The diversity of financial products offered in today’s marketplace is massive. However, there are common analytics challenges faced by any line of business, no matter what the product. I have strung together three parts of that challenge that are common across all products:
Answering the simple question of “how are we doing” proves not so simple in this industry. Price & volume are certainly the most common areas of focus because the information is more easily accessible. The mature sales & marketing teams know that decisions made without taking into account the relationship between the balance sheet and income statement are made on an incomplete and elementary understanding of the business. This relationship is captured through principles such as:
- Funds transfer pricing
- Non-interest income
- Estimated losses
- Activity-based cost allocation
- Economic (and regulatory) capital allocation
One example of looking back at the historical performance of a business unit can be seen in this demo application:
Strategic planning, modeling, stress testing and pricing are all activities focused on providing visibility into future financial results of the business. Financial services models are primarily based on the looking back concepts described in the previous section. Success (or accurately predicting the financial results) is rooted in consistency, comparability and transparency of both the assumptions and of the models’ results.
Below is a screenshot of a QlikView portfolio model created in SAS, with data stored in Teradata and delivered to leaders of a banking organization. “Looking forward” in this example not only includes visualizing the relationship of the income statement and balance sheet for a particular forecast, it also includes the important concept of comparing different forecasts and understanding the impact of changing underlying assumptions.
A full review of this application is provided in the following webinar.
And everything in between
“Everything in-between” may range from fundamental analysis of comparing actuals to forecast, to complex analysis of revenue optimization models. The only constant is that the question is different every day. The line of business must be able to react appropriately to the ever-changing business environment and the technology they use for analytics will set them apart.
Qlik recently held a Global Financial Services customer event in London and I was tasked with presenting something “interesting” to a group of Qlik customers who already know the difference between our associative platform and traditional query based tools. My presentation has been summarized in this post on Qlik Community and in this YouTube video:
In the Financial Services industry, it is a valuable set of analytics that combines several of these tools with your internal data and models to improve consistency, comparability and transparency of financial results. It becomes even more valuable though, when you Unleash Your Army of Analysts.
We researched some reports from IDC recently and this particular paper Big Data and Analytics in Financial Services caught my eye for two reasons:
- It provides a roadmap for progressing through your analytics journey that is well defined and representative of many of our customers’ journeys
- Provides four really good examples of how financial services firms utilize big data and analytics to “increase revenue, lower operating costs and keep their firms in compliance with regulations”
Take a look and let us know what you think!
Source: IDC, IDC MaturityScape: Big Data and Analytics in Financial Services, Bill Fearnley, December 2015.