Paul Jones, Head of Technology at SAS UK & Ireland
Does your bank manager know who you are? Unless your net worth is unusually high, the answer is probably no, and that’s been the case for many years. Banks have been using statistical models to inform credit-related decisions since at least the 1970s, and today almost every aspect of operational decision making is driven by sophisticated real-time analytics. So, while customers may hear about the outcome of their loan application from a bank employee, the decision itself has already been made in the background by computers without any human input.
Model-based automation has unlocked huge benefits for banks and customers alike because credit decisions can now be made in minutes or seconds, rather than hours or days. However, managing models at scale creates significant challenges of its own, and designing efficient model operations (ModelOps) is still largely an unsolved problem for most financial institutions.
Big banks, big problems
For example, the more established banks tend to have adopted model-based approaches piece by piece over the years. The use of models now extends far beyond the retail credit risk marketing function, and different parts of the business are using different methodologies, tools and techniques for managing the model life cycle. Both the data and the models themselves are isolated in departmental silos, so the bank often ends up making decisions in unconnected ways or based on only a fraction of the information it possesses on each customer. In simple terms: If the mortgage department’s model doesn’t have the same information as the loan team’s model and isn’t aware that a customer just took out a large loan, it may not make the best decision.
Similarly, the size and complexity of these banks tend to sap the agility of their model deployment processes. Going live with a new model involves surmounting countless organisational and regulatory hurdles. SAS research indicates that it can take three months to get a model deployed, while Gartner has found that over 50% of models never make it into production. The opportunity cost of failing to have the right models in place can be significant. I recently spoke with a CRO who estimated that during the credit crisis in 2007, delays to model deployment had cost the CRO’s bank around £500,000 per month.
The newer digital banks and fintechs tend to do better at agile model management. Since the burden of legacy systems doesn’t apply, they can potentially start from scratch and adopt a more joined-up approach. And because they have fewer customers and their failure poses less of a systemic risk to the economy, they attract less scrutiny from regulators, which means they can have lighter processes.
However, as these smaller banks grow, the weight of regulation they must shoulder will grow too – a burden they may lack the infrastructure and expertise to sustain. While they may currently be able to get away with a simpler approach to model management, that’s not going to work in the long term. To compete at the scale of the larger incumbents, they will need to tighten up their governance and industrialise their processes.
Model management as a key battleground
Whether the established banks maintain their dominance or the challengers prevail, ModelOps will play a key part in shaping the industry over the next few years. As artificial intelligence opens up new possibilities for even smarter cross-channel, real-time decisioning, the ability to design, train, deploy, monitor, update, audit and explain models will separate the wheat from the chaff.
Currently, almost all banks are struggling with model life cycle management, and especially with deployment. A recent McKinsey study found that less than 6% of companies had the ability to easily embed AI into formal decision making and execution processes, and less than 15% had the technological infrastructure to support deployment. This jibes with a recent article where Gartner Vice President and analyst Jim Hare stated: “Where organizations need help is how do [they]scale and operationalize and really handle an increasing number of models in production.”
At SAS, we believe that the inability to integrate analytic solutions into workflows and achieve front-line adoption is the No. 1 reason why data and analytics initiatives fail. That’s why we’ve concentrated efforts on industrialising the deployment of AI.
Why is model management so hard?
To start moving in the right direction, banks first need to understand the problem. Why are model management and deployment so hard? One of the biggest reasons is more human than technical: It’s is a place where two different traditions meet.
On one side, data science, which comes from an academic background and aims to turn groundbreaking research into game-changing business value. On the other, IT operations, which focuses on delivering reliable services within technical, regulatory and business constraints. These two traditions work in different ways, move at different speeds, and target different goals – so it’s not surprising that there’s often a clash of cultures.
How ModelOps can help
The promise of ModelOps is that it provides a robust workflow that acts like a set of intermediate gears between the data science and IT operations teams, enabling the smooth transmission of models from development into production while allowing both teams to work productively and at the right pace. By automating handoffs between teams throughout the model life cycle and providing end-to-end traceability and governance, a ModelOps approach can turn a misfiring modelling pipeline into a well-oiled machine.
At SAS, we’ve had firsthand experience of the challenges of moving to a ModelOps approach. We’ve always been both a data science company and an IT operations company, so we’ve had a foot in both camps for over 40 years. But it’s only relatively recently, with the maturity of cloud technologies and the widespread adoption of DevOps practices such a continuous integration and deployment (CI/CD), that we’ve really cracked the problem.
For example, we’ve learned how to use the cloud to break the model life cycle out of departmental silos and provide a commercial model that suits the experimental, fail-fast approach that data scientists need. Meanwhile, modern DevOps tooling provides common ground for data scientists and IT operations teams to collaborate effectively and ensure proper governance while managing models at scale.
