Analytics Stories: A Case Study from Banco Popular
As I have shared within other post within this series, businesses are using analytics to improve their internal and external facing business processes and to strengthen their “right to win” within the markets that they operate. In banking, the right to win increasingly comes from improving two core sets of business capabilities—risk management and customer service.
Significant change has occurred in risk management over the last few years following the subprime crisis and the subsequent credit crunch. These environmental changes have put increased regulatory pressure upon banks around the world. Among other things, banks need to comply with measures aimed at limiting the overvaluation of real estate assets and at preventing money laundering. A key element of handling these is to ensuring that go forward business decisions are made consistently using the most accurate business data available. It seems clear that data consistency can determine the quality of business operations especially business risk.
At the same time as banks need to strengthen their business capabilities around operations, and in particular risk management, they also need to use better data to improve the loyalty of their existing customer base.
Banco Popular launches itself into the banking vanguard
Banco Popular is an early responder regarding the need for better banking data consistency. Its leadership created a Quality of Information Office (the Office uniquely is not based within IT but instead with the Office of the President) with the mandate of delivering on two business objectives:
- Ensuring compliance with governmental regulations occurs
- Improving customer satisfaction based on accurate and up-to-date information
Part of the second objective is aimed at ensuring that each of Banco Popular’s customers was offered the ideal products for their specific circumstances. This is interesting because by its nature it assists in obtainment of the first objective. To validate it achieves both mandates, the Office started by creating an “Information Quality Index”. The Index is created using many different types of data relating to each of the bank’s six million customers–including addresses, contact details, socioeconomic data, occupation data, and banking activity data. The index is expressed in percentage terms, which reflects the quality of the information collected for each individual customer. The overarching target set for the organization is a score of 90 percent—presently, the figure sits at 75 percent. There is room to grow and improve!
Current data management systems limit obtainment of its business goals
Unfortunately, the millions of records needed by the Quality Information Office are spread across different tables in the organization’s central computing system and must be combined into one information file for each customer to be useful to business users. The problem is that they had depended on third parties to manually pull and clean up this data. This approach with the above mandates proved too slow to be executed in timely fashion. This, in turn, has impacted the quality of their business capabilities for risk and customer service. According to Banco Popular, their approach did not create the index and other analyses “with the frequency that we wanted and examining the variables of interest to us,” explains Federico Solana, an analyst at the Banco Popular Quality of Information Office.
Creating the Quality Index was just too time consuming and costly. But not improving data delivery performance had a direct impact on decision making.
Automation proves key to better business processes
To speed up delivery of its Quality Index, Banco Popular determined it needed to automate it’s creation of great data—data which is trustworthy and timely. According to Tom Davenport, “you can’t be analytical without data and you can’t be really good at analytics without really good data”. (Analytics at Work, 2010, Harvard Business Press, Page 23). Banco Popular felt that automating the tasks of analyzing and comparing variables would increase the value of data at lower cost and ensuring a faster return on data.
In addition to fixing the Quality Index, Banco Popular needed to improve its business capabilities around risk and customer service automation. This aimed at improving the analysis of mortgages while reducing the cost of data, accelerating the return on data, and boosting business and IT productivity.
Everything, however, needed to start with the Quality Index. After the Quality Index was created for individuals, Banco Popular created a Quality of Information Index for Legal Entities and is planning to extend the return on data by creating indexes for Products and Activities. For the Quality Index related to legal entities, the bank included variables that aimed at preventing the consumption of capital as well as other variables used to calculate the probability of underpayments and Basel models. Variables are classified as essential, required, and desirable. This evaluation of data quality allows for the subsequent definition of new policies and initiatives for transactions, the network of branches, and internal processes, among other aspects. In addition, the bank is also working on the in-depth analysis of quality variables for improving its critical business processes including mortgages.
Some Parting Remarks
In the end, Banco Popular has shown the way forward for analytics. In banking the measures of performance are often known, however, what is problematic is ensuring the consistency of decision making across braches and locations. By working first on data quality, Banco Popular ensured that the quality of data measures are consistent and therefore, it can now focus its attentions on improving underling business effectiveness and efficiency.
Analytics Stories: A Financial Services Case Study
Analytics Stories: A Healthcare Case Study
Who Owns Enterprise Analytics and Data?
Competing on Analytics: A Follow Up to Thomas H. Davenport’s Post in HBR
Thomas Davenport Book “Competing On Analytics”
Solution Brief: The Intelligent Data Platform
Author Twitter: @MylesSuer