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To Set the Pace or Keep Pace?: The Merits of Fast Follow in Financial Services

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Tim FitzGerald, EMEA Financial Services Sales Manager, InterSystems

 

Within the financial services sector, innovation is key to ensure firms can compete in an ever-changing landscape. However, always being at the forefront of innovation isn’t easy, particularly when trying to innovate in multiple business areas, with limited resources and budgets. Consequently, it is time for a mindset shift.

Rather than always striving to be at the cutting edge of innovation across the entire organisation, financial services institutions should recognise the merits of just keeping pace in select areas. In other words, firms should aim to be fast followers if they want to remain competitive.

Tim FitzGerald

As firms seek to “fast follow” where others are leading, speed and agility will be essential. This requires financial services organisations to refocus their attentions away from digital transformation and towards digital acceleration and adopt the right approach and technologies.

Why become a fast follower?

Becoming a fast follower doesn’t mean lagging behind. Instead, it allows firms to continue to innovate while having the luxury of learning from the mistakes of others, the so-called ‘first movers’, and leveraging the innovations of others. For instance, incumbents can learn from and leverage the advances that fintechs are bringing to the financial services sector, rather than having to start from scratch themselves. Taking invaluable learnings from the likes of fintechs will enable financial services organisations to adapt and modify when they come to their own innovation efforts, reducing likelihood for failure and often allowing them to speed time to value.

With 45% of financial services organisations globally open to collaborating with fintechs, it is clear the appetite to learn from fintechs is there. Therefore, adopting a fast follow approach to fintech innovations will help to ensure incumbents can keep pace with the competition and developments in the sector and offer their customers the same level of service.

Additionally, by becoming a fast follower in particular business areas, firms can really focus on a handful of innovation initiatives to drive forward themselves. By being able to truly home in on certain areas, whether that’s product development or regulatory imperatives, for instance, the results of those initiatives are likely to be improved. This approach will help to ensure that everything is done well, rather than a more scattergun approach in which resources are spread too thin across a variety of initiatives.

 

The importance of agility

At the heart of achieving the agility required to become a fast follower lies real-time data. Currently, many firms struggle gaining access to real-time data owing to challenges such as data silos, the growing volume, velocity, and complexity of data, and disjointed processes and technology. As such, it is vital that firms address these issues to be able to leverage data to act quickly and intelligently where others have paved the way and lay the foundations to undertake their own select innovation initiatives.

Using data platform technology to implement a connective tissue, sometimes referred to as a smart data fabric, is becoming an increasingly popular and effective method of gaining access to real-time data and insights.

A new architectural approach, the connective tissue speeds and simplifies access to data assets across the entire institution. It accesses, transforms, and harmonises data from multiple sources, on demand, to make it usable and actionable for a wide variety of business applications.

Leveraging a connective tissue infrastructure also makes it faster and easier for firms to gain new insights and power intelligent predictive and prescriptive services and applications thanks to a range of embedded analytics capabilities, including data exploration, business intelligence, and machine learning. These capabilities allow firms to leverage insights more quickly, shortening shorten time to decision, and give them the agility needed to fast follow.

Additionally, unlike alternative approaches, the connective tissue allows existing legacy applications and data to remain in place. As such, it enables organisations to maximise the value from their previous technology investments, including existing data lakes and data warehouses, without having to “rip-and-replace” any of their existing technology.

 

A new way to innovate

The rapid change of pace in today’s financial services landscape means that being at the cutting edge of innovation every time isn’t a viable option. Fortunately, more often than not, it is enough to fast follow on the heels of others, learning from their mistakes.

By arming themselves with the right data technology, firms can ensure they have the agility needed to do this. In turn, this will empower financial services organisations to keep pace with the competition and avoid lagging behind, while also focusing on a handful of key innovation initiatives to drive their business forward.

Business

How Big Data is Transforming Bilateral Trading

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By Stuart Smith, Co-Head Business Development – Data & Risk

 

Since its inception, Big Data has been an important part of how firms have identified and constructed quantitative trading strategies with hedge funds depending more on quant strategies which rely heavily on big data driven analytics.

