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HOW CAN CLOUD-BASED ANALYTICS HELP BANKS DRIVE DIGITAL TRANSFORMATION?

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By Paul Jones, Head of Technology, SAS UK & Ireland

 

Fintechs are turning up the heat in retail and corporate banking. As smaller, more agile providers have entered the banking market, customers are getting used to a higher level of service – a personalised, digital experience that guides them to make quicker, smarter decisions about their finances. For traditional banks to compete, they need to transform the way they operate. On the retail banking side, that means digitising customer-facing services. No queuing in branches, no paperwork. And when customers apply for a credit card or loan, they get a decision in seconds.

Meanwhile, on the corporate side, the aim of transformation is often to enable an everything as a service (XaaS) strategy, building smart packaged offerings such as treasury as a service or risk management as a service, which the bank can both consume in-house and provide to enterprise clients.

 

Data-driven digital transformation

To foster this type of digital business transformation, banks need to redesign both internal and customer-facing processes to embed data-driven decision making. By integrating intelligent automation and decisioning capabilities into their operations, banks can eliminate paperwork and manual processing. This will greatly improve service levels to customers while keeping the cost-to-serve to a minimum.

The creation of these data-driven services depends on the ability to design, build, test and deploy processes that embed predictive models using both well-established statistical methods and new artificial intelligence and machine learning (AI/ML) techniques. The development life cycle for these models is inherently experimental. It’s vital to try different approaches, test the results, and iterate on the candidates that offer the greatest potential. To remain relevant in the digital age, organisations must deliver such experiments with agility and speed.

 

The obstacle of legacy infrastructure

The problem is that banks’ traditional IT architectures – built around legacy on-premises systems – are a uniquely bad environment for developing these models. Due to the experimental nature of the models, it’s very difficult to forecast what type of infrastructure banks will need for upcoming projects. For example, different machine learning algorithms run best on hardware that has been optimised for that category of model building. If you invest in a cluster of servers with a particular configuration of memory and processors, it may only be suitable for a small subset of the work you actually need to do. And every time you need to change your approach, you’ll face high fixed costs and a long lead time to get the right infrastructure in place.

Instead, you need an IT architecture that allows you to set up experiments quickly and manage them flexibly. When an idea doesn’t work out, you should have the ability to fail fast and cut your losses. And when an idea succeeds, you need to get it into production rapidly and roll it out for enterprise-scale deployment.

 

The promise of cloud-based analytics

The cloud is the perfect environment for these exploratory projects. It gives you the freedom to spin up almost any type of infrastructure within minutes, and either scale it or shut it down instantly depending on the results.

Cloud environments also free you from dependencies on departmental silos and the quirks of your internal network. They give you a green-field site where cross-functional teams can collaborate freely, enabling you to build models that combine domain knowledge from different areas of the bank and create opportunities for XaaS offerings that would never have been possible in the past.

 

Regulatory hurdles

While most of the major public cloud providers now offer a range of analytics-specific infrastructure services, they come at a price. Once your data and models live in a particular proprietary cloud repository, they can be difficult to get out again. You’re locked into their infrastructure for the foreseeable future.

Besides the commercial implications, this lock-in poses a major regulatory problem for banks. According to the latest consultation paper on outsourcing and third-party risk management from the Prudential Regulatory Authority (PRA), regulators expect banks to be able to port any outsourced services over to another provider or bring them back in house without any risk to business continuity.

 

The right tool for the job

I’ve had conversations about moving to the cloud with CIOs at banks of various sizes, and this issue of portability has been a recurring theme. They are looking for analytics solutions that work with any vendor and run on any cloud platform – or move between platforms – without significant disruption. In fact, since many banking use cases involve analysing data that is too sensitive to store outside the internal network, one of the most-requested offerings is a hybrid cloud/on-premises solution. Banks could then perform experimental projects with anonymised data in the cloud and then bring the successful models back into their own data centre for deployment in production.

Finally, while there’s a lot of buzz around AI/ML techniques, it’s important to recognise that they are not always the best option. Traditional statistical methods can be equally powerful, cost less to maintain, and can be easier to explain and audit – an increasingly important capability, as a recent legal case in the Netherlands demonstrates. My advice is always that banks should look for a single platform that gives equal support to both statistical and AI/ML modelling techniques and provides easy-to-use visualisations that make models easier to interpret. This allows your data scientists to pick the best tool for the job. And makes it easier for you to ensure the safe and responsible use of your data.

We’re working with a number of leading banks to power their digital transformation initiatives and build towards the XaaS future in the cloud. Find out more about what’s possible with cloud computing.

 

Banking

Bringing Automation to Banking

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Ron Benegbi, Founder & CEO, Uplinq Financial Technologies

 

Automation is everywhere you look these days; from supermarkets to warehouses to automobiles. This prominent trend shows no sign of abating anytime soon. However, some sectors remain behind others when it comes to adopting automated technologies. Banking is one such segment, but there’s now evidence to suggest that this could be about to change.

 

What do we mean by automation?

There are a lot of ways to define automation, but broadly the term applies to any technological application where human input is minimized through design. Over the years, automation has evolved from a basic level, which took simple tasks and automated them, all the way to advanced automation powered by Artificial Intelligence (AI). In general, automated solutions work to increase productivity and efficiency within businesses and often result in a reduction in costs associated with human capital.

 

Ron Benegbi

Why has the banking sector been slow to adopt automation?

The banking sector has been built on a number of long-standing, tried and tested processes and protocols, which have been continually fortified and refined over time. This is one explanation as to why the sector has been so slow in adopting new, automated methods within its operations. Additionally, many major financial institutions have spent decades building their own internal legacy computer systems, which are often incompatible with modern automated solutions.

