Consultant, IBM Center for Applied Insights
In my last post, I took you through some of the Retail industry data from our State of Smart work.
Today, we’re going to take a deeper look at the Banking industry.
If you look at the distribution matrix below, the first think you’ll notice is that 46% of the respondents
were identified as “Outperformers”. This
was the highest ratio of Outperformers of any of industry we
surveyed. Simultaneously, 33% of
respondents were identified as having low Listen and Anticipate capabilities.
What we’re seeing here is an interesting dichotomy. Simultaneously, a significant proportion of
the industry are Outperformers while a smaller yet significant proportion of
the industry has low Listen and Anticipate capabilities – without much in between. This tells us that while Banking is clearly one of the more advanced industries when it comes to data and analytics, there are still significant opportunities for improvement.
As you might expect, the Banking industry Outperformers capture quite a
bit of data. 79% captured customer data at every interaction (2.1x more than the Others). Additionally 58% of the Outperformers
captured unstructured data (1.6x more than the Others).
What is that data used for? Interestingly, both Outperformers and Others
used analytics to guide the actions executive decision makers (83% and 79%
respectively). This was by far the
smallest gap in this capability between the Outperformers and Others of any
industry and suggests that this capability is “table stakes”.
However, there are several uses of data that differentiate
Outperformers from Others. First, 84% of
Outperformers provide insights to suppliers and business partners (2.4x more
than the Others). Second, the Banking
Outperformers tied for the highest percentage usage of analytics
to recommend actions to customers among the industries (87% - 1.7x more than the Others).
Finally, we saw 2 very interesting results when we asked
where Banks realized value from analytics.
We found that 37% of Outperformers realized value when they used
analytics to drive workforce planning and management. This was particularly interesting because the
Outperformers were 9(!) times more likely to realize value here than the
The other interesting result was one that we haven’t found
a complete explanation for (yet!). 65% of the Others vs 48% of the Outperformers realized value from analytics in regards to
risk management. This was a
counter-intuitive result, so there’s clearly something interesting going on
My current theory is that this result doesn’t mean that
these Outperformers aren’t engaged in risk management activities. To the contrary, it likely means that about half of them have
other systems in place that drive their risk management activities without relying significantly on Analytics. They
may make more use of policies, procedures, limits, and executive
oversight. Or perhaps their greater use of analytics to
engage with customers, suppliers, and business partners is effectively
providing indirect risk management.
Hopefully this has provided you with some interesting
insights into the Banking industry. As
always, please feel free to leave a comment or
send me an e-mail if you have
any questions. I’d be particularly
interested in any thoughts you might have on risk management in the Banking
See you next time!