Panama papers: Can tax departments (finally!) eradicate tax havens?

551987547Historically, tax havens have been a key tool for tax evaders to store and hide unreported and untaxed money. I would agree with most observers that the Panama papers (11.5 million leaked documents that detail financial information for more than 214,488 offshore entities) are just the tip of the tax haven iceberg - a massive iceberg into which the tax haven boat has crashed.

The momentum created by the Panama papers has accelerated an effort already well underway to eradicate tax havens (e.g.: Global Forum on Transparency and Exchange of Information for Tax Purposes). It’s now becoming expensive, very high risk and low return to use tax havens to facilitate tax evasion.

But in data terms, it’s even less than the tip of the iceberg. The volume and variety of external data that tax departments collect will grow exponentially. This data, combined with analysis techniques, will make it extremely difficult for tax evaders to remain undetected. Tax departments have a unique opportunity to deploy these new capabilities in order to make offshore fraud a thing of the past.

The challenges

The high volume and complexity of the external data has made it difficult for authorities to rapidly mobilise their current operations in order to have a systematic approach. Progress has been made by some of the leading tax authorities, but not all these new capabilities will scale as the volume and variety of external data explodes.

This creates a risk that vital information will be lost in the resulting chaos. A key process for effectively using external data is being able to match it with internal data, and is the starting point for the detection, prevention and management of offshore fraud.

This process comes with a couple of challenges:

Manual processes: Very often, the matching exercise requires some level of manual validation. While this might be an acceptable practice today, it most definitely will not scale.

Limited resources: Government human resources dedicated to prevent, detect, investigate and prosecute fraud and abuse cases are subject to attrition, turnover and are very limited compared with the scope of the assigned task.

Complexity: The nature of the fraud requires a system to be able to identify the linkages between the chains of business involved in the fraud. Detecting fraudulent networks requires the analysis of millions of taxpayers, bank transactions, and company information.

Variety of data: The growing number of data sources will make it increasingly difficult for tax evaders to remain undetected in the countries where the tax departments deploy the processes and capabilities to use this data to detect fraud. Many different external sources complement their internal data, including:

  • Leaked data (e.g.: Lux Leaks, Panama papers, Offshore leaks): These sources are valuable as they reveal the ultimate owners and beneficiaries of offshore constructions and provide key information for investigations. However, these sources are never sufficient by themselves for tax fraud enforcement.
  • Automatic exchange of information/CRS: These two specific data sources and their implications for tax departments have been described in detail in the blog post The Common Reporting Standard: An opportunity and a challenge for tax authorities.
  • Banking transactions: Transmitted to the FIU by financial institutions when there is a suspicion of tax fraud or money laundering.
  • Banking transactions to offshore: In an increasing number of countries, financial authorities have an obligation to declare transactions made with tax havens.
  • Payment data: This data is transmitted by payment organisations to the tax departments. They can, for example, be useful to detect payment cards linked to offshore accounts.

We believe that by 2018 only 30% of the tax agencies will have the capabilities to systematically use external data in their fraud detection efforts.

Capabilities

fraud-detection

Figure 1: Hybrid Detection Model

Against this background of challenges, there is nonetheless quite a variety of different, but interrelated techniques available to detect offshore fraud using both internal and external data:

Social network analysis: The starting point of the analysis, this technique matches and integrates data from various sources and discovers networks that will later be used to detect tax fraud patterns. As data is ingested, the system automatically builds networks of relationships among the data (e.g. which directors have directorships at other companies; what transactions link different businesses together, etc.) where shared details are available, such as addresses or telephone numbers (fuzzy matching methods can link similar addresses together).

Automated business rules: The most skilled investigators within the tax authority will have a range of techniques they can employ to uncover tax fraud. By having an environment in which these can be encoded as business rules that are then automated to run across the entire data set, it is possible to leverage these precious resources and their expertise.

Anomaly detection: The system can be used to identify businesses that are not behaving in the same way as other businesses within their peer group. For example, this may relate to the types or volume of transactions in comparison to the declared revenue. Such anomalies are risk factors and useful ways of discovering new types of fraud modus operandi.

Predictive modelling: When a number of businesses known to be associated with offshore fraud are identified, these can be used to train models that look at combinations of variables or business rules and then optimise them to best predict that a particular business or network of activity is associated with fraud.

Text mining: Unstructured information may contain key data. For example, open source data (internet, newspapers) can be used to find more information on a suspect beneficiary owner, its activities and previous cases involving them.

Risk vs. return

The risks associated with tax havens have gone up dramatically as public opinion is taking a tough stand on offshore fraud. Initially, offshore constructions were advised and sold by mainstream banks to their wealthy clients. Today, intermediaries who are setting up offshore constructions for their customers are taking significant legal and reputation risks. This is why most banks will no longer facilitate offshore fraud and fraudsters will have to resort to more shaky intermediaries. The cost charged by the intermediaries has also gone up because of the complexity of the constructions and the risk.

