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Banking

WHAT IS MODELOPS – AND WHY SHOULD BANKS CARE?

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.


New challengers, same old issues

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.

 

Real-world results

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.

 

Banking

WHY AGILE, SCALABLE DATA MANAGEMENT IS KEY TO DIGITAL BANKING

By Jason Hand, Global Account Executive – Enterprise Sales, Commvault

 

Back at the start of 2019, before we’d ever heard of COVID-19 (hard to imagine these days, I know), mobile banking was predicted to overtake high street branch visits within two years. But the restrictions placed on daily life to get to grips with the pandemic proved to be a catalyst in speeding up adoption.

Although banks haven’t had to close during the UK lockdowns, they discouraged unnecessary visits — and many people new to online banking discovered that it could provide a quick and easy (and COVID-safe) way to manage their finances. No surprise then, that as summer came to an end, over three-quarters of the UK population were using some form of online banking and one in ten people had switched to a digital-only bank.

When it’s implemented well, online, digital and app-based banking is as easy as shopping with Amazon, booking a cab on Uber or grabbing a takeaway via Deliveroo. With so much potential to create a similar customer experience — and so much to lose if they fail — banks are under pressure to deliver on digital services. But their success (or otherwise) will depend on how well they manage their digital data and, in particular, how willing they are to adopt more agile, scalable, cloud-based solutions to underpin their new services.

 

Adopting New Technology in a Risk-Averse Sector

The UK’s financial services sector is undoubtedly slow when it comes to adopting new technology. Indeed, many UK banks continue to rely on mainframes. This cautiousness stems from the continued rise in cybercrime and the fear of non-compliance with FCA and data protection regulations.

Banks have to tread a thin line. They do want to embrace technology that will help them scale and support customer demand for digital services. But they can only do so with an IT infrastructure that keeps out cybercriminals, hackers and anyone else without explicit authorisation to view the data. So, if their legacy IT systems are secure and protect customer data from cybercriminals, banks do not want to risk implementing new solutions that could leave them exposed — even if those old systems make them less nimble and less responsive to changing customer demands.

 

Open Banking and Shared Financial Data

The increased digitalisation across the sector leaves banks facing a second security and data management challenge. Once, they only had to worry about managing their data and keeping it safe within their closed IT environments. Now Open Banking — a UK government-backed programme — encourages banks to securely share their data with trusted third-party financial services providers via an API (Application Programming Interface).

Typically, these third-party providers offer apps to assist with utility bill management, accounting and auditing, and savings (usually rounding up apps). Once a user grants authorisation, the app directly interfaces with that user’s current account. Customers — whether individuals or SMBs — love them, but for banks, they’ve meant a reassessment of security and data management strategies.

 

What Constitutes Good Data Management?

To begin with, it could mean switching to a single data management solution. Banks historically have deployed several different products to manage their data. Multiple applications add complexity and  need more people to oversee them operationally. This approach will add cost, risk, and ultimately will not align to their digital transformation agendas.

Running multiple data management solutions makes it harder to get a holistic view, understand customer behaviour and predict future trends. It also creates unnecessary security risks. Consolidating data management platforms reduces these risks and costs. At the same time, fewer inter-app data transfer points decrease the number of potential weak-link entry points for hackers and cybercriminals. From a practical point of view, using a single data management solution also enables all relevant data points in a hybrid world to be viewed on a single pane of glass — making it much easier to digest, interpret and deliver data management as a service back to their internal clients.

Automating data management components can improve security and cut costs by reducing human contact. In addition, it enables faster and more accurate data management that can accelerate cloud adoption where data management is key to success.

It’s worth saying at this point that banks have been slow on the uptake of both public and private cloud technology, and are clearly still concerned about security and privacy threats. This is despite the fact that cloud computing — particularly with a zero-trust approach to security — has become a lot safer and carries far less risk.

In the middle of 2019, the Bank of England published a report that estimated the world’s largest global banks conducted just a quarter of their activities in the public cloud or software hosted in the cloud. But change is happening, albeit slowly. Larger banks have started to recognise that cloud computing holds the key to running an agile business  — allowing them to scale their online services and safely store, process and mine vast amounts of digital customer data.

The maturation of the hybrid cloud market may have played a role in increased adoption and allayed many of the sector’s previous doubts. A hybrid cloud infrastructure combines public cloud, private cloud and on-premises architecture, giving users the flexibility to keep some applications and systems (those with particularly sensitive information, for example) within their own four walls while still being able to migrate other systems. It’s an elegant and cost-efficient way to balance security, scalability and compliance.

