Democracy and open data: are the two linked?

Curated on Posted on by Stefaan Verhulst

Molly Shwartz at R-Street: “Are democracies better at practicing open government than less free societies? To find out, I analyzed the 70 countries profiled in the Open Knowledge Foundation’s Open Data Index and compared the rankings against the 2013 Global Democracy Rankings. As a tenet of open government in the digital age, open data practices serve as one indicator of an open government. Overall, there is a strong relationship between democracy and transparency.
Using data collected in October 2013, the top ten countries for openness include the usual bastion-of-democracy suspects: the United Kingdom, the United States, mainland Scandinavia, the Netherlands, Australia, New Zealand and Canada.
There are, however, some noteworthy exceptions. Germany ranks lower than Russia and China. All three rank well above Lithuania. Egypt, Saudi Arabia and Nepal all beat out Belgium. The chart (below) shows the democracy ranking of these same countries from 2008-2013 and highlights the obvious inconsistencies in the correlation between democracy and open data for many countries.
transparency
There are many reasons for such inconsistencies. The implementation of open-government efforts – for instance, opening government data sets – often can be imperfect or even misguided. Drilling down to some of the data behind the Open Data Index scores reveals that even countries that score very well, such as the United States, have room for improvement. For example, the judicial branch generally does not publish data and houses most information behind a pay-wall. The status of legislation and amendments introduced by Congress also often are not available in machine-readable form.
As internationally recognized markers of political freedom and technological innovation, open government initiatives are appealing political tools for politicians looking to gain prominence in the global arena, regardless of whether or not they possess a real commitment to democratic principles. In 2012, Russia made a public push to cultivate open government and open data projects that was enthusiastically endorsed by American institutions. In a June 2012 blog post summarizing a Russian “Open Government Ecosystem” workshop at the World Bank, one World Bank consultant professed the opinion that open government innovations “are happening all over Russia, and are starting to have genuine support from the country’s top leaders.”
Given the Russian government’s penchant for corruption, cronyism, violations of press freedom and increasing restrictions on public access to information, the idea that it was ever committed to government accountability and transparency is dubious at best. This was confirmed by Russia’s May 2013 withdrawal of its letter of intent to join the Open Government Partnership. As explained by John Wonderlich, policy director at the Sunlight Foundation:

While Russia’s initial commitment to OGP was likely a surprising boon for internal champions of reform, its withdrawal will also serve as a demonstration of the difficulty of making a political commitment to openness there.

Which just goes to show that, while a democratic government does not guarantee open government practices, a government that regularly violates democratic principles may be an impossible environment for implementing open government.
A cursory analysis of the ever-evolving international open data landscape reveals three major takeaways:

  1. Good intentions for government transparency in democratic countries are not always effectively realized.
  2. Politicians will gladly pay lip-service to the idea of open government without backing up words with actions.
  3. The transparency we’ve established can go away quickly without vigilant oversight and enforcement.”

What happened to the idea of the Great Society?

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John Micklethwait and Adrian Wooldridge in the Financial Times: “Most of the interesting experiments in government are taking place far from Washington: in Singapore, which delivers much better public services at a fraction of the cost; in Brazil, with its “conditional” welfare payments, dependent on behaviour; in Scandinavia, where “socialist” Sweden has cut state spending from 67 per cent of GDP in 1993 to 49 per cent, introduced school vouchers and brought entitlements into balance by raising the retirement age. In the US, the dynamic bits of government are in its cities, where pragmatic mayors are experimenting with technology.
What will replace the Great Society? For Republicans, the answer looks easy: just shrink government. But this gut instinct runs up against two big problems. The assumption that government is evil means they never take it seriously (Singapore has a tiny state but pays its best civil servants $2m a year). And, in practice, American conservatives are addicted to Big Government: hence the $1.3tn of exemptions in the US tax code, most of which are in effect a welfare state for the rich.
For Democrats, the problem is even worse. Having become used to promising ever more entitlements to voters, they face a series of unedifying choices: whether to serve society at large (by making schools better) or to protect public sector unions (teachers account for many of their activists); and whether to offer ever less generous universal benefits to the entire population or to target spending on the disadvantaged.
This is where the politics of the future will be fought, on both sides of the Atlantic. It will not be as inspiring as the Great Society. It will be about slimming and modernising government, tying pensions to life expectancy and unleashing technology on the public sector.
But what the US – and Europe – needs is cool-headed pragmatism. Government is neither a monster nor a saviour but an indispensable part of a decent society that, like most organisations, works best when it focuses on doing a few things well.”

