Tim Oldfield, Financial Services Client Director at Mango Solutions (an Ascent company)
The highly regulated financial services sector has traditionally relied on tried and tested proprietary and closed source software solutions for a variety of reasons. For the most part, this reliance on large-scale engineering teams dedicated to building software from scratch was founded on a desire to protect competitive IP and address compliance and security concerns.
In recent years, however, a number of key drivers have led the sector to rethink its position on open source technologies, which as a movement has reached a certain level of maturity. The acquisitions of open source organisations by established technology vendors over the last few years – Microsoft bought GitHub for $7.5 billion in 2018, and IBM acquired Red Hat for $34 billion in 2019 – is the best evidence of this. In the last decade, the rapid pace of digital transformation has seen new challengers disrupt the market with a raft of innovative digital banking services. In response, established financial services players have had to move fast, or risk losing out to ambitious new competitors.
Seizing the opportunity
The bottom line is that in recent years, financial services firms have begun turning to open technologies that make it possible to innovate faster and at a lower cost. Using these solutions, they’ve been able to initiate new services like fraud detection, automated loan approvals, chat bots and automated trading that resonate with today’s customers. A good example is US bank Capital One, which invested in open source to innovate at scale, developing an ongoing detection model to mitigate the remediation cycle and quality as a way to avoid code vulnerabilities and security risks.
Now, a new generation of open source technologies are transforming how financial services firms leverage data for business intelligence and undertake tasks such as monitoring and real-time analytics.
Banking on open source for real-time insights
In the field of data science, open source programming languages like R and Python have been dominating advancements in the sphere of AI and machine learning that deliver the real-time insights businesses need to handle everything, from risk management to harnessing the customer intelligence, making it possible to deliver data-fuelled and hyper-personalised experiences in real time.
However, despite the fact that leaders in the finance sector acknowledge that leveraging open source is the way of the future, it won’t be a process that happens overnight. As with any new technology or methodology, embracing open source needs a cultural shift throughout the organisation, a large part of which is steeped in overcoming perceptions around the risks historically associated with using open source.
With regard to the latter, fortunately many of the latest generation of analytics solutions now deliver secure and compliant access to validated versions of R. All of which makes it easy for users, IT and quality teams to be confident that data scientists can exploit the power of popular analytic languages like R to unlock a standardised analytics environment for global teams without risk.
Featuring an end-to-end validation process that mitigates the risk of adopting and managing open-source software that can save vital time and resources across the organisation, today’s solutions make it possible to initiate a repeatable and consistent analytical environment that delivers dependable results.
In an era when speed, innovation and analytics in real time all combine to generate competitive advantage, leveraging solutions that offer open source options in a validated way is helping financial services firms to safely and effectively productionise open source in a way that’s exactly configured for their needs.
Innovating with confidence
Today’s financial services firms are now able to take advantage of the flexibility and rapid innovation capabilities offered by open source analytics software, with none of the governance-related risks that previously threatened to hamper their forays into this field. This opens the door to deploying the latest AI tools and machine learning approaches to undertake predictive modelling and statistical analysis, and extract mission-critical information from thousands of data sources, and more.
Furthermore, today’s graduates are well versed in the usage of programming languages like R for data science and big data analysis and visualisation. In recent years, tools like R have gained tremendous traction for data science projects. Which means the sector can now draw on a rich pool of talent that has a good working knowledge of R programming.
Looking to the future
In recent years the financial services sector has undergone a significant change in cultural outlook when it comes to consuming open source toolsets. Indeed, by building on best-in-class open source technologies banks have been able to make new regulatory frameworks like PSD2 a workable reality.
Now banks are considering how they can utilise open source to supercharge their intelligence capabilities and fuel future data-driven transformation. The benefits of open source are compelling; powerful, low cost and reusable common platforms and procedures that can be deployed across multiple projects. But financial services firms that want to speed up how they ingest and derive new insights from their data need to be confident that the open source business analytics tools they build, and use, deliver a high degree of assurance, consistency, and efficiency.
Fortunately, today’s open source solutions make it easy to consume validated R packages and deliver ready-made access to the tools that will help them leverage data in truly innovative ways.