We’re now applying the insight we’ve gained to help clients throughout the financial services sector adopt a ModelOps approach. For example, Covéa Insurance chose SAS to enable the deployment of complex machine learning models in a high volume real-time scenario, while Standard Chartered Bank gained more efficient model deployment in support of IFRS 9 and won the Asian Banker’s Enterprise Technology Implementation of the Year award. Find out more about ModelOps or take a deeper dive.
TRANSFORMATION IS NON-NEGOTIABLE FOR BANKS LOOKING TO DELIVER VALUE IN A POST-PANDEMIC WORLD
Andrew Warren, Head of Banking & Financial Services, UK&I, Cognizant
In addition to responding to changing customer expectations, higher operating costs, new technology, and an evolving regulatory landscape, financial services organisations now also face the uniquely challenging business environment created by COVID-19. The economic consequences that are unfolding rapidly and unpredictably mean that banks must double-down on both their efficiency and customer experience agendas. In light of this, the need to modernise legacy banking platforms will gain sharper focus as banks emerge into the post COVID-19 landscape, driven by the need to focus on value for customers and agility to change and shift operations quickly.
If banks are to remain strong and stable and make real progress with their efficiency and experience agendas, transformation is non-negotiable – but it can be risky and have high rates of failure. So how can banks pursue their transformation agenda, while addressing the very real risk that modernisation of legacy banking platforms presents?
Communicating value across the business
Banking transformation may have traditionally been the domain of the IT function, but the impact on current and future value means it should be on the agenda of a much wider set of senior executives. This includes the CIO and COO but should also be as far reaching as the Chief Risk Officer, Chief Financial Officer, Chief Digital Officer, and Chief Experience Officer.
When we talk about value in the context of transformation it can mean multiple things. In monetary terms, transformation can reduce the total cost of a bank’s IT infrastructure, with legacy equipment 55 per cent more costly than cloud data. More importantly however, transformation often results in moving from highly manual orientated processes to more efficient, automated – and therefore accurate – processes. In turn this can lead to more informed and tailored products and services, internal process efficiencies, enhanced cybersecurity, advanced analytics, and reduced risk, especially around fraud and malicious activity. These all add significant value to customers, as well as operational and regulatory imperatives.
Furthermore, viewing transformation through a value lens should tie it to a range of specific financial and accounting metrics that ultimately measure success. That includes both those that reflect the protection and extension of current value, as well as measuring the extent to which transformation will support the capture of future value. Financial services organisations have a huge opportunity to create greater value for customers from innovation in products and services. Changing market dynamics are creating a basis upon which banks and others in the industry can evolve their offerings and organisations.
In much the same way as we have already seen in retail, for example with Amazon and AliBaba, and media platforms, such as Facebook and Netflix, customers are adjusting to a new way of banking that is changing expectations. To keep up, banks need to increasingly provide easy-to-use digital-first services across their products, as well as introduce new tools to help customers manage their money in the 21st century. And there is no doubt that the fall-out from COVID-19 will likely further drive the degree and extent of digital adoption.
Traditionally, financial institutions take many different approaches to transformation, such as developing sleek new customer experiences to compete or developing new platforms and partnering with fintechs. But achieving success for more mature banks is more challenging given the obstacles presented by their legacy platforms. Comprising complex, customised systems, these are expensive to run and very costly to change.
The inevitability of change
To truly transform operations and experience, many banks are now having to face up to the reality that they cannot move forward without banking platform transformation. That means they must – in one way or another – replace their historic systems with more modern, cost-effective, and flexible platforms. That is going to be essential to stand up the capabilities required to enable digital products and deliver the truly revolutionary experiences that customers demand.
Recognising this, many banks are now considering their options. Some have already started down the challenging path and hit bumps in the road. A very small number have successfully executed their ambition to create a platform for the future. All banks contemplating transformation should take lessons from both the successes and the mistakes. These will be critical to inform their plans.
What are the next steps?
There are a number of essential transformation steps to consider that will help realise value from investment as rapidly as possible, provide an appropriate level of delivery confidence and manage exposure to the operational risk normally associated with such changes. These include:
1. Business strategy must inform every step of transformation – ensure that the approach to platform transformation is tightly aligned to the wider business strategy.
2. Design a strategy-aligned roadmap for delivery – a transformation roadmap should clearly set out the logical order in which business outcomes will be delivered. Here again, that needs to align with the value that the organisation is seeking to achieve, with incremental progress determined by business priorities. This involves making appropriate use of modern delivery methods, such as agile, and making sure that everything that is done satisfies and is frequently assessed against the relevant value criteria.