The Collective Intelligence Handbook: an open experiment

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Michael Bernstein: “Is there really a wisdom of the crowd? How do we get at it and understand it, utilize it, empower it?
You probably have some ideas about this. I certainly do. But I represent just one perspective. What would an economist say? A biologist? A cognitive or social psychologist? An artificial intelligence or human-computer interaction researcher? A communications scholar?
For the last two years, Tom Malone (MIT Sloan) and I (Stanford CS) have worked to bring together all these perspectives into one book. We are nearing completion, and the Collective Intelligence Handbook will be published by the MIT Press later this year. I’m still relatively dumbfounded by the rockstar lineup we have managed to convince to join up.

It’s live.

Today we went live with the authors’ current drafts of the chapters. All the current preprints are here: http://cci.mit.edu/CIchapterlinks.html

And now is when you come in.

But we’re not done. We’d love for you — the crowd — to help us make this book better. We envisioned this as an open process, and we’re excited that all the chapters are now at a point where we’re ready for critique, feedback, and your contributions.
There are two ways you can help:

  • Read the current drafts and leave comments inline in the Google Docs to help us make them better.
  • Drop suggestions in the separate recommended reading list for each chapter. We (the editors) will be using that material to help us write an introduction to each chapter.

We have one month. The authors’ final chapters are due to us in mid-June. So off we go!”

Here’s what’s in the book:

Chapter 1. Introduction
Thomas W. Malone (MIT) and Michael S. Bernstein (Stanford University)
What is collective intelligence, anyway?
Chapter 2. Human-Computer Interaction and Collective Intelligence
Jeffrey P. Bigham (Carnegie Mellon University), Michael S. Bernstein (Stanford University), and Eytan Adar (University of Michigan)
How computation can help gather groups of people to tackle tough problems together.
Chapter 3. Artificial Intelligence and Collective Intelligence
Daniel S. Weld (University of Washington), Mausam (IIT Delhi), Christopher H. Lin (University of Washington), and Jonathan Bragg (University of Washington)
Mixing machine intelligence with human intelligence could enable a synthesized intelligent actor that brings together the best of both worlds.
Chapter 4. Collective Behavior in Animals: An Ecological Perspective
Deborah M. Gordon (Stanford University)
How do groups of animals work together in distributed ways to solve difficult problems?
Chapter 5. The Wisdom of Crowds vs. the Madness of Mobs
Andrew W. Lo (MIT)
Economics has studied a collectively intelligent forum — the market — for a long time. But are we as smart as we think we are?
Chapter 6. Collective Intelligence in Teams and Organizations
Anita Williams Woolley (Carnegie Mellon University), Ishani Aggarwal (Georgia Tech), Thomas W. Malone (MIT)
How do the interactions between groups of people impact how intelligently that group acts?
Chapter 7. Cognition and Collective Intelligence
Mark Steyvers (University of California, Irvine), Brent Miller (University of California, Irvine)
Understanding the conditions under which people are smart individually can help us predict when they might be smart collectively.

Chapter 8. Peer Production: A Modality of Collective Intelligence
Yochai Benkler (Harvard University), Aaron Shaw (Northwestern University), Benjamin Mako Hill (University of Washington)
What have collective efforts such as Wikipedia taught us about how large groups come together to create knowledge and creative artifacts?

