A Perspective of Why Financial Crises Happen, and How to Predict Them Before They Happen

The rise in the complexity and globalization of financial services has contributed to stronger linkages between various institutions. While this has led to smoother credit allocation and greater risk diversification among individual institutions, it has also increased the potential for disruptions spreading across banks and across borders as exemplified by the recent European banking crisis.  A financial contagion can take the form of shocks to haircuts, which trigger liquidity hoarding at some institutions that propagate across the interbank network. We live in a fascinating data-driven world full of challenges and opportunities – the world of Big Data. This is a world of unmatched availability of data to drive better decision making on a more timely basis but it is also a world where perceptions and panic can spread much faster than at earlier times.

There is a growing body of work studying the role of bank behavior in driving contagion as well as application of network techniques to better understand the probability and spread of contagion.  In these studies, the financial links between individual institutions are often represented as stable, embedded and long-term connections based on random graphs. However, one financial institution can be quite different from another in equity value and liabilities; the capacity of resisting risk impact is quite different. As a result, heterogeneous capacity of nodes is an important feature of real interbank networks versus a random network model. Also, the sequencing of transactions between institutions over time provides valuable insight into understanding the pattern of contagion spread across the network. Finally, the ability to analyze the behavior of interbank network in the context of other data in the media, development of events in regional politics, and impact of financial policy decisions in various countries can provide unforeseen insights.

New generation of advanced analytics, such as visual analysis, can be used to evaluate the resilience of the financial system to liquidity shocks affecting subset of banks. Advanced visual analysis applications can be used to explore the resilience of the financial system to liquidity shocks affecting a subset of banks under different network configurations, degree of connectivity between financial institutions, haircut assumptions, and balance sheet characteristics.  These tools combine advanced graph visualization techniques together with social and dynamic network analysis techniques to provide a comprehensive visual tool for analysts and policy makers.


Big Data = Big Opportunities through Advanced Visual Analytics

We live in a fascinating data-driven world full of challenges and opportunities – the world of Big Data. With the continuing, explosive growth of data in terms of quantity, diversity of sources and types, and speed, there are tremendous opportunities to explore this “Brave New World of Big Data” to discover new insights that were hard to uncover in the past.

In recognition of these new opportunities, more and more organizations are adding data visualization applications powered with advanced analytics, such as Social Network Analysis (SNA), into their portfolio of advanced analytics – what is often called advanced visual analysis. The growing recognition of the role of advanced visual analysis as part of analytics toolkits is highlighted by recent acquisition of i2 Technologies by IBM, growth of visualization based business intelligence products from QlikView, TIBCO/Spotfire, and Tableau as well as the recent move by Facebook to provide Open Graph as part of their makeover. This is indeed an exciting time for advanced visual analysis.

The growing use of advanced visual analysis applications, especially graph-based systems powered with SNA techniques, have led to remarkable recent track record of success in combating global terrorism, cyber warfare, and criminal fraud activities in various industries. New technologies such as pad computing devices, GPS and biometric based systems, and next generation network management systems for integrated communication of audio, data, and video, the continuing growth of online social communities, as well as integrated demand-supply chain systems have led to rise in demand for using these applications to discover insights from not just highly structured data in traditional enterprise repositories but from all data types across all types of information repositories. The increased success of these applications in different industries will continue to fuel the growth in advanced visual analysis applications of all types in the coming years.

The ongoing industry recognition of the power of advanced visual analysis will yield better integrated analytic systems in many cases, better defined visual analysis infrastructure in others, and overall a superior experience and results for organizations focused on maximizing the value of their Big Data.   This is a fascinating and exciting time for those of us involved in visualization, advanced visual analytics, and advanced data analytics of all types. We are entering an era where our ability to discover actionable insights in the right context is leading to a fundamental paradigm shift in how we leverage our data assets to create more agile, efficient, and intelligent organizations everywhere.

Visualizing the Network Map of the News Corp Scandal

The News Corp scandal is quickly snowballing as it spreads into different parts of the world, beginning with News of the World in London. In the past weeks, investigators have been analyzing the messy web of interconnections between organizations and key individuals involved with the scandal in order to indict the appropriate entities.

While there is a barrage of news articles providing the latest updates, it can become difficult to keep track of the exact causes and relationships with each new reported event. BusinessWeek and The Wall Street Journal released the following graphics to visually communicate these relationships in an easier-to-grasp form.

Data Visualization of the News Corp Scandal, Business Week

The Wall Street Journal:
Data Visualization of the News Corp Scandal, Wall Street Journal

In addition to the News Corp scandal, data visualization can often be used to help people better understand other real world developments that involve very complex subsets of relationships between various entities, such as the Galleon Group’s insider trading case and even finding Osama Bin Laden. Applying social network analysis and centrality measures can show who communicates with whom, how often, and how entities are tied together. This helps to reveal varying levels of influence of certain people or organizations that can provide critical actionable insights. Even if there are prominent perpetrators at the forefront of scandals, “hidden” individuals outside the immediate circle of their networks may interestingly come into question due to high centrality measures.

The Shift Towards a Data-Driven World and How to Navigate Through It

One of the latest buzz phrases in the information industry is that we are living in a data-driven world. Increasingly, the efficient operation of enterprise, government, and other organizations relies on the management, understanding, and effective use of vast amounts of data. One of my college professors, the late Nobel laureate and pioneer in artificial intelligence, Prof. Herbert Simon had a saying that “… What information consumes is rather obvious: it consumes the attention of its recipients. Hence a wealth of information creates a poverty of attention, and a need to allocate that attention efficiently among the overabundance of information sources that might consume it …”. His commentary about the need to more efficiently allocate attention to maximize the value of information best describes the growing interest in visualization and analysis applications.