As big data technology continues to move from being a specialised technical capability to being a commoditised capability available on a range of easily consumed technology platforms, its use within the financial derivatives will continue to increase beyond the initial quantitative driven capabilities.

At the same time, the number and range of available data sources is increasing rapidly. Whether it’s the increase in alternative data sets or new technology enabling firms to simply keep more of the data they have been creating, the volume of data available is increasing dramatically.

 

Big Data in Risk Management

Risk Management has always had requirements which have driven a close collaboration between business and technology to make available risk analytics useful for the business to make better decisions. As technology becomes more advanced, the metrics available continue to improve as well. This is typically because many risk metrics require high numbers of scenarios and valuations to correctly identify risks in multiple scenarios. To maintain flexibility, this has led to an explosion of data to manage. Firms are increasingly keeping all this data available which can run into many Terabytes (TBs), much of which needs to be ‘In Memory’ to make it accessible to analysts.

Stuart Smith

To achieve this big-data, technology is critical to allow firms to move large volumes of data quickly and easily from affordable long-term storage into high performance in-memory analytics. Big Data technology is ideal for this type of problem to enable large volumes of data to be recalled from across multiple stores and appropriately aggregated or filtered based on the analysis which users are requesting. Whereas in the past, analysts would have to accept that data outside of the last 3-5 days is only available in a summarised format, they can now expect that the data can be re-hydrated quickly and easily from cloud data stores and available to them in an easy-to-consume web interface.

This can enable much more dynamic types of analysis, for example where a new risk is identified, through analysis of a recent data set it’s now possible to find a long history of that risk, whereas previously it would have been lost through summarisation and fixed reporting processes.

 

Collaborative Data Sets

More big data stores are being created as the industry becomes more collaborative and uses increasing numbers of fintech solutions and platforms. With this change come new ways to analyse data and provide new insights.

For instance, through the automation of collateral exchange, an historical store of margin calls, payments and disputes has been created. This history provides a resource for banks to understand their performance in accurately issuing and making margin calls based on derivatives and compare their performance to that of the industry as a whole. The example below shows how a firm can be benchmarked while holding other institutions data private.

These types of analysis are new and could not be delivered without the centralised collaborative data model. It can prove to be instrumental in improving firms’ overall operational efficiency and client service.

It also provides an opportunity for Machine Learning techniques, based on big data sets, to analyse and predict payments requests which are likely to be disputed and potentially identify causes before an actual dispute is even raised. This type of ‘self-healing’ process can only be enabled by a large history of data through which algorithms can be trained.

In the case of Initial Margin (IM) calculated by ISDA SIMM* a new set of challenges have been introduced through having a two-sided risk calculation as part of the process of deriving payment information. This adds another level of complexity to the resolving of disputes; however, the potential offered by having large volumes of data opens up new options on how this challenge could be solved. The long history of Common Risk Interchange Format (CRIF)** data provides a long-term view of the sensitivities for most OTC derivatives, which can enable firms to identify basic issues like stale market data day over day. However, as with most detailed analysis differences in models, they can also be identified through looking at differences over long periods of time. Identification of these types of model discrepancies can help firms to be more proactive about reviewing their modelling deficiencies to ensure that differences don’t lead to disputes.

 

Looking ahead

The sheer volume of data can be an industry-wide challenge with firms having to manage disparate, needlessly duplicated and ultimately overwhelming information. Creation of an industry standard for reporting and analytics is, therefore, crucial to enable firms get clarity and valuable insights from the masses of data and centralise the information as a single data layer. Acadia has designed Data Exploration (DX) suite to be one-of-its-kind big data analytics platform to help sell-side, buy-side and fund administrators see its market positioning, trends and analysis of industrywide metrics.

The impact of big data will only grow and the industry is left with no choice than to evolve the use of technology, whether that is to drive quant strategies for hedge funds, more dynamic forms of risk management or larger shared industry data sets. All of these applications rely on underlying big data technology platforms to provide distributed analysis capabilities. As these capabilities continue to develop so will the types of analysis which are available to firms.

*The ISDA Standard Initial Margin Model (ISDA SIMM™) is a common methodology for calculating initial margin for non-centrally cleared derivatives, developed as part of ISDA’s Working Group on Margin Requirements (WGMR) to help market participants meet the BCBS-IOSCO margin framework for non-cleared derivatives.