The rise of open data driven businesses in emerging markets

Curated on Posted on by Stefaan Verhulst

Alla Morrison at the Worldbank blog:

Key findings —

  • Many new data companies have emerged around the world in the last few years. Of these companies, the majority use some form of government data.
  • There are a large number of data companies in sectors with high social impact and tremendous development opportunities.
  • An actionable pipeline of data-driven companies exists in Latin America and in Asia. The most desired type of financing is equity, followed by quasi-equity in the amounts ranging from $100,000 to $5 million, with averages of between $2 and $3 million depending on the region. The total estimated need for financing may exceed $400 million.

“The economic value of open data is no longer a hypothesis
How can one make money with open data which is akin to air – free and open to everyone? Should the World Bank Group be in the catalyzer role for a sector that is just emerging?  And if so, what set of interventions would be the most effective? Can promoting open data-driven businesses contribute to the World Bank Group’s twin goals of fighting poverty and boosting shared prosperity?
These questions have been top of the mind since the World Bank Open Finances team convened a group of open data entrepreneurs from across Latin America to share their business models, success stories and challenges at the Open Data Business Models workshop in Uruguay in June 2013. We were in Uruguay to find out whether open data could lead to the creation of sustainable new businesses and jobs. To do so, we tested a couple of hypotheses: open data has economic value, beyond the benefits of increased transparency and accountability; and open data companies with sustainable business models already exist in emerging economies.
Encouraged by our findings in Uruguay we set out to further explore the economic development potential of open data, with a focus on:

  • Contribution of open data to countries’ GDP;
  • Innovative solutions to tackle social problems in key sectors like agriculture, health, education, transportation, climate change, financial services, especially those benefiting low income populations;
  • Economic benefits of governments’ buy-in into the commercial value of open data and resulting release of new datasets, which in turn would lead to increased transparency in public resource management (reductions in misallocations, a more level playing field in procurement) and better service delivery; and
  • Creation of data-related private sector jobs, especially suited for the tech savvy young generation.

We proposed a joint IFC/World Bank approach (From open data to development impact – the crucial role of private sector) that envisages providing financing to data-driven companies through a dedicated investment fund, as well as loans and grants to governments to create a favorable enabling environment. The concept was received enthusiastically for the most part by a wide group of peers at the Bank, the IFC, as well as NGOs, foundations, DFIs and private sector investors.
Thanks also in part to a McKinsey report last fall stating that open data could help unlock more than $3 trillion in value every year, the potential value of open data is now better understood. The acquisition of Climate Corporation (whose business model holds enormous potential for agriculture and food security, if governments open up the right data) for close to a billion dollars last November and the findings of the Open Data 500 project led by GovLab of the NYU further substantiated the hypothesis. These days no one asks whether open data has economic value; the focus has shifted to finding ways for companies, both startups and large corporations, and governments to unlock it. The first question though is – is it still too early to plan a significant intervention to spur open data driven economic growth in emerging markets?”

#BringBackOurGirls: Can Hashtag Activism Spur Social Change?

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Nancy Ngo at TechChange: “In our modern times of media cycles fighting for our short attention spans, it is easy to ride the momentum of a highly visible campaign that can quickly fizzle out once another competing story emerges. Since the kidnappings of approximately 300 Nigerian girls by militant Islamist group Boko Haram last month, the international community has embraced the hashtag, “#BringBackOurGirls”, in a very vocal and visible social media campaign demanding action to rescue the Chibok girls. But one month since the mass kidnapping without the rescue of the girls, do we need to take a different approach? Will #BringBackOurGirls be just another campaign we forget about once the next celebrity scandal becomes breaking news?

#BringBackOurGirls goes global starting in Nigeria

Most of the #BringBackOurGirls campaign activity has been highly visible on Twitter, Facebook, and international media outlets. In this fascinating Twitter heat map created using the tool, CartoDB, featured in TIME Magazine, we can see a time-lapsed digital map of how the hashtag, “#BringBackOurGirls” spread globally, starting organically from within Nigeria in mid April.

The #BringBackOurGirls hashtag has been embraced widely by many public figures and has garnered wide support across the world. Michelle Obama, David Cameron, and Malala Yusafzai have posted images with the hashtag, along with celebrities such as Ellen Degeneres, Angelina Jolie, and Dwayne Johnson. To date, nearly 1 million people signed the Change.org petition. Countries including the USA, UK, China, Israel have pledged to join the rescue efforts, and other human rights campaigns have joined the #BringBackOurGirls Twitter momentum, as seen on this Hashtagify map.

