Banking on Data – Opportunities, Challenges, Trends


“Data is the new oil” is a phrase frequently heard and debated at conference panel discussions these days. Today, as most of our interactions with the products and services around us are digital, a plethora of data is being captured about us, our preferences, and our consumption and spending patterns. And given that a good proportion of it involves financial services, it is no surprise that there is an ever-increasing need in financial services to have a broader and deeper understanding of data about customers. While gathering data is perhaps the easier activity, deriving meaningful insights and information from it is a challenge, especially given the complexity of the products, systems and patterns that could emerge out of this data. There are issues to be dealt with in terms of regulation — general data protection regulation (GDPR) being the latest hot topic of discussion, privacy concerns and data quality issues (legacy systems anyone?). But how exactly are banks making use of this newly found (or rather renewed) love of data?

This article takes a sneak peek into some aspects of how banks are leveraging data, going about building a data strategy and how it is redefining customer relationships itself. The article is a preamble to some of the webinars, speaker sessions and activities we aim to do under the ISB Analytics SIG.

The world produces 2.5 quintillion bytes of data a day, and 90% of all data has been produced in just the last two years. According to the European Commission, by 2020 the value of personalised data will be 1 trillion euros, almost 8% of the EU’s gross domestic product (GDP). As this trend grows, so too will the conflict between the value of data and individual privacy and consent. Who owns the data and has intellectual property rights on it? The global data economy is pegged at US$ 3 trillion and consumers also need to reap the benefits of their self-generated data. It is no wonder that the challenges, opportunities and risks are huge.


There is an interesting chart that shows the amount of time spent by users on the major wallets in the country (Figure 1). Although it is slightly dated (end of 2015), it illustrates the potential in terms of the data that is available and can be captured via customer interactions. With approximately 230 million registered Paytm wallet users and an active user base of 54 million at an average of 70 minutes per month, that is a staggering 3.7 billion minutes per month of customer interactions available to Paytm alone. Combine this with all of the other digital channels, mobile banking apps and more, and there’s practically an infinite amount of data being generated.

Source: Nielsen-Reports

Figure 2 shows the complex maze of regulatory compliances that banks have to adhere to in 2018. Banks will have to respond to this new wave of regulatory changes with enhanced enterprise risk management systems and processes to effectively manage risk and comply with the requirements. They must continue transforming their risk management, finance and compliance technologies, processes and practices in terms of capital calculation, expected loss estimation, data management, stress testing and reporting. Not a single one of these compliances can be carried out without having a strong understanding and control over the data that banks generate, maintain and use. Therefore, the key to managing all these compliances is a strong data strategy.
Figure 2: Global Banking Radar
Source: Moody’s Analytics. Global Banking Regulatory Radar.

Data Analytics to Decision Analytics:
As far as banks are concerned, the power of data also lies in being able to make smart decisions. These decisions need not necessarily be macro decisions but can be micro decisions. Here are some guiding principles (Garg et al, 2017) and examples that show how banks are using the power of data to make smart decisions.


This involves asking questions around who are the most valuable customers, how good are the efforts of my sales team, etc., building customer profiles by demographics, geographical segmentation at micro levels, and market events impacting investment banking decisions. Great analytics starts with high-value questions, not data. To guide the discovery process, ask what problem you want to solve and how much value the solution can create.


The smallest edge can make the biggest difference. Advanced analytics is not about solving your biggest problems, it is about solving hundreds of small ones that all add up. In operations especially, these techniques can help to redefine processes and shorten them by several steps. Examples of these are identifying opportunities to make investment or retail banking operational processes more speedy (i.e., straight through processing (STP)) and establishing feedback loops back to the businesses.

Today’s competitive edges are established through the ecosystems that exist around existing customer relationships — direct or indirect. These intersection points can be used to establish new and emerging ecosystems within the customer segments that the banks serve. To identify these ecosystems, make use of insights which live at the boundaries between data sets. Many relationships become apparent only when widely varying parameters are compared. Banks have massive amounts of data scattered through different departments. Pilots to bring together small samples of information can reveal their potential.

Analytics alone isn’t enough; adoption is essential. Use whatever means necessary — incentives, role modelling, communication, more communication — to get decision makers to use the new tools. Way too often, best-in-class algorithms sit idle in computers because users do not trust what they regard as a black box, fear the impact it could have on their roles, or simply do not want to go through the discomfort of change. Creating analytics is like putting jet fuel in your car. At the end of the day, if the driver does not develop the skills needed to drive faster, the effort is wasted.

Associated risks and controls:
In summary all of data strategy has to be complimented by a very strong data security standards, guidelines, controls. Banks carry huge amounts of personal data around customers and any breach of this data not only means breach of trust as far as banks are concerned but could also result in huge financial losses for the customers. There are several scrupulous characters around who are waiting to tap into the smallest
loophole left behind by Banks’ controls and security systems through various means – phishing, vishing etc. In order to prevent these kind of attacks, banks have to be several steps ahead on building the necessary hardware, software infrastructure and controls. That in itself a huge topic of discussion, research and application.


Boden, R. (2016, March 14). Nielsen reports on mobile wallet market in India. NFC World Knowledge Centre. Retrieved from

Garg, A., Grande, D., Miranda, G. M-L., Sporleder, C., & Windhagen, E. (2017, April). Analytics in banking: Time to realize the value. McKinsey and Company. Retrieved from

Moody’s Analytics. Global Banking Regulatory Radar. Retrieved from

Thirani, V., & Gupta, A. (2017, September 22). The value of data. World Economic Forum. Retrieved from

About the author:
This article has been written by Himanshu Warudkar, 
alumnus from PGPMAX Co '15, Moderator of the Business Analytics Special Interest Group(SIG) & Director – NatWest Markets Technology, Royal Bank of Scotland