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R&D in the Banking Sector: Making the case for Innovation Data Labs

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By Muraleedhar Ramapai, Executive Director of Data, Maveric Systems

Ranked by industry percentages, what position do you think the banking industry occupies in global research and development spending?

Computing & Electronics, Healthcare, Auto, and Software & Internet are the top four (22%, 21%, 16% and 15% respectively). Banking does not occupy the fifth (Industrials), sixth (Chemicals and Energy) or even seventh (Consumer) spot.

In fact, R&D in the banking industry is so low, it is dumped as “others” at 1.9%.

To cut a long story short: Banks have a long way to grow their R&D initiatives, especially, in today’s shifting landscape of increasing regulatory pressures, rising customer expectations, innovative technologies, and nimbler challengers regularly combining to disrupt the financial services sector.

Let us next examine in some detail, a variable that admittedly has had the largest impact on a BFSI organization’s R&D capabilities, namely, Open Source Technology

The Evolutionary Impetus
Our appetite for far-reaching technology changes is matched (and fuelled) with the incredible leverage open source technologies bring across industries.

(Think of the ‘Unicorns’ formed around Open Source Technologies: Red Hat, MuleSoft, Databricks, Elastic NV, Confluent, Hashicorp)

In fact, Fintech Open Source Foundation (FINOS), in its 2018 white paper, makes the central argument succinctly, “The question is not whether to use open source but how to do it more strategically, efficiently, and extensively than your competitors. With digital disruption handled collectively by technology solutions that become de facto industry standards, financial services companies become defined not by their software, but by execution and differentiation in customer service

Traditionally large banks have protected all technology as Intellectual property and the driver of competitive advantage with large scale engineering teams building all software from scratch. In the last decade this has rapidly changed.

Open source technology (and other turnkey solutions) has made serious inroads in financial services.

Back end technologies: Servers supporting the massive compute landscape, data storage and processing, and trading infrastructure – essentially all back-end software capabilities – largely run on open source Linux platforms

Engineering layer: Financial services software development has been commoditized with the large-scale use of open source for network communications, database storage, workflow management, web application development and much more

“As a Service” offerings: While not necessarily open source, these range from infrastructure and compute power to software and entire platform offerings that are greatly reduce the customization and integration effort required by banks

Regulator-mandated openness & standardization: Regulators have started mandating the industry to open up, standardize and become more transparent, with Europe leading the drive with PSD2 and Open Banking in the UK.

Consequently, plugging in open source solutions means Banks today can free up precious resources to focus on integration and more importantly, concentrate on building their unique business value.

As the BFSI industry turns its attention to Fintechs to meet their digitization challenges; the eventual target areas for their R&D efforts does not waver: Develop and Deploy new technologies to better serve B2B banking customers, Increase profits, improve compliance and security preparedness and reduce infrastructure costs.

If the end-goals are similar, then where does the Banking R&D differentiation come from?

In one word: Reliability

To infuse reliability as a core rubric in its R&D paradigm means Banks have to check a number of boxes.

Firstly, Banks and Technology teams need to bedrock ‘reliability-as-a- yardstick’ in their partnerships; across vendors, across geographies, across platforms.

Secondly, Reliability is built over time by adopting a divergent approach.

The ‘traditional-hire-and-instruct-engineers-on-a-project-mode’, does not produce optimum test results because to harness advanced technologies necessitates an experimental mindset as opposed to the erstwhile engineering approach.

Finally, reliability comes at a cost.

To experiment with production in real time comes with a sizable expense – One, the cost of errors can be high (especially when teams start implementing on a project to realise the underlying thinking is incorrect and it has to begin anew), and Two, the multifarious skill base that runs these R&D experiments is rare to come by (it rather needs to be cherry picked and teethed on multiple R&D propositions); both actions require investments.

Unsurprisingly then barring a few major banks, in-house ‘sandbox environments’ have largely been the domain of a few ivy-league academia and an elitist start-up/incubator ecosystem, either options hardly conducive to support projects of large scale or variable scopes.

Banks today primarily focus on banking processes and not on creating horizontal pieces of technology. Rather than hiring technologists, they are beginning to partner with technology companies that bring the wherewithal to deliver on their high-value R&D outcomes.

Successful Innovation labs (data or digital) tightrope between real-life opportunities today with the possibilities of tomorrow; between applied technologies and blue-sky thinking.

The operative words here are “balance” and “focus”.

Guided by a set of concrete business benefits, banks seek Innovation labs; who given a specific problem statement pare it down to related experiments. Thereafter the labs set up, run and package Proof of Concepts (PoC). Then in quick turnaround, they come back with appropriate choices of solution architecture, and recommendations for underlying technologies that either addresses the specific business challenge or seizes the market place’ quantum opportunities.

This is the ‘playpen-mindset’: Flexible in approach but committed to the outcome; Balanced and Focussed.

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