Is #BringBackOurGirls repeating the mistakes of #KONY2012?

Kony_2012_Poster_3

A great example of a past campaign where this happened was with the KONY2012 campaign, which brought some albeit short-lived urgency to addressing the child soldiers recruited by Joseph Kony, leader of the Lord’s Resistance Army (LRA). Michael Poffenberger, who worked on that campaign, will join us a guest expert in TC110: Social Media for Social Change online course in June 2013 and compare it the current #BringBackOurGirls campaign. Many have drawn parallels to both campaigns and warned of the false optimism that hyped social media messages can bring when context is not fully considered and understood.

According to Lauren Wolfe of Foreign Policy magazine, “Understanding what has happened to the Nigerian girls and how to rescue them means beginning to face what has happened to hundreds of thousands, if not millions, of girls over years in global armed conflict.” To some critics, this hashtag trivializes the weaknesses of Nigerian democracy that have been exposed. Critics of using social media in advocacy campaigns have used the term “slacktivism” to describe the passive, minimal effort needed to participate in these movements. Others have cited such media waves being exploited for individual gain, as opposed to genuinely benefiting the girls. Florida State University Political Science professor, Will H. Moore, argues that this hashtag activism is not only hurting the larger cause of rescuing the kidnapped girls, but actually helping Boko Haram. Jumoke Balogun, Co-Founder of CompareAfrique, also highlights the limits of the #BringBackOurGirls hashtag impact.

Hashtag activism, alone, is not enough

With all this social media activity and international press, what actual progress has been made in rescuing the kidnapped girls? If the objective is raising awareness of the issue, yes, the hashtag has been successful. If the objective is to rescue the girls, we still have a long way to go, even if the hashtag campaign has been part of a multi-pronged approach to galvanize resources into action.

The bottom line: social media can be a powerful tool to bring visibility and awareness to a cause, but a hashtag alone is not enough to bring about social change. There are a myriad of resources that must be coordinated to effectively implement this rescue mission, which will only become more difficult as more time passes. However, prioritizing and shining a sustained light on the problem, instead getting distracted by competing media cycles on celebrities getting into petty fights, is the first step toward a solution…”

