Financial Analysis and Risk Management

This chapter provides an introduction to the papers in this volume, which are based on the workshop on Records and Information Management for Financial Risk Analysis and Management, held in Vancouver, Canada, 24–25 August 2011. The chapter contextualizes the papers presented at the workshop, and included in this volume, in relation to international initiatives to improve the transparency of global financial markets and to address information gaps identified as having contributed to or exacerbated the global financial crisis of 2007–2009. The chapter highlights the novel contributions made by each of the papers, and concludes with an examination of future research directions for an emergent field of financial records and information management.

We argue that the single largest operational risk (OR) for the Office of Financial Research (OFR) is to be inundated with indecipherable financial contract data. Clear semantics on the attribute level alone is not sufficient for risk avoidance; the OFR needs a semantic system capable of describing the entire intent of the financial contract. We introduce the concept of Contract Types (CT), which encapsulates this semantic. The idea of CTs overrides the cherished yet unexamined separation of data and algorithms, which lies at the core of the observed data chaos in banks. We distinguish the mechanical parts of finance, where separation is counterproductive, from the subjective parts, where a separation makes sense. We conclude with a model that contributes to an operational OFR.

Financial records can be used to help identify operational and systemic risks associated with financial transactions since they provide evidence of how a transaction may occur. They are necessary for regulation of the financial system and in order to assert legal and financial claims. In some cases financial institutions do not adequately record information, which can lead to many problems, as was the case during and in the aftermath of the financial crisis of 2007–2009. In these cases it would be useful to understand exactly how missing or incomplete records affected the situation, why they were missing or incomplete, in what ways this situation may have contributed to risk buildup in the financial system, and how the situation could be rectified or even prevented. In this study we suggest using conceptual modelling, a technique more often used for systems development, to gain an understanding of financial records creation, transmission, and management in the processes along the private label residential mortgage-backed securities originate to distribute supply chain. Conceptual modelling is the act of representing the physical and social domain using specific abstractions of it. The study discusses how three different conceptual modelling techniques were used to explore the relationship between records and risk in the context of the financial crisis. From experiences with using these different conceptual modelling approaches, we conclude that conceptual modelling is valuable as a tool to help understand and model relationships and dynamics in financial risk management and as a tool to generate new insights that would otherwise have been more difficult to see.

Understanding how individuals and organizations make financial decisions under uncertainty and with different information settings is fundamental to informing the theory and practice of information management. Due to limitations on cognitive ability and problems of information overload, complex information sets may not be fully understood, resulting in suboptimal economic decision-making. We have applied visual analytics (VA), which enables users to interactively discover information from large information sets, to improve the financial decision-making process. Using an experimental methodology, we find evidence that VA reduces the cost of obtaining information, improves decisions, and increases confidence of users in a range of different financial decision tasks involving risk. This is a nascent area of research, and additional work is needed to develop and evaluate VA tools for financial decision-making and risk management. Best practices guidelines for presenting complex information sets may only develop through rigorous evaluation of the effect of information presentation on actual choice. In addition, the impact of VA in collaborative decision-making environments is not fully understood. The future of applied VA for financial decision-making and risk management must involve an interdisciplinary team of behavioural economists, VA researchers, computer scientists, and cognitive scientists.

In this study, we aim to understand and bridge the gap between visual analytics (VA) research and deployment in imperfect conditions to solve multilayered, often vaguely defined, problems in the real world. We further narrow the scope to analysis problems in finance, with a focus on investment portfolio analysis. The goal of this project is to create a functional evaluation framework of VA techniques with regard to investment portfolio analysis problems, as well as a table of existing products that are capable of supporting problems. With a functional evaluation framework and a table of off-the-shelf solutions, more effective and theoretically grounded cost-benefit analysis can be performed to justify and plan applications of VA in financial organizations. We then apply this functional evaluation framework in a case study of a fixed-income investment management company. In this case study, we systematically identify the areas for improvement in the analytic process of the company and isolate the areas that could be improved with VA. We then map these detailed problem definitions to VA techniques in order to find the optimal visualizations of the data. Finally, we implement a solution for the company by building upon one of the free toolkits that we have evaluated in order to achieve all the analytic goals with the least amount of time and expenses.

The aim of this chapter is to connect the representational theory of financial records with concepts related to the interplay between sociality and materiality in the techno-organizational settings of financial operations. At a second stage the aim is to apply this conceptual framework to the analysis of two critical operational domains: proprietary trading and custodial services. Proprietary trading is driven by fogginess of information resulting from blackboxing as a way to cope with capital market and operational complexities, as well as with time pressures. On the contrary, custodians struggle to restore the visibility of financial records. Furthermore, the awareness of messiness and fogginess, as central components of information management in finance, is related to the informational consequences of financial bubbles originating especially from high-leverage and derivative-driven operations. Cognitive framing, adopted by professional groups and communities of practice in financial organizations, often results in foggy representations that obstruct the view of the primary sources of information stored on financial records. Institutionalized and formalized expertise can under certain circumstances cause fogginess and hinder reflexive approaches regarding the way recorded information is shaped and used. Fogginess implies also messiness of entries and of data organization as a consequence of reduced visibility of the features of the records and of the subsequent inscriptional requirements. This analytic perspective can be applied beyond proprietary trading and custodial services, to other communities of practice in finance facing problems of information management and thus exposed to high operational risks.

The InterPARES (International Research on Permanent Authentic Records in Electronic Systems) project has been running consecutively for 12 years with an enduring research interest in the issues and challenges imposed by digital records as to their management and preservation. During the three phases that constitute the 12-year research period, the project developed theoretical and methodological knowledge at its first and second phases and applied the developments to practical settings at its third phase. This chapter presents the findings and products of the third phase and analyzes them in relation to preservation foundation, preservation mechanism, and preservation technological system. The analysis established that the situation of digital records preservation in the participating organizations is challenging due to weak or even lack of preservation foundation. It is believed that the analysis could shed light on future actions that financial regulators and market participants might take to ensure quality maintenance and long-term preservation of financial records.