propose products and services
  4. Targeted offers - Analyze payment data to identify spending patterns and make targeted offers- all in real time.
  5. Customer retention - proactively predict attrition and engage with customers at risk based on analysis of cause.
  6. Overdraft Alerts - review customer’s cash flow and predict overdraft.  Trigger alerts and recommended products.
  7. Travel Support – predict upcoming or in-flight travel, reduce card declines proactively, and offer services (exchange, insurance).
  8. ATM awarenessproactively identify patterns of foreign ATM fees and send proactive notices to prevent fees.
  9. Balance growth - identify increasing balances, identify most likely cause and recommend products, services, or contact.
  10. Auto bill-pay - identify salary deposits and notify customer and offer option to pay bills online.
  11. Regular non-bank payments - provide payment reminders to for regular payments and offer online payment options.
  12. Improve banking experience - identify and shift customers to mobile deposits, automated billpay, and other services.

 

 DataHub is working closely with IBM to help banks build out their analytics strategy. If you would like additional information or set up a call, please reach us at This email address is being protected from spambots. You need JavaScript enabled to view it.

 

 

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