Iowa Department of Corrections Annual Performance Report, FY2006

Agency Performance Repor

An evolutionary theory of systemic risk and its mitigation for the global financial system

This thesis is the outcome of theory development research into an identified gap in knowledge about systemic risk of the global financial system. It takes a systems-theoretic approach, incorporating a simulation-constructivist orientation towards the meaning of theory and theory development, within a realist constructivism epistemology for knowledge generation about complex social phenomena. The specific purpose of which is to describe systemic risk of failure, and explain how it occurs in the global financial system, in order to diagnose and understand circumstances in which it arises, and offer insights into how that risk may be mitigated. An outline theory is developed, introducing a new operational definition of systemic risk of failure in which notions from evolutionary economics, finance and complexity science are combined with a general interpretation of entropy, to explain how catastrophic phenomena arise in that system. When a conceptual model incorporating the Icelandic financial system failure over the years 2003 – 2008 is constructed from this theory, and the results of simulation experiments using a verified computational representation of the model are validated with empirical data from that event, and corroborated by theoretical triangulation, a null-hypothesis about the theory is refuted. Furthermore, results show that interplay between a lack of diversity in system participation strategies and shared exposure to potential losses may be a key operational mechanism of catastrophic tensions arising in the supply and demand of financial services. These findings suggest new policy guidance for pre-emptive intervention calls for improved operational transparency from system participants, and prompt access to data about their operational behaviour, in order to prevent positive feedback inducing a failure of the system to operate within required parameters. The theory is then revised to reflect new insights exposed by simulation, and finally submitted as a new theory capable of unifying existing knowledge in this problem domain

Big Data in Finance: Highlights from the Big Data in Finance Conference Hosted at the University of Michigan October 27-28, 2016

How can financial data be made more accessible and more secure, as well as more useful to regulators, market participants, and the public? As new data sets are created, opportunities emerge. Vast quantities of financial data may help identify emerging risks, enable market participants and regulators to see and better understand financial networks and interconnections, enhance financial stability, bolster consumer protection, and increase access to the underserved. Data can also increase transparency in the financial system for market participants, regulators and the public. These data sets, however, can raise significant questions about security and privacy; ensuring data quality; protecting against discrimination or privacy intrusions; managing, synthesizing, presenting, and analyzing data in usable form; and sharing data among regulators, researchers, and the public. Moreover, any conflicts among regulators and financial firms over such data could create opportunities for regulatory arbitrage and gaps in understanding risk in the financial system. The Big Data in Finance Conference, co-sponsored by the federal Office of Financial Research and the University of Michigan Center on Finance, Law, and Policy, and held at the University of Michigan Law School on October 27-28, 2016, covered a number of important and timely topics in the worlds of Big Data and finance. This paper highlights several key issues and conference takeaways as originally presented by the contributors and panelists who took part

The Role of the Financial Sector in Economic Performance

What distinguished financial institutions from other firms is the relatively small share of real assets on their balance sheets. Thus, the direct impact of financial institutions on the real economy is relatively minor. The indirect impact of financial markets and institutions on economic performance is extraordinarily important. The financial sector mobilizes savings and allocates credit across space and time. It provides not only payment services, but also enables firms and households to cope with economic uncertainties by hedging, pooling, sharing and pricing risks. An efficient financial sector reduces the cost and risk of producing and trading goods and services and thus makes an important contribution to raising the standard of living. The authors begin their analysis by considering how an economy would perform without a financial sector and then proceed to introduce a simplified financial sector with direct financial transactions between savers and investors. Financial intermediaries are introduced which transform the direct obligations of investors into indirect obligations of financial intermediaries which have attributes that savers prefer. This approach emphasizes how the financial sector can improve both the quantity and quality of real investment and thereby increase income per capita. The authors then consider the role of government in supporting an efficient financial sector. However, the authors show that not all government intervention is beneficial. They demonstrate the potentially detrimental effects of regulation on both the financial structure and the real economy. They also emphasize the competitive forces that influence the ultimate impact of regulations. Technological trends in telecommunications and computation seem likely to increase the ease with which users and providers of financial services can circumvent burdensome regulations, according to the authors. This has led to calls for reduction in the overall restrictions on financial firms, as well as for international harmonization of regulations regarding safety and soundness, insider trading and taxation. The authors examine how to quantify the gains to the economy from improving the efficiency of the financial sector and the potential social gains and costs which may result from the formation of financial conglomerates. The authors then consider pressures for international harmonization of financial regulation, contrasting institutional regulation with functional regulation. The authors conclude that efficient financial markets require an infrastructure of laws, conventions and regulation. Most of all, an efficient financial system requires confidence. Confidence encourages investors to allocate their savings through financial markets and institutions rather than to buy non-productive assets as a store of value. The authors suggest that such confidence can be fostered by appropriate regulation of institutions and markets to ensure users of financial services that they will receive fair treatment. According to the authors, the challenge is to foster a static and dynamically efficient financial system while maintaining sufficient regulatory oversight to promote confidence in the safety and soundness of the financial system.

