Data Financial


Posted by Admin

Data Financial

Data Financial

> Posted by Center Staff

This week’s highlights of the blogosphere include a look at the Arab Spring through the lens of Tunisia, a discussion of where mobile phones can take Big Data (and us), and reflections on what we need to know in order to improve financial access.

  • Jonathan Morduch poses “Ten Research Questions on Improving Financial Access,” and argues on the Financial Access Initiative blog that these queries need to be answered “if we are to make informed decisions on how to improve financial access.”
  • Grant Tudor,  writing about “The Age of Big Data” on NextBillion, maps out the thinking that’s been done on a new era where mobile phones may help resolve our data dilemmas.
  • How is the industry responding to the new challenges and opportunities created by the Arab Spring? In “From Microcredit to Microfinance: New ambitions for Tunisia” on CGAP’s Microfinance Blog, Emna Kallel and Alice Negre examine some answers.

Have you read?

Mexico, Mobile Banking & Shodh Yatra – Top Picks of the Microfinance Blogosphere – September 30, 2011

Energy Drinks, Lebanon, & Impact Investing – Top Picks of the Microfinance Blogosphere – September 14, 2011

Andhra Pradesh, Haiti & Corporate Capital – Top Picks of the Microfinance Blogosphere – August 22, 2011

Financial Ed, Mobile Money, & Savings – Top Picks of the Microfinance Blogosphere – August 3, 2011

Women, India, & Tourism – Top Picks of the Microfinance Blogosphere – July 19, 2011

Armenia, Andhra Pradesh & Pakistan – Top Picks of the Microfinance Blogosphere – June 13, 201

Researchers at the Department of Energy’s Pacific Northwest National Laboratory (PNNL) have developed new software that helps identify anomalies in complex financial data, in hopes of detecting problematic financial trends that jeopardize U.S. and global financial systems. It’s a great example of the kinds of research opportunities at the intersection of computer science and finance.

From the official DoE press release:

Identifying atypical information in financial data early could help identify problematic financial trends such as the systemic risk that recently put the U.S. and global financial systems in a downward fall. Recognizing such anomalous information can also help regulators, investors and advisors better manage their investment and savings portfolios.

 

Now, new analytical software developed by Battelle researchers based [at PNNL] can do just that…

 

In a demonstration of this technology, the Battelle-developed Anomalator™ software recently picked out the atypically stable and positive returns reported by disgraced financier Bernard Madoff as an anomaly among hundreds of funds. He is now serving a 150-year prison sentence for scamming investors out of as much as $65 billion in a Ponzi scheme that spanned at least 20 years. Unfortunately, Madoff’s fraud was concealed for more than 15 years. The use of this sophisticated anomaly-detection and visualization tool could have exposed Madoff early on, and can help expose future scandals, its inventors say…

 

Traditional financial analysis reports either provide a list of numbers or a simple line graph to represent the value of just one investment over time. The Anomalator™ is unique in its ability to identify unusual trends in complex financial data and graphically show how it compares with larger datasets.

So how does it work? Find out after the jump…

Anomalator™ uses mathematical algorithms to identify the atypical data in databases that record the movement of funds or the people who manage them over time. The software then creates a line graph representing the progress of anomalous funds or managers, as well as other user-selected funds or managers of interest…

In the case of Madoff:

Homing in on the tool’s potential to detect financial fraud, [researchers] compiled Madoff’s stated returns from one of the leading Madoff feeder funds… ran several scenarios and found that while the majority of the market was volatile, repeatedly spiking up or down, Madoff’s returns were atypically upward sloping and effectively never lost money. This technology’s unique anomaly-detection and visualization helps expose glaringly anomalous patterns such as those produced by Madoff.

To learn more, check out the DoE press release.

(Contributed by Erwin Gianchandani, CCC Director)