Sharing in a Changing Climate

Curated on Posted on by Stefaan Verhulst

Helen Goulden in the Huffington Post: “Every month, a social research agency conducts a public opinion survey on 30,000 UK households. As part of this households are asked about what issues they think are the most important; things such as crime, unemployment, inequality, public health etc. Climate change has ranked so consistently low on these surveys that they don’t both asking any more.
On first glance, it would appear that most people don’t care about a changing climate.
Yet, that’s simply not true. Many people care deeply, but fleetingly – in the same way they may consider their own mortality before getting back to thinking about what to have for tea. And others care, but fail to change their behaviour in a way that’s proportionate to their concerns. Certainly that’s my unhappy stomping ground.
Besides what choices do we really have? Even the most progressive, large organisations have been glacial to move towards any form of real form of sustainability. For many years we have struggled with the Frankenstein-like task of stitching ‘sustainability’ onto existing business and economic models and the results, I think, speak for themselves.
That the Collaborative Economy presents us with an opportunity – in Napster-like ways – to disrupt and evolve toward something more sustainable is compelling idea. Looking out to a future filled with opportunities to reconfigure how we produce, consume and dispose of the things we want and need to live, work and play.
Whether the journey toward sustainability is short or long, it will be punctuated with a good degree of turbulence, disruption and some largely unpredictable events. How we deal with those events and what role communities, collaboration and technology play may set the framework and tone for how that future evolves. Crises and disruption to our entrenched living patterns present ripe opportunities for innovation and space for adopting new behaviours and practices.
No-one is immune from the impact of erratic and extreme weather events. And if we accept that these events are going to increase in frequency, we must draw the conclusion that emergency state and government resources may be drawn more thinly over time.
Across the world, there is a fairly well organised state and international infrastructure for dealing with emergencies , involving everyone from the Disaster Emergency Committee, the UN, central and local government and municipalities, not for profit organisations and of course, the military. There is a clear reason why we need this kind of state emergency response; I’m not suggesting that we don’t.
But through the rise of open data and mass participation in platforms that share location, identity and inventory, we are creating a new kind of mesh; a social and technological infrastructure that could considerably strengthen our ability to respond to unpredictable events.
In the last few years we have seen a sharp rise in the number of tools and crowdsourcing platforms and open source sensor networks that are focused on observing, predicting or responding to extreme events:
• Apps like Shake Alert, which emits a minute warning that an earthquake is coming
• Rio’s sensor network, which measures rainfall outside the city and can predict flooding
• Open Source sensor software Arduino which is being used to crowd-source weather and pollution data
• Propeller Health, which is using Asthma sensors on inhalers to crowd-source pollution hotspots
• Safecast, which was developed for crowdsourcing radiation levels in Japan
Increasingly we have the ability to deploy open source, distributed and networked sensors and devices for capturing and aggregating data that can help us manage our responses to extreme weather (and indeed, other kinds of) events.
Look at platforms like LocalMind and Foursquare. Today, I might be using them to find out whether there’s a free table at a bar or finding out what restaurant my friends are in. But these kind of social locative platforms present an infrastructure that could be life-saving in any kind of situation where you need to know where to go quickly to get out of trouble. We know that in the wake of disruptive events and disasters, like bombings, riots etc, people now intuitively and instinctively take to technology to find out what’s happening, where to go and how to co-ordinate response efforts.
During the 2013 Bart Strike in San Francisco, ventures like Liquid Space and SideCar enabled people to quickly find alternative places to work, or alternatives to public transport, to mitigate the inconvenience of the strike. The strike was a minor inconvenience compared to the impact of a hurricane and flood but nevertheless, in both those instances, ventures decided waive their fees; as did AirBnB when 1,400 New York AirBnB hosts opened their doors to people who had been left homeless through Hurricane Sandy in 2012.
The impulse to help is not new. The matching of people’s offers of help and resources to on-the-ground need, in real time, is.”

The Surprising Accuracy Of Crowdsourced Predictions About The Future

Curated on Posted on by Stefaan Verhulst

Adele Peters in FastCo-Exist: “If you have a question about what’s going to happen next in Syria or North Korea, you might get more accurate predictions by asking a group of ordinary people than from foreign policy experts or even, possibly, CIA agents with classified information. Over the last few years, the Good Judgment Project has proven that crowdsourcing predictions is a surprisingly accurate way to forecast the future.

The project, sponsored by the U.S. Director of National Intelligence office, is currently working with 3,000 people to test their ability to predict outcomes in everything from world politics to the economy. They aren’t experts, just people who are interested in the news.

“We just needed lots of people; we had very few restrictions,” says Don Moore, an associate professor at University of California-Berkeley, who co-led the project. “We wanted people who were interested, and curious, who were moderately well-educated and at least aware enough of the world around them that they listened to the news.”
The group has tackled 250 questions in the experiment so far. None of them have been simple; current questions include whether Turkey will get a new constitution and whether the U.S. and the E.U. will reach a trade deal. But the group consistently got answers right more often than individual experts, just through some simple online research and, in some cases, discussions with each other.
The crowdsourced predictions are even reportedly more accurate than those from intelligence agents. One report says that when “superpredictors,” the people who are right most often, are grouped together in teams, they can outperform agents with classified information by as much as 30%. (The researchers can’t confirm this fact, since the accuracy of spies is, unsurprisingly, classified).
…Crowdsourcing could be useful for any type of prediction, Moore says, not only what’s happening in world politics. “Every major decision depends on a forecast of the future,” he explains. “A company deciding to launch a new product has to figure out what sales might be like. A candidate trying to decide whether to run for office has to forecast how they’ll do in the election. In trying to decide whom to marry, you have to decide what your future looks like together.”
“The way corporations do forecasting now is an embarrassment,” he adds. “Many of the tools we’re developing would be enormously helpful.”
The project is currently recruiting new citizen predictors here.”

