It’s a pleasure for me to introduce the first of two special issues of Cutter Business Technology Journal (CBTJ) showcasing the thought leadership and cutting-edge research and development (R&D) being done in State Street Corporation’s Advanced Technology Research Centres in Europe, the Middle East, and Africa (EMEA) and Asia Pacific (APAC), in partnership with University College Cork (UCC) and Zhejiang University (ZJU), respectively. The articles in this issue represent a small sample of the output from the R&D undertaken in these centers, which combine academic excellence with real industry impact.
I want to begin by providing some context and some insights as to how academia and industry can collaborate in a sustainable manner. The collaboration between State Street, UCC, and ZJU offers a successful model of how institutions of higher education and business organizations can successfully conduct R&D that benefits both academia and business.
Having reviewed various academic/industry collaboration models, I have observed that many extant models struggle to achieve a balance between academic excellence and business relevance. Finding a model that enables all parties to successfully work together for mutual benefit is something that continues to perplex many would-be collaborators. For academics, the primary objective is publishing in highly ranked peer-reviewed journals, while business leaders are primarily concerned with ensuring that the R&D they are funding has real business impact. Therefore, many collaborative R&D initiatives are not sustainable in the medium and long term due to this misalignment between the parties’ respective objectives.
The model implemented by State Street in its Advanced Technology Centres in UCC and ZJU is an exemplar in that the products of the R&D efforts include thought leadership pieces in highly ranked peer-reviewed journals and conference proceedings, industry white papers and journal articles (such as those included here), patents, cutting-edge proofs of concept, and software applications that have been implemented by the business. In the case of the papers and articles produced, they are coauthored by academic and industry colleagues. Furthermore, the research efforts have informed and benefited multiple stakeholders in the financial services ecosystem, including international standards bodies, customers, other financial services companies, and academia.
The four articles published in this issue discuss some of the key technologies that will be of significant relevance to the future of financial services and, potentially, other domains. Specifically, they focus on semantic ontologies, next-generation robo-advisors, tools supporting internationalization and localization of legacy systems, and 3D visual analytics.
While much of the emphasis in fintech R&D remains on emerging technologies, the industry is also looking at how to efficiently integrate various technologies, platforms, and systems together. The financial services industry is a sector where systems integration is a significant issue, due to ongoing technological developments over multiple decades. Financial services organizations must not only stay abreast of emerging fintech technologies, but also effectively integrate them with legacy information systems within an organization’s existing IT ecosystem.
In this dynamic and rapidly evolving ecosystem, efficiently and effectively integrating data from the multiplicity of systems that exist is a significant challenge. Furthermore, CIOs and CTOs must continuously wrestle with the dilemma of when to invest — and in what technologies. Therefore, they face ongoing issues pertaining to standards and systems integration. This is where semantic technologies can play a key role. Through applying semantic technologies and adding a semantics layer to an organization’s IT architecture, the organization’s data can remain in its existing system (legacy information system, data warehouse, database, Excel file, etc.) and its existing format (structured, unstructured). By using triple store database technology, data can be transformed into RDF graphs, an industry standard model for data interchange. A critical advantage of this approach is that an organization does not need to copy the entirety of the data universe into a triple store database; only data necessary for the specific use case is transformed.
This approach creates a map of an organization’s data, encompassing its characteristics — specifically its data types, properties, and interrelationships — together with an explicit specification of the intended meaning of the vocabulary. Therefore, the semantic data model, through associating meaning with each piece of data, enables faster, better data analysis. The critical advantage of semantic ontologies is not just that they enable data to remain in its host system, but that they also support data standards, facilitating much greater levels of interorganizational data interchange. Furthermore, semantic technologies can play a vital part in dynamically identifying anomalies in an organization’s data set through the utilization of inference engines. Among other things, this enables much better data quality for regulatory reporting.
The semantic ontologies approach has huge business value. Critically, when the next generation of fintech technology comes along, an organization won’t have to undertake expensive system migration projects. For example, while semantic ontologies can be applied to existing systems, they will also help to maximize organizational value from distributed ledger (aka blockchain) technology.
Due to their potential for disruption, blockchain-based systems are attracting a massive amount of interest and investment. However, blockchain technology in and of itself does not address core issues pertaining to data quality and seamlessly facilitating exchange of data that may exist off chain. Therefore, R&D efforts are now looking beyond blockchain as a standalone technology, focusing on how a semantic layer can sit upon a blockchain-based system. A critical advantage of this approach is that it would facilitate a standard semantics-based data dictionary for multiorganizational data and assist with data quality issues, thereby offering a much stronger business case for the implementation of this technology.
