The banking industry is one of the most conservative sectors in Canada for good reason: banks must protect customers’ investments and meet increasingly rigorous national and international laws. But with the onset of FinTech and related technologies, the country’s largest banks have been taking a serious look at innovations that harness the power of big data and analytics to thrive in the new digital economy.
According to its website, the insurance branch of the country’s largest bank, Royal Bank of Canada, used predictive analytics tools to improve outcomes of customers’ claims for disability support from Canada and Quebec Pension Plans. As a result, the number of clients who received CPP/QPP disability payments increased by two per cent—more than $425,000 in additional monthly benefit payments to customers.
Like many other industries, Canada’s banks have been gleaning meaningful insights from volumes of data to make effective decisions and offer what customers are looking for. This can increase the trust they’ve developed with clients over the years and allow them to stay competitive and sustainable down the road. For example, in 2014, Canadian Imperial Bank of Commerce (CIBC) tapped into years of customer data across its credit card portfolio and launched a travel rewards program that tailored service and product offerings for clients and enhanced their experience of personalized banking.
How do developments at banks compare to those in a larger corporate landscape? A recent Smith School of Business survey of 250 senior leaders in Canada reveals that executives are now relying more on data analytics to guide strategic business moves. Of those surveyed, 56% report that decisions informed by data analytics, as opposed to experience and intuition alone, tend to deliver better results. They use more data and analytics for strategic decision making than in the past and emphasize intuition for crisis management and human-factor decisions involving their teams.
Challenges and risks in using data analytics
Like any other new technologies, increased use of data and analytics also comes with challenges and risks. One of the key risks in collecting and analyzing increased volumes of data for analytics purposes is security breach. This risk pre-dates the growth of analytics; however, analytics applications often require higher-value integrated data; for example, data that combines customer transaction and demographic information. The bigger the data, the more prone it is to being the target of hackers who steal customer information, particularly from banking and retail firms.
In Canada, the number of reported data breaches reached 20,456 in 2015, costing each breached company an average of $5.32 million according to the “Cost of Data Breach in Canada 2015” study released last year by Ponemon Institute. The study, which looked at 21 Canadian companies from 11 different sectors, indicates that data breaches cost an average of $250 per compromised record. Malicious or criminal attacks are the usual culprits of data breaches, but employee negligence, system glitches and human error contribute to the problem too.
Aside from data theft, managing volumes of information is also a key challenge. Storing and organizing massive amounts of data has long been a challenge for information systems professionals, but integrating data from multiple sources in the ways required by advanced analytics adds another level of difficulty, as does ensuring that the data remains updated and accurate. It’s no wonder that the Smith School’s executive survey revealed that there is still some hesitation among Canada’s organizational leaders to fully embrace data analytics. Executives report limited availability of accurate or reliable data, lack of tools for data collection and analysis and shortages of expert analytics staff.
Given these risks, what can banks and their customers do?
Bank clients are perennial targets of malware attacks to steal passwords and banks are prime targets for hackers seeking access to their databases
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. Banks must actively monitor their networks and continuously upgrade their ability to ward off online threats to avoid disruption of customer service.
On the customer-relations front, the risks posed by security breaches and the complexity of big data management can lead to customer backlash. As customers become more aware of the data that firms are collecting, they can become increasingly wary of sharing information and may demand more details of the uses of their data. To keep customers engaged, companies should be transparent on how they handle client information and should show tangible improvements in customer experiences as a result of collating more client data.
For modern managers, striking an appropriate balance between analytics and intuition is a key aspect of success in decisions, particularly those relating to customers and employees. It is not either/or with these two approaches to decisions. There are some lower-level decisions that may be effectively handled entirely with analytics—inventory control and small-item forecasting, for example; however, formation of effective business strategies, even with good data available, needs intuition and experience. To be effective, and used, analytics solutions must have the capability to combine objective data with subjective data from managers to produce nuanced results. Analytics tools should be viewed as decision-making aids, not replacements. Managers can use additional data to test their assumptions and recognize new patterns but should never be forced to ignore their intuition.