This article was originally published to Global Custodian on July 26, 2024.
The role of market infrastructures has dramatically shifted over the past 25 years as technology continues to evolve, altering business models and competition. For example, the value proposition of the stock exchange has transformed from a venue of listing and trading into a critical source of market information. In 1993, the Stockholm Stock Exchange was the first to demutualise, becoming a for-profit entity. By the end of the 1990s, most major, stock exchanges followed suit and focused on low-latency technology and data services. For example, LSEG recently reported that exchange activity represents 19% of the group’s income, while data and analytics represent 66% of their business. In less than 30 years that followed, every large exchange group fundamentally transformed its business model from offering transactional services to providing information services.
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One of the most significant shifts during that time occurred after the 2008 financial crisis when the industry experienced 15 years of fee compression as competition between asset managers and custodians focused on lower pricing. Their businesses implemented offshoring, automation, modernisation and other cost-reduction strategies to maintain margins. Market leaders recognised the effectiveness of these cost-cutting initiatives, and realised they must also generate new value, especially as settlement and payments became increasingly commoditised.
Asset servicing was introduced as one of the value-generating offerings and has continued to evolve to meet customers’ growing demands, with a strong focus on data discovery and self-service tools, to access transactional and position data. Data has become a competitive differentiator and asset servicing institutions are beginning to follow the exchange model by designing innovative information products to complement transaction services. Banks are evolving from safekeeping and payment-based transactional providers into data service providers that enable investors by providing better insight into valuation, price discovery, liquidity, operational efficiency and risk management. In addition, these data assets and information services expand banks’ front-office relevance.
Competitive forces are also driving banks, that were historically slow to adopt technology, to function differently. Cloud-based services have, in part, accelerated bank innovation by lowering the cost and complexity of developing analytics from data, allowing banks to leverage information available within their existing infrastructure. At the same time, cloud-native application providers are creating data management tools and ecosystems that make it easier to capture and curate insight from transactions, positions and reference data. Cloud ecosystem applications also enable firms to utilise ubiquitous tools and modern programming languages while enabling shared data environments between banks and their clients. Ultimately, as firms move historical data onto modern platforms, they can develop “data-centric” and “data-driven” value strategies by unlocking historically fragmented enterprise content to discover new insights by leveraging artificial intelligence and machine learning.
As historic transactional activity data becomes more accessible, the demand for it grows due to its potential to derive statistical meaning while improving the accuracy and integrity of essential functions such as pricing, liquidity management, and risk. Given the fragmentation in the market structure of most asset classes, post-trade infrastructure represents a natural aggregation point for empirical transaction data. DTCC, and other Central Securities Depositories (CSDs), are creating new value for stakeholders by using collective data assets derived from asset servicing and helping banks better serve their asset management clients.
The financial services industry is at a pivotal moment for innovation and transformation, with all roads leading to data. While the future impact of AI on the industry is still uncertain, it is clear that “Large Language Models” require insight and training through the implementation of trusted, historical, empirical and unbiased transaction data. The data services evolution is reshaping how all CSDs can create client value from their collective data assets. Asset servicing will always have transactional banking as its core, but technological innovation, market pressure, and competition are requiring banks, and the financial industry as a whole to harness the power of data to gain new insights that propel strategy and improve client service and value.