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A LOW-CODE LONDON MARKET – THE KEY TO INDUSTRY FUTUREPROOFING

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By Richard Farrell, Chief Innovation Officer at Netcall

 

Aged 332 years, the London Market isn’t new to the need to modernise. For many years, the insurance market has been cautious regarding change and technological advancement, whilst facing mounting pressure to radically transform and keep pace in the digital world. The pandemic, however, has amplified this need for change. Following a year of economic instability, London Market firms risk becoming obsolete if they do not take immediate and urgent action to modernise.

In September 2020, the London Market reported a half-year loss of £400m, compared with a £2.3bn profit in the first half of 2019, and expects to pay out around £5bn in COVID-related claims. With further turbulence and financial uncertainty ahead, the corporate body must keep its sights firmly set on cutting unnecessary costs and transforming internal processes to facilitate this.

Whilst throwing the sector into chaos, the pandemic was a true eye-opener for the Market. Relying on systems and processes built years ago, which were centred around people doing business in a City office, left the London Market at a crossroads and facing once-in-a-lifetime challenges and opportunities. Lockdown created an urgent need for new systems to support a new hybrid workforce, and within this need now lies the opportunity for London Market firms to rejuvenate – building greater efficiency into systems and processes to enable agility and future growth, as well as long-term digital ways of meeting and working. Intelligent automation technologies such as low-code platforms, when combined with robotic process automation (RPA) and powered by artificial intelligence (AI), will be pivotal to this transformation. With these platforms, London Market business users can collaborate and build new applications with IT teams without the need for costly and time-consuming traditional coding methods.

As a result of redesigning processes, London Market businesses can identify where efficiencies can be made and then rapidly develop optimised systems that keep both technology and people at their core. This will be crucial to achieving significant long-term cost savings and maintaining the London Market’s current position on an international level.

 

Using 2020 challenges as inspiration to evolve

The last year has seen a range of hurdles for both the London Market and individual businesses: a shift to remote working, the need to optimise costs, and the imperative to maintain status in the global order. These have not been easy, and these challenges are likely only the start of greater change that we will see in the coming months and years.

Businesses have proven in the last 12 months that they can adapt and shift when needed with the Blueprint plan, Lloyd’s of London’s ambitious plan to create the world’s most advanced insurance marketplace. Blueprint Two, which was released in November 2020 and built on March’s Blueprint One, established new ways of doing business, underpinned by the need for digital channels that enable advanced data collection and management. The right tech and tools can enable brokers, insurers and partners with delegated authorities to operate at a materially lower cost, estimated to be at least £800m as part of this evolution.

As John Neal, Lloyd’s of London CEO, states, the London Market needs to make itself ‘more relevant, more innovative and much more cost-effective’. Solutions that enable rapid digital transformation, whilst boosting efficiency and lowering costs, will be crucial to achieving this goal.

 

Future-proofing the London Market 

Due to its ease-of-use, low-code platforms can empower London Market teams to collaborate to build new applications in the fastest way possible and speed through application backlogs. Rather than taking a rip-and-replace approach to innovation, the technology can enable London Market organisations to stitch legacy systems together with new applications – effectively building upon existing investments to provide a better user and customer experience.

With the right technologies, the London Market can rapidly reduce inefficiencies by automating manual or broken processes, whilst also integrating with a number of different systems. This will enable organisations to provide a central platform that can give visibility across all parties – and in turn enable better decision-making through richer data and the use of AI.

Perhaps one of the more pressing London Market processes brought into the limelight during the pandemic has been the process of claims management – which intelligent automation solutions can help with too. With so many stakeholders involved, managing the claims lifecycle can be extremely complex, and the sheer number of claims being processed means that teams face huge pressure to provide swift service, and to keep claims pipelines moving. By consolidating data and processes under one platform, the lifecycle management can be improved to provide real-time information relating to a company’s claims exposures, including aggregates, and other elements such as supplier management. Greater visibility of these elements will, in turn, drive greater sector efficiencies.

 

Reshaping the London Market once and for all 

The next few months will bring myriad challenges and opportunities around reshaping how London Market businesses work and trade for the benefit of its clients and people. There are considerations for all organisations, including new ways of employee and trading partner engagement. A one-size-fits-all strategy simply won’t work in such a complex environment, but using the right software can unlock business benefits and growth potential for London Market firms large and small.

Fundamentally, London Market firms must invest in and prioritise the technologies that will enable their workforce to save time and drive value back to the organisation – as well as work how they want to work. Whilst the social nature of the London Market, which is largely based on personal networks, indicates a strong return to office work when lockdown restrictions are lifted, there is still likely to be some level of remote working moving forward.

Flexible and agile intelligent automation technologies can empower the London Market to join data together across numerous back-end systems to provide an easy-to-use workflow across complex process requirements. By enabling employees to make better-informed underwriting and claims decisions, based on better access to enriched information, organisations can not only drive greater efficiencies, but keep up with the demands of a digital-first future.

 

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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