 

To the Cloud: Big Data in a Turbulent World

Curated on Posted on by Stefaan Verhulst

Book by Vincent Mosco: “In the wake of revelations about National Security Agency activities—many of which occur “in the cloud”—this book offers both enlightenment and a critical view. Cloud computing and big data are arguably the most significant forces in information technology today. In clear prose, To the Cloud explores where the cloud originated, what it means, and how important it is for business, government, and citizens. It describes the intense competition among cloud companies like Amazon and Google, the spread of the cloud to government agencies like the controversial NSA, and the astounding growth of entire cloud cities in China. From advertising to trade shows, the cloud and big data are furiously marketed to the world, even as dark clouds loom over environmental, privacy, and employment issues that arise from the cloud. Is the cloud the long-promised information utility that will solve many of the world’s economic and social problems? Or is it just marketing hype? To the Cloud provides the first thorough analysis of the potential and the problems of a technology that may very well disrupt the world.”

Open Government Data Gains Global Momentum

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Wyatt Kash in Information Week: “Governments across the globe are deepening their strategic commitments and working more closely to make government data openly available for public use, according to public and private sector leaders who met this week at the inaugural Open Government Data Forum in Abu Dhabi, hosted by the United Nations and the United Arab Emirates, April 28-29.

Data experts from Europe, the Middle East, the US, Canada, Korea, and the World Bank highlighted how one country after another has set into motion initiatives to expand the release of government data and broaden its use. Those efforts are gaining traction due to multinational organizations, such as the Open Government Partnership, the Open Data Institute, The World Bank, and the UN’s e-government division, that are trying to share practices and standardize open data tools.
In the latest example, the French government announced April 24 that it is joining the Open Government Partnership, a group of 64 countries working jointly to make their governments more open, accountable, and responsive to citizens. The announcement caps a string of policy shifts, which began with the formal release of France’s Open Data Strategy in May 2011 and which parallel similar moves by the US.
The strategy committed France to providing “free access and reuse of public data… using machine-readable formats and open standards,” said Romain Lacombe, head of innovation for the French prime minister’s open government task force, Etalab. The French government is taking steps to end the practice of selling datasets, such as civil and case-law data, and is making them freely reusable. France launched a public data portal, Data.gouv.fr, in December 2011 and joined a G8 initiative to engage with open data innovators worldwide.
For South Korea, open data is not just about achieving greater transparency and efficiency, but is seen as digital fuel for a nation that by 2020 expects to achieve “ambient intelligence… when all humans and things are connected together,” said Dr. YoungSun Lee, who heads South Korea’s National Information Society Agency.
He foresees open data leading to a shift in the ways government will function: from an era of e-government, where information is delivered to citizens, to one where predictive analysis will foster a “creative government,” in which “government provides customized services for each individual.”
The open data movement is also propelling innovative programs in the United Arab Emirates. “The role of open data in directing economic and social decisions pertaining to investments… is of paramount importance” to the UAE, said Dr. Ali M. Al Khouri, director general of the Emirates Identity Authority. It also plays a key role in building public trust and fighting corruption, he said….”

Looking for the Needle in a Stack of Needles: Tracking Shadow Economic Activities in the Age of Big Data