Beneficiaries of offshore constructions are also taking a significant risk, since appearing in any document leaks (e.g.: offshore leak, Panama papers) comes with a massive reputational cost. For small fraudsters, this reputational cost will outweigh the initial tax gain of offshore fraud.

The return of offshore fraud is also lower as it has becomes more difficult to use offshore money. The vast majority of countries have anti-money laundering measures in place, preventing fraudsters from repatriating or accessing offshore funds (e.g. using a credit card linked to an offshore account). Finally, many countries are now launching voluntary disclosure programs. These programs are attractive for taxpayers with offshore constructions as they drastically reduce their risk at a reasonable cost.

Conclusions

Tax havens may soon be a thing of the past as tax departments are adopting a more systematic approach to detect, prevent and manage offshore fraud. But we now have a unique opportunity to deploy a wide array of effective techniques and capabilities, and are on track to eradicate this type of fraud.

The major risk we now see is that the wealthier fraudsters will set up even more complex structures (i.e.: potentially combining tax evasion with a minimum of bona fide commercial activity). Tax departments need to be proactive in tackling these new types of fraud. To learn more, read this white paper: Analytics to Fight Tax Fraud.

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Can you measure and optimize happiness?

Improving citizen happiness is an important goal for many, if not all, governments.  But what is happiness really?  Can it be objectively measured?  Can we discover the key factors that best correlate with happiness?  And ultimately, can governments implement policies and programs that maximize happiness?

Is maximum happiness nothing more than a non-linear conjugant gradient optimization?

In the late summer last year, I had the pleasure of spending about a week in the United Arab Emirates, participating as a speaker in the National Security Middle East 2016 event in Abu Dhabi.  It was the second time I travelled to the UAE last year, and I found the Emiratis to be warm, friendly and welcoming without exception.

It turns out that the Emiratis’ warmth is something they're now attempting to measure, and in some sense optimize across society.  During one of the breaks at the event, I was speaking with my SAS Middle East colleagues, and they shared with me that the UAE government had recently created a Cabinet-level agency, the Ministry of Happiness, led by Her Excellency Ohoud bint Khalfan Al Roumi. Read More »

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4 adaptability attributes for analytical success

After reading a recent LinkedIn post by Jeff Haden, "Want to Achieve Lifelong Success? An Army Ranger Says You Need This 1 Trait the Most", (spoiler alert: It's adaptability) something occurred to me. One of the reasons I enjoy solving business problems with analytics is that analytics is all about being adaptive and causing change (i.e. growth).

adaptability_blogI've learned over my career that you can either choose to change OR change happens to you. And it's almost always better to proactively choose change than to reactively respond to it.

When you're looking to use analytics, don't look for a black box approach to solving a problem, because once you implement that solution, guess what happens? Life or situations change and your analytics need to adapt and change quickly in order for you to continue to succeed.

What can you do about it? Here are four adaptability attributes for analytics success, and what they mean from a business and technology perspective.

1. Agility

  • Business: The ability to try out different ideas without having to switch to other tools, languages, or physical environments.
  • Technology: The ability to deploy analytical software capabilities quickly without the hindrance of securing new hardware resources in advance.

2. Resilience

  • Business: No more failed jobs due to "unknown system issues." The process, job, or model continues to work seamlessly without interruption.
  • Technology: Having an architecture designed for high-availability means there are fewer fire-drills, and upgrades occur with minimal disruption.

3. Speed

  • Business: The ability to fail fast, try alternatives, and evaluate the results quickly so more ideas can be researched, and the ability to move new results into production now instead of later.
  • Technology: Having an architecture that supports the requests of all users in a timely manner, and the ability to deploy insights from models into production.

4. Scalability

  • Business: Users are no longer limited in their data exploration and modeling by the size of their data. As the problem grows the ability to process grows with it.
  • Technology: Since analytical workloads are variable the environment needed to process these workloads should be able to grow and shrink as needed.  For example, a scalable environment will burst to additional compute nodes and/or spill to disk if available memory is short.

Your next step? Learn how analytical success starts with the right analytics platform.

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Digital footprints in the sand … a source of rich behavioural data

woman-163426_960_720-300x189In the word of digital marketing, one of the more controversial moves I’ve seen recently was from U.K. car insurer Admiral. The company recently announced that it would begin offering car insurance discounts to less risky customers based on voluntarily provided social media data. The insurer would analyze Facebook likes and posts, and could analyze the language and patterns. This allows it to identify behaviour and personality traits which predict a higher or lower risk compared with the average for that demographic profile.

While Admiral’s plans were eventually scrapped due to Facebook’s data privacy policies, the simple truth is that many digital footprints are already being harnessed, analysed and shared to assist digital marketing efforts (from consumer goods to political parties). It’s possible to increase conversions and reduce the cost of acquisition through understanding digital visitors better and ensuring that adverts reach the “right kind” of consumer. Read More »

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Lessons learned from customer modernization projects

522144344A number of posts on SAS Voices have touched upon the theme of modernization. This is certainly a hot topic with our customers as many of them continue to be interested in taking advantage of the evolving software landscape.