 

Demand for the Future

With so much change taking place across the UK banking sector, data management has never been more critical. Open Banking, consumer demand for digital banking, and app-based banks like Starling and Monzo are all shaking up the market. But the threats from cybercriminals and the risk of falling foul of FCA regulations are still very much present. And, while navigating all these challenges, banks still face pressure from shareholders and investors to make a profit, retain customers and grow the business.

For these reasons, data management strategy — and linked to that, the pace and effectiveness of cloud computing adoption — are now two of the most significant determining factors in how banks cope today, and how effectively they will operate in the future. As such, 2021 should be the year that most banks and financial organisations embrace and invest in new technology when it comes to data management.

 

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Banking

SEIZING THE OPEN BANKING OPPORTUNITY

Nick Maynard is a Lead Analyst at Juniper Research

 

Open Banking has made significant progress in 2020, having recently launched across much of Europe and now starting to emerge in other markets too. And there are two primary reasons why Open Banking is disrupting the banking industry so much:

  • Banks have begun to discover the real competitive advantage of a more open approach to banking. Offering a superior Open Banking experience to customers can be a compelling differentiator from other competitors as part of a wider digital app experience. Open Banking also creates a level playing field in markets where regulatory intervention has led to Open Banking deployment. As all banks are required to deploy APIs in this scenario, the situation is the same and does not put any one particular bank at a disadvantage.
  • Legislation – for example, in October 2015, the European Parliament adopted PSD2 (the revised Payment Services Directive). By early 2020, major banks in the EU had adopted Open APIs. There have however been many cases of late deployments of APIs and problems with the availability of APIs.

 

Nick Maynard

The Disruption Factor

Open Banking is a major disruptive factor for banks. The reason for this being that it opens up account data to both AISPs (Account Information Service Providers) and PISPs (Payment Initiation Service Providers), which can attempt to carve out a role in the banking area.

  • AISPs: These new vendors are able to access transaction data and balance information, as well as related information. This has, in particular, led to the rise of vendors such as Emma, Yolt and Connected Money. These vendors combine information from multiple sources, adding value to the user.
  • PISPs: In this case, the vendors are able to leverage Open Banking API connections to initiate payments directly from the bank accounts in question. This means that these players are able to bypass traditional payment methods, such as cards. Vendors such as American Express and PayPal have already launched solutions that have taken full advantage of this action.

 

PSD2 Changes

Generally, the implementation of the new PSD2 European regulation for electronic payment services effectively reduces the entry barriers for new digital players. It also opens up banks to the potential for competition, enabled by their own APIs. This allows these players to compete with existing services in fields currently offered by the banks. In the case of AISPs, it is possible that third-party applications could displace the role of the apps from incumbent players, which would dilute the bank’s relationship with their users.

As with any fundamental change to markets in the banking area, there is the potential to bring a number of both opportunities and challenges to consider with Open Banking.

Open Banking Opportunities & Challenges to Consider

Source: Juniper Research

Banks and other parties that are looking to become involved in the Open Banking ecosystem must weigh these opportunities and challenges carefully. Open Banking certainly needs a more collaborative approach than traditional banking models, which will require significant effort to make them successful.

 

The Forecast for Open Banking

The total number of Open Banking users is set to double between 2019 and 2021, reaching 40 million in 2021 from 18 million in 2019. The ongoing Coronavirus pandemic is increasing the need for consumers to have the clarity of combining their accounts and gaining insight on their financial health, and also boosting momentum in the adoption of Open Banking.

This extraordinary growth is being driven by Europe, where the regulator-led approach to Open Banking has created a standardised market, with low barriers to entry. This contrasts with markets like the US, where a lack of central regulatory intervention is limiting growth potential.

 

Open Banking – Delivering Opportunities and Threats

It is worth noting that Open Banking can be both a threat and an opportunity for traditional banks. While Open Banking exposes user information and access to potential competitors, this threat has the potential to affect all players in the market equally. Consequently, established banks must create innovative Open Banking services that will provide benefits for the user, while also attracting customers from less innovative competitors.

Payments will be critical to the emerging Open Banking ecosystem; accounting for over $9 billion in transaction value in 2024. However, payments in this ecosystem are at a particularly early stage. While eCommerce is dominated by card networks, there is the potential that this role will be eroded over time by ‘direct from account’ payments. Consequently, card networks should look to offer Open Banking-enabled payment services, in order to offset the risk of future disruption.

Open Banking Users in 2021 (m), Split by 8 Key Regions: 40 Million

Source: Juniper Research

 

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