3. Assess technology selection against business value – organisations often undertake detailed and exhaustive market, functional and technical assessments when reviewing new products and suppliers. This often means either the technical assessment dominates proceedings and / or new technology platforms are selected without a clear line of sight to the value required. Poor product selection is a risk as a result, as well as a lack of understanding of how products should be deployed to inform the sequence of delivery required by the transformation roadmap.
4. Assess your readiness for change – unsurprisingly, given the sheer scale and velocity of change that business leaders must deal with, resistance to change is often a key reason given for the failure of banking transformation projects. However, it is crucial that the ability of the organisation to deliver and adopt the operational, technical, and cultural changes required to support transformation is comprehensively assessed and done early.
The impact of COVID-19 paired with and the demands that financial services organisations face from all directions, make change an inevitable necessity for the most. The approach to delivering a successful banking transformation, underpinned by a modernised platform, will vary dramatically from bank to bank. However, above all, businesses need to ensure that value drives every aspect of change explicitly linking transformation strategy and investment with the realisation of value.
CLOUD ALLOWS BANKS TO BASK IN CHANGE
by: Elliott Limb, Chief Customer Officer at Mambu
As a new era of banking takes off, the cloud is enabling players to adapt fast at low cost and with minimum risk, while rolling out products that customers actually want, writes Elliott Limb
For all the talk of today’s banking landscape being the most competitive ever, you’d think the customer would be spoilt for choice. Sure, there are more banks and prices are low, but the reality is that it is still pretty hard to tell one from another when it comes to real value-added services.
Every retail bank, for example, offers some form of online and mobile banking; and most private banks have adopted automation and robo advice of some kind to help bring costs down and make its service more relevant to customers.
The upshot of this homogeneity is that rather than working to provide a unique service, banks seek to stand out from the pack through marketing – offering free travel insurance for premium customers; zero-fee balance transfers; no interest on overdrafts; low-cost or flexible loans. These offers aren’t about providing a better banking service. They are small treats in an industry that has raced to the bottom on price.
But this old-school approach is now being challenged. Technology across other industries has already forced change, putting choice for the customer front and centre. The big platform companies like Amazon or Google were among the first to use Big Data and algorithms to analyse behaviour and thus predict what the customer wants – often before the customer knows it themselves.
As other industries apply predictive technologies, it has had two effects: customers have come to expect a highly personalised and relevant service that enhances their lives; and the big platform companies are beginning to encroach on some banking activities such as loans and payments. The capital reserves held by Apple today would put it among the top ten banks outside China.
Taken together, these changes are dragging banking into a new era of differentiation and choice, where customers will expect to get what they want when they want it at a price they’re willing to pay.
What every successful player will have in common is agility – the ability to quickly adapt and change not just products and services but business strategy to reflect movements within its own market space. And to be clear: this agility isn’t just about the technology that is used – it’s a business model.
The agile model doesn’t wed the bank to a set of tools; it marries the bank to choice, thereby maximising the chances of it becoming and remaining the best. This agility can only come from cloud operations.
Enter the cloud
Cloud allows banks to innovate fast. Digital technology in the cloud lets them quickly reconfigure products and services to take into account new regulations or temporary circumstances – the fall-out of Covid-19 and the need to waive overdraft fees or provide payment holidays, for example. Where legacy systems demand banks carefully plan and time changes, which can take many months, banks working with the cloud can carry them out on the hoof, often within hours. This makes them more competitive, incurs lower costs and lowers risk.
Working with the cloud also allows banks to align costs to revenues because billing is on a pay-as-you-use basis. Use can be scaled up or down according to demand, so expensive technology doesn’t lie idle on-premise ever. Locked-in costs are minimised. This means that you could launch a great new customer-centric bank today and scale up to become a $1bn unicorn fast.
Finally, cloud technology helps cut risk. By providing flexibility, banks can adapt their products and services as the market evolves. They aren’t locked into medium and long-term strategies. They can be nimble.
Furthermore, cloud providers invest heavily in their technology, updating and upgrading it constantly and ensuring its resilience and security in a way that individual banks simply couldn’t afford. So banks working with cloud providers will have access to the best, most secure, resilient, up-to-date technology.
Make no mistake. Competition going forward will be tough and customers will expect the best or they will go elsewhere. Margins are already low, thanks to the above mentioned fight to the bottom on price.
However, banks using cloud technology will be ready to compete on a level unseen as yet and offer customers services that they want and need, at price points they can afford. They are able to differentiate on agility and adapt quickly as their market dictates and they are able to manage risk. As a result, customers will have real choices for the first time – choices that will add value to their banking experience and even their lives.
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