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Manju Bansal in MIT Technology Review: “The undocumented guys hanging out in the home-improvement-store parking lot looking for day labor, the neighborhood kids running a lemonade stand, and Al Qaeda terrorists plotting to do harm all have one thing in common: They operate in the underground economy, a shadowy zone where businesses, both legitimate and less so, transact in the currency of opportunity, away from traditional institutions and their watchful eyes.
One might think that this alternative economy is limited to markets that are low on the Transparency International rankings (such as sub-Saharan Africa and South Asia, for instance). However, a recent University of Wisconsin report estimates the value of the underground economy in the United States at about $2 trillion, about 15% of the total U.S. GDP. And a 2013 study coauthored by Friedrich Schneider, a noted authority on global shadow economies, estimated the European Union’s underground economy at more than 18% of GDP, or a whopping 2.1 trillion euros. More than two-thirds of the underground activity came from the most developed countries, including Germany, France, Italy, Spain, and the United Kingdom.
Underground economic activity is a multifaceted phenomenon, with implications across the board for national security, tax collections, public-sector services, and more. It includes the activity of any business that relies primarily on old-fashioned cash for most transactions — ranging from legitimate businesses (including lemonade stands) to drug cartels and organized crime.
Though it’s often soiled, heavy to lug around, and easy to lose to theft, cash is still king simply because it is so easy to hide from the authorities. With the help of the right bank or financial institution, “dirty” money can easily be laundered and come out looking fresh and clean, or at least legitimate. Case in point is the global bank HSBC, which agreed to pay U.S. regulators $1.9 billion in fines to settle charges of money laundering on behalf of Mexican drug cartels. According to a U.S. Senate subcommittee report, that process involved transferring $7 billion in cash from the bank’s branches in Mexico to those in the United States. Just for reference, each $100 bill weighs one gram, so to transfer $7 billion, HSBC had to physically transport 70 metric tons of cash across the U.S.-Mexican border.
The Financial Action Task Force, an intergovernmental body established in 1989, has estimated the total amount of money laundered worldwide to be around 2% to 5% of global GDP. Many of these transactions seem, at first glance, to be perfectly legitimate. Therein lies the conundrum for a banker or a government official: How do you identify, track, control, and, one hopes, prosecute money launderers, when they are hiding in plain sight and their business is couched in networked layers of perfectly defensible legitimacy?
Enter big-data tools, such as those provided by SynerScope, a Holland-based startup that is a member of the SAP Startup Focus program. This company’s solutions help unravel the complex networks hidden behind the layers of transactions and interactions.
Networks, good or bad, are near omnipresent in almost any form of organized human activity and particularly in banking and insurance. SynerScope takes data from both structured and unstructured data fields and transforms these into interactive computer visuals that display graphic patterns that humans can use to quickly make sense of information. Spotting of deviations in complex networked processes can easily be put to use in fraud detection for insurance, banking, e-commerce, and forensic accounting.
SynerScope’s approach to big-data business intelligence is centered on data-intense compute and visualization that extend the human “sense-making” capacity in much the same way that a telescope or microscope extends human vision.
To understand how SynerScope helps authorities track and halt money laundering, it’s important to understand how the networked laundering process works. It typically involves three stages.
1. In the initial, or placement, stage, launderers introduce their illegal profits into the financial system. This might be done by breaking up large amounts of cash into less-conspicuous smaller sums that are then deposited directly into a bank account, or by purchasing a series of monetary instruments (checks, money orders) that are then collected and deposited into accounts at other locations.
2. After the funds have entered the financial system, the launderer commences the second stage, called layering, which uses a series of conversions or transfers to distance the funds from their sources. The funds might be channeled through the purchase and sales of investment instruments, or the launderer might simply wire the funds through a series of accounts at various banks worldwide. 
Such use of widely scattered accounts for laundering is especially prevalent in those jurisdictions that do not cooperate in anti-money-laundering investigations. Sometimes the launderer disguises the transfers as payments for goods or services.
3. Having successfully processed the criminal profits through the first two phases, the launderer then proceeds to the third stage, integration, in which the funds re-enter the legitimate economy. The launderer might invest the funds in real estate, luxury assets, or business ventures.
Current detection tools compare individual transactions against preset profiles and rules. Sophisticated criminals quickly learn how to make their illicit transactions look normal for such systems. As a result, rules and profiles need constant and costly updating.
But SynerScope’s flexible visual analysis uses a network angle to detect money laundering. It shows the structure of the entire network with data coming in from millions of transactions, a structure that launderers cannot control. With just a few mouse clicks, SynerScope’s relation and sequence views reveal structural interrelationships and interdependencies. When those patterns are mapped on a time scale, it becomes virtually impossible to hide abnormal flows.

SynerScope’s relation and sequence views reveal structural and temporal transaction patterns which make it virtually impossible to hide abnormal money flows.”