Business Intelligence in Banking: a Literature Analysis from 2002 to 2013 using Text Mining and Latent Dirichlet Allocation

Abstract This paper analyzes recent literature in the search for trends in business intelligence applications for the banking industry. Searches were performed in relevant journals resulting in 219 articles published between 2002 and 2013. To analyze such a large number of manuscripts, text mining techniques were used in pursuit for relevant terms on both business intelligence and banking domains. Moreover, the latent Dirichlet allocation modeling was used in order to group articles in several relevant topics. The analysis was conducted using a dictionary of terms belonging to both banking and business intelligence domains. Such procedure allowed for the identification of relationships between terms and topics grouping articles, enabling to emerge hypotheses regarding research directions. To confirm such hypotheses, relevant articles were collected and scrutinized, allowing to validate the text mining procedure. The results show that credit in banking is clearly the main application trend, particularly predicting risk and thus supporting credit approval or denial. There is also a relevant interest in bankruptcy and fraud prediction. * Corresponding author (S. Moro). Email addresses: [email protected] (Sérgio Miguel Carneiro Moro), [email protected] (Paulo Alexandre Ribeiro Cortez), [email protected] (Paulo Miguel Rasquinho Ferreira Rita) Preprint submitted to Expert Systems With Applications September 1, 2014 Customer retention seems to be associated, although weakly, with targeting, justifying bank offers to reduce churn. In addition, a large number of articles focused more on business intelligence techniques and its applications, using the banking industry just for evaluation, thus, not clearly acclaiming for benefits in the banking business. By identifying these current research topics, this study also highlights opportunities for future research

Approaching algorithmic power

Contemporary power manifests in the algorithmic. Emerging quite recently as an object of study within media and communications, cultural research, gender and race studies, and urban geography, the algorithm often seems ungraspable. Framed as code, it becomes proprietary property, black-boxed and inaccessible. Framed as a totality, its becomes overwhelmingly complex, incomprehensible in its operations. Framed as a procedure, it becomes a technique to be optimised, bracketing out the political. In struggling to adequately grasp the algorithmic as an object of study, to unravel its mechanisms and materialities, these framings offer limited insight into how algorithmic power is initiated and maintained. This thesis instead argues for an alternative approach: firstly, that the algorithmic is coordinated by a coherent internal logic, a knowledge-structure that understands the world in particular ways; second, that the algorithmic is enacted through control, a material and therefore observable performance which purposively influences people and things towards a predetermined outcome; and third, that this complex totality of architectures and operations can be productively analysed as strategic sociotechnical clusters of machines. This method of inquiry is developed with and tested against four contemporary examples: Uber, Airbnb, Amazon Alexa, and Palantir Gotham. Highly profitable, widely adopted and globally operational, they exemplify the algorithmic shift from whiteboard to world. But if the world is productive, it is also precarious, consisting of frictional spaces and antagonistic subjects. Force cannot be assumed as unilinear, but is incessantly negotiated—operations of parsing data and processing tasks forming broader operations that strive to establish subjectivities and shape relations. These negotiations can fail, destabilised by inadequate logics and weak control. A more generic understanding of logic and control enables a historiography of the algorithmic. The ability to index information, to structure the flow of labor, to exert force over subjects and spaces— these did not emerge with the microchip and the mainframe, but are part of a longer lineage of calculation. Two moments from this lineage are examined: house-numbering in the Habsburg Empire and punch-card machines in the Third Reich. Rather than revolutionary, this genealogy suggests an evolutionary process, albeit uneven, linking the computation of past and present. The thesis makes a methodological contribution to the nascent field of algorithmic studies. But more importantly, it renders algorithmic power more intelligible as a material force. Structured and implemented in particular ways, the design of logic and control construct different versions, or modalities, of algorithmic power. This power is political, it calibrates subjectivities towards certain ends, it prioritises space in specific ways, and it privileges particular practices whilst suppressing others. In apprehending operational logics, the practice of method thus foregrounds the sociopolitical dimensions of algorithmic power. As the algorithmic increasingly infiltrates into and governs the everyday, the ability to understand, critique, and intervene in this new field of power becomes more urgent