In the context of decision support, robo-advisors have received a great deal of attention for their potential to assist both individual and institutional investors with their investments. Indeed, much of the recent focus has been on how these technologies should be operationalized, be it in a fully automated online model or via a hybrid model, whereby the technology is utilized by investment advisors to better inform their clients. Yet from a usability perspective, most of these systems are limited in that their interfaces are typically two-dimensional and text-based. Looking toward the design of the next generation of robo-advisors, researchers are exploring how big data, analytics, and 3D visualization techniques can be combined and integrated to operationalize greater analytical and predictive value. Not only is it possible to use these systems to track the performance of “star traders” and efficiently communicate and illustrate this using 3D visualization techniques, these systems can also predict the future of investment markets. By combining technologies, such systems will play a critical role in decision support by visually informing investors’ investment decisions.
Now let’s explore the articles in our current edition of CBTJ in more detail.
In This Issue
In the wake of the financial crisis of 2007-2008, calls for further regulation of the financial services sector intensified. But as the authors of our first article — Oliver Browne, Nenad Krdzavac, Philip O’Reilly, Mark Hutchinson, David Saul, Dáire Lawlor, and Daragh McGetrick — observe:
For financial institutions, regulatory reporting has become something of a jigsaw puzzle — one that must be cobbled together into a coherent picture from several boxes into which the pieces from different puzzles have been put over time, for an audience that will never appreciate the pain involved in organizing that picture or the time and manpower required to build it.
Assembling this puzzle has often meant using Excel macros to pull data from various silos and aggregate and verify it manually, a process that is prone to errors and omissions. Fortunately, the authors argue, there is a better way. Browne et al. introduce FIBO (Financial Industry Business Ontology), “a standard financial ontology language being developed to enable a common understanding among financial institutions” that would “allow aggregation and comparison of data from all institutions on a like-for-like basis and enable [regulators] to fully grasp counterparty risk exposures.” They make a persuasive case that a flexible ontology-based approach to regulatory reporting will benefit financial institutions and regulators alike.
In our next article, Jie Yang, Hanxi Ye, Yadan Wei, and Linqian Bao tell us about a hot topic in the fintech world: robo-advisors. These automated, online portfolio managers are making a splash because they offer users entrée to the investment world with lower fees, lower account minimums, innovative features, and user-friendly interfaces. Managing an investment portfolio with little or no human intervention requires finding ways to determine investor goals, identify and allocate assets in pursuit of those goals, and monitor and rebalance portfolios. Yang et al. explain what it takes for robo-advisors to do what they do, bearing in mind both the human and technical perspectives.
Benjamin Franklin famously said that “in this world nothing can be said to be certain, except death and taxes.” Many IT practitioners might be tempted to add “and legacy systems.” In our third article, Bo Zhou and Lucy Chen note that “some legacy systems developed between the 1980s and 1990s did not take software internationalization and localization into consideration, yet for various reasons ... are still in service around the world.” In an age when software applications are distributed worldwide as a matter of course, lack of multi-language support is unacceptable. Zhou and Chen tell us about a framework and tool supports they and their colleagues used to internationalize and localize a large-scale fund accounting legacy system at State Street Corporation. With the help of their source code ranker and automated code search tool, they were able to complete roughly 80% of the reengineering work automatically. Should you wish to replicate their success, the authors assure us that “this framework could be used for other internationalization and localization projects.”
Our final article proves the adage “A picture is worth a thousand words.” When trying to analyze large-scale, complex temporal data, columns of figures just won’t do. Thus, authors Jerry Cristoforo, Qiao Huang, Zhiyu Peng, and Xiaohu Yang introduce Apsara, an interactive visual analytics (VA) system for multidimensional temporal data. After canvassing the work that’s been done in visualization methods, systems, and tools, the authors describe how Apsara presents “large-scale data in a vast universe ... enhanced by color, motion, and sound.” They explain how Apsara implements two critical facets of effective VA systems — customizable visualization and interactive design — to yield “insight into patterns, trends, and correlations in the data.” These capabilities are called into service in a case study in which they use Apsara to visualize China’s mutual fund market, both to compare it to the mutual fund market in the US and to predict the growth of China’s QDII (Qualified Domestic Institutional Investor) program. Like the authors that precede them, they promise that “this customizable solution can be adapted to different data models and applied to multiple domains.”
The articles in this issue of CBTJ offer significant insights on key emerging technologies and how they can be integrated together to create substantial business value. They present operational techniques, frameworks, and models that can be used by organizations and stakeholders in the financial services sector and beyond. Finally, they illustrate the value of the R&D model that State Street has implemented in partnership with University College Cork and Zhejiang University, which has resulted in high-impact R&D on the cutting edge of technological innovation in financial services. We hope you will enjoy this journey to the fintech frontier.
