For Financial Services organisations, efficiently managing data is essential.
Not only can sound data management create efficient operational processes, which have a substantial impact on a company’s bottom line, it can also help organisations make market-leading decisions driving top-line growth.
Unfortunately, many firms simply lack the technical firepower to get to grips with their data. Reliant on legacy technology and slow change processes, many organisations find that, despite years of work, and millions of pounds of investment, their technology is still not delivering the portfolio management capabilities they need.
However, thanks to SaaS technology, organisations now have the opportunity to achieve their potential. By unlocking the power of data, firms are increasingly realising the benefits as a force behind business growth and alpha generation.
Manage Multiple Asset Classes in a Single System
Constrained by rigid data models in their legacy systems, fund managers have often had to grapple with technologies that are unable to handle multiple asset classes in a single place. As managers look to diversify their portfolios, SaaS technologies offer flexible solutions that are able to deal with almost every asset class in both public and private markets.
Having one, flexible system that can deal with all assets removes the need to pay for separate systems for each asset class (e.g. private vs. public), and also takes away the burden of manual intervention. Portfolio Managers can, therefore, invest more time in exploring revenue growth opportunities, for example, portfolio scenario building and what-if analysis, to deliver a wider range of products and services to clients.
Trust in New Data Sets
Portfolio Managers have access to a broader variety of data than ever before. However, bringing data into a single system to gain an accurate view of their data is near-impossible. As a result, users are typically slow to establish trust in new data sets, leading to operational inefficiencies and prolonging the investment decision-making process.
To accelerate and enhance the integration of diverse data sets for value extraction, the use of Machine Learning (ML) is becoming more widespread across the industry. A scalable data architecture fused with data virtualization is crucial to supporting the ML Operations Cycle, such as data preparation. Data virtualization allows for new data sources to be connected from source systems in real-time, allowing Portfolio Managers to rapidly identify patterns, trends and correlations for deeper data-driven insights and informed decision-making.
Over-reliance on Closed Systems
Legacy systems lack the flexibility to ingest and master increasing volumes of complex investment data. However, interoperable SaaS technology enables financial and non-financial data to be ingested, cleaned and mastered from any source in any format.
As firms embark on technology transformation, this system interoperability provides the flexibility to outsource elements of the trade lifecycle processing that are critical to business operations, such as a pre-trade compliance engine, whilst retaining their preferred operating model. This enables firms to preserve their proprietary investment process, such as building next-generation products and services for alpha-generating investment strategies.
SaaS data mastering capabilities present systems and decision-makers with every piece of data they have about security, enabling them to take advantage of all the information available to make the best decisions. It also improves efficiency and operational performance as users can seamlessly clean data with highly configurable workflows that can either automatically resolve issues in line with business rules or prompt a human to manually intervene.
Leveraging SaaS Technology to Transition Away from Legacy Systems
Most organisations find that their existing technology lacks the flexibility to adapt to market changes or business requirements such as mergers and acquisitions.
SaaS technology provides the critical functionality needed to bridge the gap between new and existing systems, allowing users to enhance functionality whilst interacting with existing technology, algorithms and processes. This allows firms to take a low-risk approach to technical transformation, so that they can drive out operation inefficiencies whilst establishing a sustainable framework for future growth.
To learn more about FINBOURNE’s Portfolio Management capabilities, click here.