The demand for data visualization and analysis applications is multifaceted and growing rapidly. This trend is consistent with the exponential growth of relevant data available to organizations as noted by former Amazon.com chief scientist Dr. Andreas Weigend, “… In 2009, more data will be generated by individuals than in the entire history of mankind through 2008 …”.  These visualization and analysis applications address a diverse universe of usages from business intelligence to network management and social network analysis. In all these cases, the common denominator is the recognition that visualization and visualization coupled with analysis which leverage people’s ability to better process dynamic images instead of long lists of text and numbers needs to be part of an organization’s tool kit for understanding information better.

Here at Tom Sawyer Software, as well as other companies in the data visualization and analysis market, we have witnessed a dramatic increase in demand for software for building these types of applications. For example, after the recent financial market situation in many countries, there is an increased interest in using graph visualization and social network analysis applications to proactively recognize trends and anomalies in financial institution networks for fund transfers and risk management markets. The interbank markets are pivotal for liquidity management purposes of financial institutions because they allow banks to buffer shocks by permitting rapid transfer of funds from surplus to deficit agents. The ability to recognize potentially dangerous situations and conducting forensic analysis on dangerous situations for root causes is becoming more important.

In the networking and telecommunications industries, there has been a major shift towards IP based, service centric, next generation networks in the last few years. The underlying drivers for the change include competitive pressures, game-changing technology innovations, change in purchasing behavior of consumers, and changes in regional regulations. The virtual layering involved in these networks require far more sophisticated network management tools and increasing number of companies have integrated visualization and analysis capabilities into their management systems. In addition, social network analysis is having an impact on how telecommunications companies leverage their data assets to improve their performance and increase competitive advantages. Social network analysis applications are increasingly being used to study how the network of customers, partners, and community members that exist outside the organization influence the organization to detect customer churn and to better target marketing efforts.

As recent world events have shown, there is a growing use of data visualization and social network analysis in the defense and law enforcement organizations to identify terrorist threats and analyze fraud situations in bank transactions to insider trading. With the growing recognition and proven ROI around these applications, we continue to expect a sustained growth of interest in sophisticated data visualization and analysis applications and software for building these applications.

Visualize and Understand Complex RDF Data [Video]

Resource Description Framework (RDF) provides the foundation for publishing and linking data, and is the standard and a key enabling component for the Semantic Web. RDF, by nature, results in very large data sets because the underlying schema is simple but reflects very complex data relationships. Although it is machine friendly, it is difficult to view by humans.

Many important relationships and trends hidden in RDF data are potentially captured in complex patterns. Therefore, RDF data in raw form does not provide much insight when viewed solely by the human eye. Tools like data visualization and social network analysis applications allow a new and powerful way to view, explore, and understand complex RDF data better and faster at the speed of thought.

Two weeks ago, we attended SemTech 2011 where our CTO, Dr. Francois Bertault, gave a short demonstration of Tom Sawyer Perspectives in Oracle’s tutorial session. The demonstration featured an example application built with Tom Sawyer Perspectives for visualizing RDF data from the Oracle Semantic Data Store. It showcased RDF integration and social network analysis capabilities, along with powerful interactive features for navigating and exploring complex data.

Below is a short video of the example application we demoed at SemTech. This application was built in only 3 days using soon-to-be released Tom Sawyer Perspectives, Verison 3.0. The RDF data set used in this example is from SEC 10-K filings involving 1.8M+ triples. The data is accessed from Oracle Database 11g Semantic Technologies.

Predicting Social Behavior using Social Network Analysis

MIT researcher Deb Roy gave one of the most impressive talks at TED 2011, “The Birth of a Word: from Gaga to Water”. Roy collected about a quarter million hour of footage inside his home to analyze how social environments influence the way his son learns language.

Roy plotted the data of the son’s interaction with the people around him inside the house and the words that were acquired on a 3D map of his house. He used this data for his research on predicting language acquisition based on the structure of when certain words were heard.

A wordscape of the word “water” – the big spikes occur in the kitchen, and the spike on the right is in the bathroom:

Roy then applies this same concept and methodology of analyzing social environments and language acquisition to the conversations happening around the world in social media. The insights you can gain are phenomenal – the factors that start topics of conversations, and more interestingly, how you can use these factors to predict social behaviors.

The nation’s conversations, updated in real time, in response to Obama’s State of the Union Address:

The power of this type of visualization has major implications. People around the blogosphere have debated over whether this could be used for good or evil, in politics, marketing and advertising, or even our personal lives. What are your thoughts?

It’s Not Who You Know…

…but who you don’t really know that will make your network more effective.

There are many collaborative tools out there to help people in organizations work better together and to increase productivity. However, individuals who constantly interact with the same network of people may actually hinder innovation.

The idea is that if your network consists of people who are actually less connected, you are more likely to hear new ideas that have not gone public yet. This empowers you to put disparate ideas together and create brand new opportunities. When you remove yourself from the crowd, you will be enlightened by how much more you can think outside the box.

Based on this theory, the following diagrams from Harvard Business Review suggest that although Richard might have a “better” network at first glance, it is actually Susan who has a more “effective” network.

social network analysis - richardsocial network analysis - susan

If your organization uses a social network analysis tool, you might want to keep an eye out for the Susans of the world when choosing someone to fill your next leadership position.

Source: Harvard Business Review