Increasing margin and cost pressures on buy-side participants continues to challenge firms to find ways of improving their performance by driving organisational transformation, focused on reducing costs and reevaluating growth strategies, in an attempt to differentiate themselves in a highly competitive market.
Whether it be generating alpha, optimising operational performance, improving customer experience or anything in between, data and investment in data analytics is at the heart of driving long term performance and profitability improvements.
A recent study outlined that 90% of all of the data in the world, has been created in the last four years. Much of that data was raw, it needed to be cleansed and because there was so much data it was impossible to develop a strategy on how to use it effectively. Consequently, firms have been dutifully collecting data, that they have generated both themselves and through investment in market data feeds and essentially hoarding it, often without adequate investment in their data programs and enough data literacy experience to access and unlock its true value.
With an increasingly more competitive environment, we are seeing a paradigm shift, with more and more buy-side firms viewing data less as a purely functional asset, or a means to an end. They are taking a step back to reassess their data strategy, enabling them to harness the power of their data to establish a sustainable infrastructure for growth, transformation and providing them with a stronger foundation that allows them to leverage emerging digital technologies with more agility.
We believe that the buy-side firms that are embracing the data and digital disruption wave, by executing foundational data programs, will emerge from the pack and will be able to differentiate themselves with more complex products, sophisticated value-added services and customer centric user experiences, whilst strategically reducing costs.
Outlined below are some of our observations and examples of where firms are benefiting from robust data programs:
Strategic cost reduction:
As low margins continue to thwart the industry, firms are looking to reduce soaring technology costs. They are encumbered by legacy technology infrastructure, often finding it difficult to keep up with the ever-increasing pace and cost of innovation to enable and maintain these platforms. Consequently, they are now looking to cloud-based solutions.
Buy-side participants are also beginning to rationalise their core processing platforms, such as order management systems (OMS), investment book of records (IBOR) platforms, with an increasing number of firms considering a cloud based platform-as-a-services (PaaS) solution, leveraging the efficiency that these platforms bring to reduce spend. Other benefit realisation includes improved data integration and greater on-demand access to data.
An initiative that Asset Managers are focusing on to reduce costs is the utilisation of intelligent data tools and data cleansing techniques to optimise their operations, reducing unnecessary steps in the end-to-end process, and improving accuracy. These data centric tools allow operations teams to use a low code approach to digitising structured and unstructured data, effortlessly enrich data in an intelligent way and to leverage machine learning algorithms.
Time to market – data accessibility
Many firms have established aspirational roadmaps to digitally enable their business and are now “looking under the covers” to determine precisely what they need in order to establish an open ecosystem, that will allow them to crystalise their plans. We have determined that firms are ‘API enabling’ their application stacks to provide improved accessibility to comprehensive real-time reporting across front, middle and back office processes.
In this highly competitive market, the agility and availability of data in a self-service environment is increasing the ability to monetise data, offer differentiated solutions and to provide insights to investors.
Innovation
Innovation is undoubtedly the impetus of the data movement. But without a comprehensive, executable end-to-end data strategy, driven by strong governance, a cleaner data set, more robust business intelligence (BI) tools, and innovations in data analytics, the timely and successful execution of innovation is often threatened with delivery challenges. The increasing number of similar types of data sets, improvements in the quality of those data sets and the evolution of data cleaning techniques tools has resulted in cleaner inputs. Whilst buy-side innovation is largely focused on providing differentiated services and solutions to create alpha, firms are also looking to innovate their product lineup, which is often heavily dependent on data sources.
Alternative data sources are becoming more prevalent in the mainstream to create alpha to provide a competitive edge. A Greenwich Associates study stated that 72% of investment firms reported that alternative data enhanced their signal, with over a fifth of respondents saying they got over 20% of their alpha — the ability to beat the market — from alternative data1. This alternative data comes in both structured and unstructured formats and needs to be digested into data infrastructures to leverage the immense business value. Impact investing is a way that firms are innovating their product offering with ESG Strategy Funds, which relies heavily on unstructured inputs. A recent report has suggested that more than a quarter of the world’s $88 trillion in AUM is invested according to ESG principles – a year-on-year rise of 17%. A recent study by US SIF Forum for Sustainable and Responsible Investment estimates $8.72 trillion of ESG investments in the US alone, up 33% from 2014. ESG investing now represents about 20% of total assets under professional management in the US, according to the US SIF data. Firms with ESG strategies are not challenged with matching the structured data and unstructured data inputs to formulate a holistic recommendation. With over 100 providers of ESG data the transparency of source of data and granularity is a challenge many are experiencing.
Whilst some firms have deployed some RPA into their environments, focus has shifted to developing use cases for Artificial Intelligence (AI) and Machine Learning (ML). One of the use cases that JDX has developed is leveraging AI for contract discovery extraction and execution. We believe that this will be a valuable tool in the transition away from LIBOR, providing an end-to-end holistic approach from which firms can drive a successful transition.
In the same vein, we have observed institutional-side firms considering AI for the management and extraction of key data points within lengthy investment agreements. They are using that extracted data to set trade guideline configuration.
Beyond AI, advanced analytics are playing front and center in many new offerings. One particular offering we have observed is the development of a ‘what if, hypothetical analysis’ which provides an institutional client’s ‘peer asset allocation group’ behavior which provides feedback with historical product strategies selections within certain market conditions. Analytics is also at the core of solutions that track performance and liquidity for those investors diversifying into the alternative market, to help them manage their risk. It goes without saying that as the value of AI is unlocked, its use along with analytics is moving well beyond the front office and it’s prevalence along with other emerging technologies and innovation is increasing in the renewal of the back-office ecosystem—the middle office and operations functions of banks are now seeking to use AI and robotics, in real practical application, to reengineer and improve their own processing, whilst improving the client experience.
The use cases for AI is increasing rapidly and it is an exciting time to witness how firms are transforming and driving disruption with this tool.
Customer Experience
In a highly digitised world, investor expectations are mandating an intuitive digital and omni-channel suite, driven by curated experiences and firms are providing tool sets to guide investors through a customised journey. Sentiment and behavioral analysis, underpinned with a rich analytics engine, can move the experience beyond traditional personalisation to a truly individually curated experience. Whilst customer experience has become focused on the availability of web and mobile channels, we have also observed how Asset Management firms have brought that same experience to the contact center platforms, offering AI chatbot features, text, video features, dual browsing with agents and preferred agent profiling based upon profile preference built over time utilising sentiment analysis. The focus on Customer Experience and investment cannot be underestimated and we believe there will be continued growth in this area as firms continue to enable AI and industry tools.
What’s Next
Please look out for subsequent articles providing our insights on how to navigate the challenges of the LIBOR transition, how to implement a comprehensive program to mitigate risks and reduce operational burden by leveraging data digitisation and artificial intelligence.
Share this:
Share on linkedin
Share on twitter
Share on facebook
Related
JDX Establishes a Go Green Society
MONIKA MESKAUSKAITE Breaking the Plastic Habit is a programme which aims to eliminate single-use plastics from stores and businesses at Canary Wharf via new technology,
JDX to support British Schoolgirls Skiing Races 2020
The British Schoolgirl Races, which are organised by the Ladies Ski Club, first took place on the slopes of Gstaad in 1959, moving to Villars in 1976.
Why Letting Data Go Matters to Customers and Companies
MARKUS BUHMANN We hear a lot of talk about how, why and when organisations collect data from individuals.
It’s an attitude.
Have you got what it takes to join the JDX team?
