This is a two-part series on vertical business intelligence (BI) trends
- Trends and tips for using business intelligence in financial services
After the various banking, mortgage and Wall Street meltdowns that have tilted the foundations of many financial services sectors, it might be easy to assume that most of the business intelligence (BI) activity in the financial industry these days is focused on risk management. In a weak economy, risk can easily suck up attention.
While many financial services firms are indeed investing heavily in risk management, governance and regulatory compliance, the most forward-looking businesses seem to be maintaining their loftiest goals: uniting fractured operating units to provide customer-focused clarity.
"Financial services companies are continuing to heavily invest in customer data integration, particularly through their subsidiaries, product lines and service lines, which is an ongoing need," said Rick Sherman, founder of Athena IT Solutions, a BI and data warehousing consulting firm.
For banks, it is critical to juggle risk management with the kind of smart intelligence that they need to grow and be successful.
"We certainly have an emphasis on risk management; however, focusing on customer profitability, particularly in today's environment, we think is very important," noted Basil Blume, executive vice president and chief analytics officer at Colorado Capital Bank.
For example, financial institutions often provide services for some customers where the relationship is actually unprofitable. Think free checking for high-activity customers with low balances and no other accounts, loans or services.
"In fact, in many cases, the majority of a bank's customers may not provide a positive contribution margin – the bank's entire profit comes from the 20% or 30% of customers who are profitable," Blume explained. "With our BI solution, we certainly want to know who our profitable customers are and let them know how much we appreciate their business."
Blume also said his company can use BI to identify unprofitable customers and find opportunities to enhance those relationships. "For every unprofitable customer, the elimination of the loss greatly improves the bottom line," he said.
The challenge, of course, is that the core makeup of many financial services-based organizations is built on distinct product lines. In the case of banks, it could be checking and related accounts, credit cards, lines of credit, home loans, investment services and even insurance – all of which fall under the same bank brand. While a consumer sees a coherent picture, the back-end world of a bank might be anything but. Different product lines are often run on a complicated labyrinth of heterogeneous IT systems, which can make it difficult to identify and discern how a customer – or household – affects the bottom line for the overall financial organization.
To make matters more difficult, when it comes to BI, many financial services companies invested in tools for different product lines at different times. And the bigger the company, the more likely it is that it is running entirely different BI products. "The good news is that early adopters are getting more value, and the bad news is that they've got additional systems integration costs as they move forward," Sherman said.
Some companies, particularly in the insurance sector, have tried to create integration through complicated service-oriented architecture schemes. But, according to Sherman, the integration point for today's BI is the data warehouse.
And along with the data warehouse comes the plan to manage it.
"One key trend we are certainly seeing is the focus on a central data steward organization with accountability and metrics to improve the data governance, security, structure, architecture and master and meta-data management policies, standards, roles and responsibilities, and technologies across the enterprise," said Omer Sohail, global market development lead for financial services at Accenture Information Management Services.
Peter Redshaw, a vice president of research in banking and investment services at Gartner, is seeing similar action. In some cases, for example, banks are holding off on major application changes in anticipation of new industry regulations, particularly for global banks that may need to address regulations from multiple countries. Meanwhile, many banks are investing in data and business process management, according to Redshaw.
"Banks are starting to plan for when we come out of the recession," he said. "They're asking themselves, 'What kind of a bank will we need to be?'"
Most banks don't expect to pick up where they left off in 2006 or 2007, he added.
"It will still be a world of relatively slow growth, high volatility, small margins and heavy regulations," Redshaw said. "So banks are going to need a different operating model, and I think they see BI as one of the key investments that will enable them to manage a more unpredictable world."
Seven tips for BI in financial services
Pull instead of push. Old-school financial services BI was often geared to identify related products that customers might add, sparking a flurry of direct mail and phone solicitations.
"Many banks are still pushing products onto customers. Instead, they need to reorient themselves to pull customers in through a greater use of business intelligence and more robust analytics," Sohail said. "However, in order to do so, banks will need an integrated and intelligent view of their customers that enables them to quickly identify their needs and match products and services accordingly."
Identify features for flexibility. Instead of pushing products, can better intelligence identify features or services that can be tweaked to snag more business and still be profitable?
"These days," Redshaw said, "customers expect [their experience to be] more of a two-way push-pull model where customers say, 'Actually, this is what I want. I don't care what you have pre-built; I want to negotiate some of the features of this product, and these are the terms and conditions I'm looking for, the price I'm willing to pay.'"
Understand your data. "In the financial services world, customer data is the key within your product line and across the enterprise," Sherman said. "No matter what tools you buy, in the long run, you're going to have to understand what your customer is and get that into a single place to create one common understanding of what they are."
Get centralized data governance. While it may be impossible to consolidate disparate line-of-business systems and data stores into a more centralized data warehouse, financial services organizations can still benefit from centralized data stewardship.
"Most banks are moving towards a hybrid model with a central governing organization and data stewards in lines of businesses to actually implement and adhere to the common standards," Sohail explained.
The risk of not moving in that direction? "Failure to do this would result in a lack of standards and compliance and a non-cohesive information management strategy, leading to an inability to achieve top-line initiatives that depend heavily on customer, product and financial data quality," Sohail said.
Remember: BI is a business problem. It's easy to get caught up in technology – faster systems, better tools, databases, multi-dimensional cubes – and lose sight of the fact that IT can't roll out an effective BI system on its own.
"The biggest common mistake is assuming that BI is a technology problem. It's not. It's a business – governance, performance management, organizational structure, processes, etc. – problem. Business has to own BI," said Boris Evelson, a principal analyst at Forrester Research.
Specific tools are less important. "I think most tools have matured to the point [where] – and vendors don't like to hear this – you're not going to make or break your app by picking a particular tool," Sherman said.
"BI has been around for a while and the basic stuff everyone has copied from each other," he continued. "And the more emerging stuff, the predictive analysis, is more in your business rules and algorithms and transformation of your data than it is in the tool itself. Vendors aren't going to be able to code a deep understanding of your business – they can't automate that – but they are all much better than they were even two years ago. You can do far more with the tools now."
Mix education with information gathering in an iterative process. "I think many companies spend an inordinate amount of time trying to allocate costs and determine customer profitability at the net income level," Blume said. It doesn't have to be so hard. To streamline a customer profitability calculation and put it to work, for example, he recommends that organizations start with a small group to create a definition of customer profitability.
Next, you should develop a draft methodology for calculating profitability and compute some specific examples using existing customers. Deliver the draft to a few small groups within the organization, incorporate feedback and make revisions quickly.
For the broader rollout, provide education on how you define profitability and the way it's computed – with examples. Train your staff on how to use the information to make more profitable customer decisions. And, finally, solicit feedback to help improve the calculation methodology and its usage.
Chris Maxcer is a freelance writer.
This was first published in October 2009