What is Driving Firms to Streamline Technology and Operations?

May 31, 2019

This blog explores the key trends affecting asset managers and asset owners and examines their impact from an investment management technology and operation standpoint. In Part 1, Quantifi and Celent examined a number of key trends that are reshaping the industry including the shift from active to passive, growth in multi-asset and broadening of investable asset classes and increasing demand for tailored, outcome-focused investment solutions. This blog examines how margin pressures are forcing firms to improve costs and to streamline their core technology and operations and Celent’s recommendations for pursuing fit-for-purpose solution strategies.

Staying operationally lean and nimble

Margin pressures are forcing firms to improve costs and to streamline their core technology and operations. This comes at a point when technology suppliers themselves are also expanding their functional footprint across different parts of the asset management value chain. These combined dynamics are opening up opportunities for investment firms to rationalise, consolidate and pivot their application landscape towards a more fit-for-purpose configuration. We are observing a few specific areas within the asset management ecosystem, where convergence is actually taking place. The first one is the area of trade and order management (OMS and EMS), this is already well underway and we have seen some firms collapse six applications to just one or two.

In the coming years we anticipate more to come in terms of convergence, especially in areas where there are parallel asset-centric applications such as for portfolio management and construction, and also to better align across applications for derivatives and alternatives. Risk measurement and performance attribution are also areas where firms are evaluating the potential to converge. Both of these areas have data sets and analytical production activities that overlap significantly and therefore have the potential to converge. The key take out here is that in the medium term, in tandem with outsourcing and greater automation, we expect that investment managers can achieve cost reductions of up to 25% if they adopt an ecosystem approach to streamlining their technology and operations landscape.

We have observed a clear trend towards optimizing solution architecture, more so in the hedge fund space where firms are embracing cloud strategies to address performance, flexibility and agility. A significantly faster time to market is also a factor, as is the ability to scale faster and more efficiently. Over the last couple of years we have seen a number of established and start-up funds looking for a lightweight end-to-end solution that can support front-office risk as well as operational requirements and can also scale as the firm and AUM grow.

Upgrading the ‘investment engine’

FinTech service providers are increasingly directing their efforts to use cases within an investment management context. Some of these use cases are potentially disruptive whereas others can be complementary to existing buy-side ecosystems.

Emerging digital technologies are rapidly advancing and we expect this to have important implications for the asset management value chain. At present, investment activities around fundamental research, security selection, portfolio modelling and trading are being challenged by digital adopters.

Across the value chain we see pockets of innovation, which in itself may not necessarily be ground-breaking, but are interesting signposts to the some of the substantial changes that are coming. For example, in sales distribution and advice, we see that investment offerings are likely to become more targeted and reflective of an investor's financial growth and risk appetite. This would involve advanced data mining or machine learning techniques to understand the investor’s financial goals and risk appetite. In the case of research, both retail and institutional investors now have access to resources that can enable them to gauge and evaluate market sentiment. They also have access to more specific market intelligence based on alternative data sources including social media, geospatial data sets and in some cases the ability to crowdsource investment strategies. Finally, we are seeing firms researching and applying machine learning algorithms for more accurate classification, regression and clustering of data and these are being applied to specific investment activities such as security selection, portfolio construction and also risk budgeting.

Whilst AI techniques are not necessarily new, the phenomenon occurring here is the growing availability and access to computational resources that are driving more experimentation and also innovation across different industries.

We are seeing some forward-thinking firms re-evaluating their business processes to leverage emerging technologies, but in our experience, these new technologies lend themselves specifically to particular problems in niche functional areas. For example, portfolio construction and optimization using technologies like AI, machine learning and big data, and are being explored by a number of retail as well as institutional asset managers. It is also worth noting that the sheer access to resources and a higher level of computing power is changing the way things are done. More and more firms are demanding real-time and on-demand PnL and risk for OTC asset classes. In in the past we had only seen this demand for securities but now there is a demand even for OTC derivatives because there are resources available to crunch large volumes of data. There is also demand for more computationally intensive analysis, for example, simulation based multi period stress and rebalance cycles to do better liquidity risk management.  We have had large asset managers asking for forward-looking stress testing, not just limited to stressing the portfolio but also incorporating the rebalancing response across multiple cycles which is something that is now possible because of the computational power that is available.

A recent survey conducted by Celent highlighted that some firms are still experiencing rudimentary pain points related to conventional data and analytics enablement across different firms. Portfolio managers are facing barriers to access consistent portfolio and risk analytics that are interactive, granular, multi-dimensional and based on fresh data.


What are the top challenges to manage risk?


What are the top challenges to manage risk?
 

The emphasis here is on achieving a firm-wide view of exposures, risk and performance as well as reducing inefficiencies and cost associated with data aggregation across the firm on a larger scale.

On both fronts some of the fundamental challenges faced by portfolio management and risk functions cannot be solved merely by adding more functionality. Firms need to adopt an ecosystem based approach to reduce the application footprint and to centralize core investment data set in order to realize the full potential associated with next-generation digital technologies and approaches. Firms will also need to have a good understanding of what a fit-for-purpose application and data ecosystem should look like for their organisation. This is not merely to achieve a leaner cost base but also to set down modern technology foundations in order to embrace the new capabilities we referenced earlier.

Pursuing fit-for-purpose solution strategies: Key take outs from Celent

Firstly, when selecting solutions, Celent recommend that targeting 80% of functional coverage is often a good rule to follow but they would also advise firms to be cognizant of how quickly the solution can actually be adapted. Certain applications may have less extensive functional coverage but their systems architecture and data foundations, for example microservices or native cloud design, can play to your advantage in the longer run in terms of adaptability and also total cost of ownership.

The investment technology provider ecosystem we have seen over the last few years is also increasingly bifurcating into integrated platforms versus best-of-breed providers. Hence, how your firm selects, integrates or partners with the changing technology provider market will depend on how you envisage a fit-for-purpose operating model within your own firm.

In recent studies Celent highlighted the main archetypes that they consider fit for purpose. These are usually nuanced by an investment firms’ strategies, their asset composition and also legacy technology starting point. Celent would often advise clients to take steps to evolve towards one of these operating blueprints as consistently and practically possible.

Thirdly, in deciding on next generation platforms, Celent typically advises clients not to let functional use cases and features halt the entire discussion. They advise firms to consider elements that would enhance the maintainability, adaptation and future proofing of their investment before making a decision.

The final point is that in a convoluted and converging vendor landscape, which is especially pertinent for a portfolio and risk management systems, Celent asks firms to ensure that they understand a vendors’ DNA and solutions heritage as well as the trajectory of where roadmaps and investments for specific products are headed. In most cases firms can select a technology provider for current functional needs and to some degree future functionalities. However, a platform’s architectural foundations and other intangible factors are likely to determine how quickly your firm will be  adapt, extend or expand into the future and this is becoming an important priority for more forward-thinking firms.

 

Read Part 1: What Trends are Impacting Asset Managers and Asset Owners?