** The CRIF file (Common Risk Interchange Format) is the industry template used to hold and exchange sensitivity data. ISDA’s calculation specifications are used to produce Delta, Vega and Curvature sensitivity numbers at Risk Factor-level

 

 

 

 

 

 

 

 

 

 

 

 

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Banking

Three tips to help banks profit from the rise of managed services

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By Chris Mills, Global Head of Managed Services Sales, Finastra

Research from IDC finds that only 29% of banks claim to have a long-term, strategic digital transformation plan in place, despite results showing firms that had invested in transformation saw improvements of 27% in reducing risk, 27% in innovation and 26% in improved customer satisfaction. The days when banks’ IT teams operated in isolation of business goals should be very old news. Effective CEOs build digital transformation into their strategies from the start, and the most successful CTOs understand how to apply technology to achieve business success.

In many ways, CTOs have become more like orchestrators or conductors than individual instrumentalists. They need everybody on their team to work in concert to deliver value according to desired business outcomes. It’s less about building IT from scratch and more about assembling components and making sure that they operate smoothly and cost-effectively.

Chris Mills

One of the most striking findings is that 40% of financial institutions said that the pandemic meant they had to accelerate and increase all of their digital-first initiatives. They had to innovate to remain viable and competitive. It’s also clear that there is no longer just one, singular path of IT delivery. Instead, CTOs are facing multi-threaded challenges. It means CTOs must consider many different deliverables and leverage all the resources at their disposal, including internal and external partners.

Changing customer expectations

The financial services sector was facing a range of external challenges even before the pandemic arrived. For example, from a consumer’s perspective, the exponential advancement of a smartphone’s technological capabilities in recent years has increased their expectations for new updates and improvements. This behavioural change has impacted customer decision-making and they now expect a high level of service and responsiveness, whether they are customers of a retail or a corporate bank.

The banking industry also faces regulatory, compliance, resilience, and sustainability issues. As ESG agendas become an increasingly important priority for financial institutions, pushed by the rise of net-zero targets, CTOs must respond to these demands, and that’s why they see innovation as such a key focus.

But how can financial institutions that are late to the digital transformation party use technology to capture competitiveness and improve responsiveness for their clients?

One approach that has proved successful is managed services, which is a term used to capture the blending of services, product, and functional capabilities. When CTOs consider this option, they need to start by thinking about the business outcomes with the associated technical and functional expertise they need.

This includes the business uptime that is required, scalability and deployment speed. Does the bank need to roll out capabilities across the globe, and does it need to serve only the main financial markets, or emerging markets too?

Another question CTOs must consider is choosing what service partner to work with. Large system integrators have been providing these services for a long time, but a software partner like Finastra has advantages in terms of product proximity.

Service providers must offer tailored products focusing on the needs of its clients. Offering quality software allows banks to achieve their long-term strategic outcomes.

It’s important to look at all areas of a banks’ business, For example, what does the payments team need?

What does the head of lending need? What does the head of treasury need in order to grow their business over the next five years?

With that in mind, I offer three tips to banks when considering managed services.

1. Be very clear about what your business outcomes need to be. Really drill down into KPIs and metrics that we can look at to ensure we provide the service your bank demands. This can range from resiliency, compliance, regulation or even functionality and capabilities – such as how often you require upgrades.

2. Measure and assess your own resources, skills and capabilities. Understand where you want to draw the line between the responsibilities you would want a service partner to take on and what you want to retain. There shouldn’t be any grey areas. You want a clearly-defined line where responsibilities lie, so that everyone is very clear about who’s doing what and how KPIs and service levels will be met.

3. Be prepared to develop a long-term strategic partnership, over five or 10 years. We expect hard questions, and you should be expecting them back – ultimately that’s how good relationships and partnerships work.

As IDC writes in its report ‘New service models to accelerate innovation in banking’ these holistic and software-led models require banks to master a set of new skills, including governance and partner management. Service partners should be industry-savvy, should supply end-to-end expertise, and should be aligned to support the financial institution’s business goals, not just technical KPIs.

Digital transformation infrastructure management requires CTOs to act as a conductor, rather than a solo performer.

 

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