Various cases where analytics has been used in the banking sector

1. Role of Business Analytics
in the Banking Industry
Vaisakh R Nambiar

2. Introduction
• Banking analytics, or applications of data mining in banking, can
help improve how banks -- segment, target, acquire and retain
customers.
• Additionally, improvements to risk management, customer
understanding, risk and fraud -- enable banks to maintain and
grow a more profitable customer base.

3. Top 5 Advantages Of Big Data In Banking
We Should Know
Efficient Risk ManagementTo Prevent Errors And Frauds
Provides Personalized Banking SolutionsTo Customers
Easier Filing Of Regulatory Compliances
Boosts Overall Performance
Effective Customer Feedback Analysis

4. Power of Analytics with three examples
• Example 1 - To counter a shrinking customer base, a European bank tried
a number of retention techniques focusing on inactive customers, but
without significant results.
• Then it turned to machine-learning algorithms that predict which
currently active customers are likely to reduce their business with the
bank.
• This new understanding gave rise to a targeted campaign that reduced
churn by 15 percent.

5. • Example 2 - A US bank used machine learning to study the discounts its
private bankers were offering to customers.
• Bankers claimed that they offered them only to valuable ones and more
than made up for them with other, high-margin business.
• The analytics showed something different: patterns of unnecessary
discounts that could easily be corrected.After the unit adopted the
changes, revenues rose by 8 percent within a few months.
Power of Analytics with three examples

6. • Example 3 –A top consumer bank in Asia enjoyed a large market share but
lagged behind its competitors in products per customer.
• It used advanced analytics to explore several sets of big data: customer
demographics and key characteristics, products held, credit-card
statements, transaction and point-of-sale data, online and mobile
transfers and payments, and credit-bureau data.
• The bank discovered unsuspected similarities that allowed it to define
15,000 microsegments in its customer base. It then built a next-product-to-
buy model that increased the likelihood to buy three times over.
Power of Analytics with three examples

7. Conclusion
• To summarize,Analytics provides banks with more marketing muscle.
• Functional areas like Risk, Compliance, Fraud, NPA monitoring, and
CalculatingValue at Risk can benefit greatly fromAnalytics to ensure
optimal performance, and in order to take crucial decisions where timing is
very important.
• The use of Analytics can help banks differentiate themselves and remain
competitive in the future.