A Digital Bank is a Data Centric Bank..

“There’s no better way to help a customer than to be there for them in the moments that matter.” — Lucinda Barlow, Google

The Banking industry produces the most data of any vertical out there with well defined & long standing business processes that have stood the test of time. Banks possess rich troves of data that pertain to customer transactions & demographic information. However, it is not enough for Bank IT to just possess the data. They must be able to drive change through legacy thinking and infrastructures as things change around the entire industry not just from a risk & compliance standpoint.

For instance a major new segment are the millennial customers – who increasingly use mobile devices and demand more contextual services as well as a seamless unified banking experience – akin to what they commonly experience via the internet – at web properties like Facebook, Amazon, Uber, Google or Yahoo etc.

The Data Centric Bank –

Banks, wealth managers, stock exchanges and investment banks are companies run on data—data on deposits, payments, balances, investments, interactions and third-party data quantifying risk of theft or fraud. Modern data applications for banking data scientists may be built internally or purchased “off the shelf” from third parties. These new applications are powerful and fast enough to detect previously invisible patterns in massive volumes of real-time data. They also enable banks to proactively identify risks with models based on petabytes of historical data. These modern data science applications comb through the “haystacks” of data to identify subtle “needles” of fraud or risk not easy to find with manual inspection.

The Bank of the future looks somewhat like the below –

                                            Illustration – The Data Driven Bank

How do Banks stay relevant in this race and how is the Digital Journey to be accomplished?

I posit that there are five essential steps –

1. Build for the organization of the future by inculcating innovation into the cultural DNA. A good chunk of the FinTech’s success is owed to a contrarian mindset in terms of creating business platforms using technology & generating a huge competitive advantage. The secret is following a strategy of continuous improvements. This is done by generating new ideas, being unafraid to cannibalize older (and even profitable) ideas and constant experimenting across new businesses. For instance Facebook is famous for not having a review board that designers and engineers go present to with PowerPoint slides. Here prototypes and pilot projects are directly presented to executives – even to CEO Mark Zuckerberg. Facebook pivoted in a couple of years from weak mobile offerings to becoming the #1 mobile app company (more users access their FB pages using mobile devices running iOS & Android compared to using laptops).
2. Leverage Predictive Analytics across all data sets – A large part of the answer is to take an industrial approach to predictive analytics.  The current approach as in vogue – to treat these as one-off, tactical project investments does not simply work or scale anymore.  There are various organizational models that one could employ from the standpoint of developing analytical maturity. These ranging from a shared service to a line of business led approach. An approach that I have seen work very well is to build a Center of Excellence (COE) to create contextual capabilities, best practices and rollout strategies across the larger organization. These modern data applications make Big Data and data science ubiquitous. Rather than back-shelf tools for the occasional suspicious transaction or period of market volatility, these applications can help financial firms incorporate data into every decision they make. They can automate data mining and predictive modeling for daily use, weaving advanced statistical analysis, machine learning, and artificial intelligence into the bank’s day-to-day operations.
3. Drive Automation across lines of business  – Financial services are fertile ground for business process automation, since most banks across their various lines of business are simply a collection of core and differentiated processes. Examples are consumer banking (with processes including on boarding customers, collecting deposits, conducting business via multiple channels, and compliance with regulatory mandates such as KYC and AML); investment banking (including straight-through-processing, trading platforms, prime brokerage, and compliance with regulation); payment services; and wealth management (including modeling model portfolio positions and providing complete transparency across the end-to-end life cycle). The key takeaway is that driving automation can result not just in better business visibility and accountability on behalf of various actors. It can also drive revenue and contribute significantly to the bottom line.It enables enterprise business and IT users to document, simulate, manage, automate and monitor business processes and policies. It is designed to empower business and IT users to collaborate more effectively, so business applications can be changed more easily and quickly.
4. Adopt Open Source  -Open Source while being some what of an unknown challenge  to the mass middle market enterprise represents also a tremendous opportunity at most Banks & FinTechs across the spectrum of Financial Services. As one examines business imperatives & use-cases across the seven key segments (Retail & Consumer banking, Wealth management, Capital Markets,Insurance, Credit Cards & Payment processing, Stock Exchanges and Consumer Lending) it is clear that SMAC (Social, Mobile, Analytics, Cloud and Data) stacks can not just satisfy existing use-cases in terms of cost & satisfying business requirements across a spectrum but also help adopters build out Blue Oceans (i.e new markets). Segments of open source include the Linux OS, Open Source Middleware, Databases and Big Data ecosystem. Technologies like these have disrupted proprietary closed source products ranging from popular UNIX variants, Application Platforms & EDWs, RDBMS’s etc.
5. Understand the Customer – Across Retail Banking, Wealth Management, Capital Markets, a unified view of the customer journey is at the heart of the bank’s ability to promote the right financial product, recommend a properly aligned portfolio products, keep up with evolving preferences as the customer relationship matures and accurately predict future revenue from a customer. But currently most retail, investment banks and corporate banks lack a comprehensive single view of their customers. Due to operational silos, each department has a limited view of the customer across multiple channels. These views are typically inconsistent, vary quite a bit and result in limited internal collaboration when servicing customer needs. Leveraging the ingestion and predictive capabilities of a Big Data based platform, banks can provide a user experience that rivals Facebook, Twitter or Google and provide a full picture of customer across all touch points.

Recommendations – 

Summary – 

I have found myself spending the vast majority of my career working with a range of marquee financial services, healthcare, business services & Telco clients. More often than not, a vast percentage of these strategic discussions have centered around business transformation, enterprise architecture and overall strategy around Open Source initiatives & technology.

Global Banking is at an inflexion point, there is now an emerging sense of urgency in mainstream Financial Services organizations to create and expand on their Digital Transformation strategy.In the last few years, more and more of the technology oriented discussions have been focused around Cloud Computing, DevOps, Mobility & Big Data.

The prongs of digital range from Middleware to BPM to Cloud Computing (IaaS/PaaS/SaaS) to the culture & DevOps practices.

The rise of Open Standards and Open APIs have been the catalyst in the digital disruption. Neglect them at your peril.