The thing is, modernization can be hard. I should know, I’ve been on some of those projects! In this post I want to take you through some of the challenges, some lessons learnt and how best to approach a modernization project.

Lesson #1: Be clear about your modernization objectives

One of the biggest challenges that I have found when we talk about modernization is resistance to change. That’s why, one of the very first things you need to do is be clear about your objectives and ensure this is communicated and well-understood by all those involved. This is very important because, by definition, modernization projects introduce a transition state into an organization (i.e. the old system and the new systems are temporarily in place at the same time). It can be difficult to support both of these states at the same time and one of the best ways to manage the situation and get buy-in is to ensure that everyone understands the project objectives. Some examples of these might be:

  1. Introduce new software to enable users with new functionality.
  2. Improve performance by upgrading the hardware.
  3. Reduce costs through infrastructure consolidation.

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Retailers use optimization to improve in-store fulfillment and keep customers satisfied

523540737Omnichannel shoppers have been disrupting retailers for years, and its likely to top the industry’s agenda of challenges for years to come. But optimization, an omnichannel analytics technology, can help harness the positives of omnichannel retailing and minimize showrooming.

Consider this everyday retail dilemma: E-commerce sales are growing, but in-store sales volumes are declining. Customers still enjoy browsing in brick-and-mortar locations, but they buy items with a mobile device a few hours later. It’s this showrooming behavior that leads to complexities such as maintaining adequate inventory and staffing levels to support in-store traffic while sales decline.

I'm headed to New York for the National Retail Federation Big Show (NRF17), and feeling a bit overwhelmed this year by all the challenges modern omnichannel retailers face in order to survive. Showrooming is one of those challenges I'm sure we'll talk about at the confernece, and I wanted to cover a few possible solutions here as well.

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Intelligent ecosystems and the intelligence of things

145073602I've long been fascinated by both science and the natural world around us, inspired by the amazing Sir David Attenborough with his ever-engaging documentaries and boundless enthusiasm for nature, and also by the late, great Carl Sagan and his ground-breaking documentary series, COSMOS. The relationships between the creatures, plants and ecology of our planet is an incredible story of symbiotic systems evolving, failing, adapting and improving, for more than 4 billion years on our own planet (which you could argue is itself just a tiny part of the longest running, deepest learning algorithm of all).

So at the dawn of this new age of autonomous connected devices and AI-enhanced software, it’s worth looking at natural systems to see if we can draw any lessons about the evolution of technology.

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The seven traits of a modern analytical platform

modern woman in front of tall, modern buildingsMuch of my recent work has been along the theme of modernization. Analytics is not new for many of our customers, but standing still in this market is akin to falling behind. In order to continue to innovative and remain competitive, organizations need to be prepared to embrace new technologies and ways of working. For example, one of my current banking customers is looking to transition their many different analytical systems into a modern, consolidated environment. This is to reduce infrastructure costs, share resources and provide a better understanding of how data are transformed in the organization for the purposes of compliance and regulation.

Modernization can be a complex endeavor, especially for an organization with a lot of legacy infrastructure - that might be the topic of another blog. In this article, I'll start by outlining some of the characteristics that underpin a modern platform for analytics. Read More »

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An era of promise and uncertainty for the energy industry

ISOs/RTOs keep the lights onIt’s no secret that the US energy landscape has undergone massive changes in recent years: the emergence of cost-effective renewables, the natural gas revolution, the wide-scale penetration of intelligence across energy delivery networks, and soon a new resident at 1600 Pennsylvania Avenue.

All of these changes are impacting different pockets of the energy industry in different ways. For example, independent system operators, also known as regional transmission organizations (ISOs/RTOs) are keeping a close eye on these developments. Since ISOs/RTOs coordinate, control and monitor multi-state electric grids in the US, their viability and success are, to a large degree, dependent on how well they manage through these changes. Though there are only seven players in this space, their importance in keeping the lights on and the economy humming are critical. Read More »

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Closing out a year of transformation in the communication and media industries

It's that time of year again. Holidays, parties, gifts, cooking, closing annual business, hitting targets and preparing for 2017. 128207006Looking back on the year for the communication and media industries, it has been a year of transition for the industry and for many of the customers I work with in my role at SAS.

Communication service providers (CSPs) and media companies are wrangling with digital transformation – where traditional business models are suffering from declining revenues or becoming obsolete. As the market changes, CSP activities now span many businesses. Per PWC Strategy&: CSPs are challenged to be a network guarantors, business enablers, experience providers and global multimarketers. This is no easy feat, and the key to being successful is to innovate with analytics. For media companies, digital transformation means moving from traditional media like DVDs to new digital media, like over the top services. Plus, CSPs are buying up media companies, further changing the landscape.

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