When combined, these two issues have caused a significant lag in the banking sector with regards to the adoption of automated technologies. This lag has created a market opportunity that a number of fintech providers have been able to exploit in recent years. Offering a more responsive and tech-first user experience, many fintech providers are leveraging the power of automation to better meet the banking needs of their customers. However, there is still time for the banking sector to start bridging this gap.

 

Does automation have a place in the banking sector?

The opportunity for automation to play a role within banking can be transformational.

To achieve this, it’s important that legacy organizations begin to learn from their more tech-savvy, smaller counterparts. If used effectively, automated financial solutions can greatly improve the experience of banking customers, both on a personal and business level. So, what exactly does this change look like, and how far away are we from seeing it become a reality?

A good place to start is the small business credit lending process, where not much has changed since the 1980’s. Over that period, the world has greatly transformed, but the methods used to assess credit worthiness have remained somewhat static. For the most part, banks assess data related to businesses’ accounting and banking records and from credit scores. For many businesses, especially the newer and less established ones, this antiquated approach is having a detrimental effect. In fact, it’s often cited as a contributor to the huge funding gap between SMBs and their larger counterparts.

 

How can automation benefit the banking sector?

By adopting more automated technologies, lenders in the banking sector can begin to assess more comprehensive information when making credit decisions. Notably, new methods exist, which enable additional data sets to be evaluated, in order to build a more accurate financial depiction of a business’ overall position. This data can come from sources like external market attributes, economic indicators, demographic data and exogenous shocks.

By leveraging additional data sets through new methods of financial automation, banks are now in a position to respond more effectively to small businesses, including those in emerging and evolving markets where there is a lack of conventional sources of information.

With more ways to access funding, facilitated by alternative data and automated processes, small business owners can improve their operational efficiencies and accelerate their growth efforts. In doing so, legacy oriented financial institutions can now better equip themselves in protecting against new, nimbler tech-based disruptors.

 

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Banking

MYTH BUSTING THE ROLE OF OPEN SOURCE IN FINANCIAL SERVICES

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Nigel Abbott, Regional Director North EMEA, GitHub

 

There is no denying the financial services (FS) industry is under pressure to innovate. Not only have customer and consumer expectations for digital experiences surged in recent years, but the emergence of nimble and ambitious fintechs have disrupted the market. Yet, despite striving for innovation being table stakes across the industry, FS organisations inevitably face familiar hurdles that slow their progress, including concerns surrounding security, compliance, and the ability to act fast.

Open source is increasingly seen as a route to drive innovation and create new value. The FS sector’s utilisation of open source and the transformative role it can play is accelerating – on paper, at least. According to the recent Fintech Open Source Foundation’s (FINOS) 2021 State of Open Source in Financial Services survey, as many as 80 percent of FS leaders said that innovation, reduced time-to-market and total cost of ownership are factors for FS businesses to consume open source.

Nigel Abbott, Regional Director North EMEA -GitHub

But the reality is these positive adoption figures don’t tell the whole story. The survey also revealed that 75 percent of FS technology leaders said their businesses are either not “open source first”, or that they did not know if they were. Tellingly, less than one in ten (eight per cent) said that their business has put in place policies to encourage open source contribution.

The statistics point towards disparity between uptake of open source and the ability to use it to its full potential. But why?

For me, it comes down to some common myths about the role of open source that need demystifying:

 

Myth #1: There are limits to the innovation that open source can deliver

This could not be further from the truth. All enterprises, including FS companies, rely on open source software to build the best software for their customers, improve infrastructure, and unlock the potential of their engineering teams. Nationwide, for example, has completely redesigned its DevOps processes to respond faster to market changes and keep pace with customer expectations to remain relevant. The impact is transformative when they actively embrace it and participate fully in the open source community, creating a win-win situation for end-users. 

 

Myth #2: Data can be shared without consent 

Quite the opposite. Open source does not require FS businesses to share all their secrets and give away their competitive advantage. Instead, taking an “innersource” approach allows financial institutions to take the skills of developers who are accustomed to using open source tools and brings these inside the company firewall, providing a secure internal platform for working collaboratively on projects.

 

Myth #3: Open source is not secure

The most common misconception is that higher security risks are associated with code being openly available to anyone who uses it. But the open concept is, in fact, one of the biggest security strengths of open source. This is because of the collaborative nature of how code is built. The open source community has a shared responsibility for developing and maintaining secure code, and there is a vast global pool of developers identifying and fixing security issues. Supported by the right tools and processes, open source makes it easier for developers to code securely throughout the entire software development lifecycle, reducing the amount of time and financial investment in delivering secure products. Research from Red Hat found that security is regarded as a top benefit for enterprises using open source.

 

Myth #4: The open source community lacks finance sector contributors

This is untrue. Financial enterprises of all shapes and sizes are prominent participants in the open-source community and lead by example, sharing meaningful code contributions. Challenger banks and institutions such as Goldman Sachs contribute to open source initiatives via FINOS. By opening their code and ideas, FS companies can share lessons and support the whole community – helping them deliver better services and more value to their customers. And crucially, they are advancing a community that they can systematically tap into and benefit from.

Open source is already delivering innovation in the FS sector. But the bottom line is that there is so much extra value it can bring. Unlocking the full potential of open source to effect change does not just require buying DevOps tools. Open source requires organisation-wide understanding and support, a culture of collaboration and a progressive DevOps and governance process to thrive. Only then can it deliver its true value and accelerate innovation.

 

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