Should you be banking on open source analytics?
Caroline Hermon, head of adoption of artificial intelligence and machine learning at SAS UK & Ireland | 08:59 Wednesday 3rd July 2019 | 12
Banks see open source as a hotbed of innovation — and a governance nightmare.
Do the rewards outweigh the risks? Open source software used to be treated almost as a joke in the financial services sector. If you wanted to build a new system, you bought tried and tested, enterprise-grade software from a large, reputable vendor. You didn’t gamble with your customers’ trust by adopting tools written by small groups of independent programmers. Especially with no formal support contracts and no guarantees that they would continue to be maintained in the future.
Fast forward to today, and the received wisdom seems to have been turned on its head. Why invest in expensive proprietary software when you can use an open source equivalent for free? Why wait months for the official release of a new feature when you can edit the source code and add it yourself? And why lock yourself into a vendor relationship when you can create your own version of the tool and control your own destiny?
Enthusiasm for open source software is especially prevalent in business domains where innovation is the top priority. Data science is probably the most notable example. In recent years, open source languages such as R and Python have built an increasingly dominant position in the spheres of artificial intelligence and machine learning.
As a result, open source is now firmly on the agenda for decision makers at the world’s leading financial institutions. The thinking is that to drive digital transformation, their businesses need real-time insight. To gain that insight, they need AI. And to deliver AI, they need to be able to harness open source tools.
The open source trend encompasses more than just the IT department. It’s spreading to the front office, too. At SAS, we’ve seen numerous examples of initiatives across banking domains from risk management to customer intelligence. For example, we’re seeing many of our clients building their models in R rather than using traditional proprietary languages.
A fool’s paradise?
However, despite its current popularity, the open source software model is not a panacea. Banks should still have legitimate concerns about support, governance and traceability.
The code of an open source project may be available for anyone to review. But tracing the complex web of dependencies between packages can quickly become extremely complex. This poses significant risks for any financial institution that wants to build on open source software.
Essentially, if you build a credit-risk model or a customer analytics application that depends on an open source package, your systems also depend on all the dependencies of that package. Each of those dependencies may be maintained by a different individual or group of developers. If they make changes to their package — and those changes introduce a bug or break compatibility with a package further up the dependency tree or include malicious code — there could be an impact on the functionality or integrity of your model or application.
As a result, when a bank opts for an open source approach, it either needs to put trust in a lot of people or spend a lot of time reviewing, testing and auditing changes in each package before it puts any new code into production. This can be a very significant trade-off compared with the safety of a well-tested enterprise solution from a trusted vendor. Especially because banking is a highly regulated industry, and the penalties for running insecure or non-compliant systems in production are significant.
What use is power without control?
When it comes to enterprise-scale deployment, open source analytics software also often poses governance problems of a different kind for banks.
Open source projects are typically tightly focused on solving a specific set of problems. Each project is a powerful tool designed for a specific purpose: manipulating and refining large data sets, visualising data, designing machine-learning models, running distributed calculations on a cluster of servers, and so on.
This ‘do one thing well’ philosophy aids rapid development and innovation. But it also puts the responsibility on the end user — in this case, the bank — to integrate different tools into a controlled, secure and transparent workflow.
As a result, unless banks are prepared to invest in building a robust end-to-end data science platform from the ground up, they can easily end up with a tangled string of cobbled-together tools, with manual processes filling the gaps.
This quickly becomes a nightmare when banks try to move models into production because it is almost impossible to provide the levels of traceability and auditability that regulators expect.
Language doesn’t matter
The good news is that there’s a way for banks to benefit from the key advantages of open source analytics software — its flexibility and rapid innovation — without exposing themselves to unnecessary governance-related risks.
The language a bank’s data scientists choose to write their code in shouldn’t matter. By making a clean logical separation between model design and production deployment, banks can exploit all the benefits of the latest AI tools and frameworks. At the same time, they can keep their business-critical systems under tight control.
SAS plus open source
One SAS client, a large financial services provider in the UK, recently took this exact approach. The client uses open source languages to develop machine-learning models for more accurate pricing. Then it uses the SAS platform to train and deploy models into full-scale production. As a result, model training times dropped from over an hour to just two-and-a-half minutes. And the company now has a complete audit trail for model deployment and governance. Crucially, the ability to innovate by moving from traditional regression models to a more accurate machine learning-based approach can deliver significant financial benefits.
Temenos partners with ClearBank for cloud payments
Banking software company Temenos has formed a strategic relationship with ClearBank to provide banks with a faster route to market for real-time cloud payments...
Unity Trust Bank registers 34% rise in profits
Unity Trust Bank increased profits by 34% in 2019...
Believe the hype – why explainable AI is a trend that’s here to stay
Technology has become a ubiquitous part of our day-to-day lives...
Piloting tech updates: ‘The bigger the bank, the harder it is to get anything done’
In the latest Medianett filmed roundtable session, we discussed how important technology is in the banking space, and what impact the industry expects it to have on its businesses in the future...
What banks need to know about cloud security
One of the most common perceived concerns when adopting the cloud is the issue of security...
OakNorth sees 95% increase in pre-tax profits
OakNorth Bank has announced a 95% rise in pre-tax profits in 2019 to £65.9m, up from the £33.9m recorded in 2018...
Redwood Bank signs up to Women in Finance Charter
Redwood Bank has announced that it has signed up to the Women in Finance (WIF) Charter...
Masthaven launches digital Women in Leadership programme
Masthaven Bank has launched a new Women in Leadership digital development programme for female senior leaders...
Protecting against supply chain disruption and the domino effect
Disappointingly, many UK SME business owners don’t understand their supply chains...
Confused about which Isa to choose? Hopefully this mini-guide will help…
We are now firmly in Isa season, so you’re likely to read multiple articles about the most competitive Isa products in the market and how best to make the most of your Isa allowance before the end of the tax year...
Garden shed entrepreneurs contribute £16.6bn to the UK economy
Entrepreneurs who run their businesses from garden sheds contribute £16.6bn annually to the UK economy, according to a recent study...