Smart Cities experts: Collaboration is key

Government, academia and the private sector all have a role in Smart Cities.

Government, academia and the private sector all have a role in Smart Cities.

Cities must work with companies, universities, other cities and organizations to truly realize the Smart Cities vision. This was a consistent message at last week’s Smart Cities Innovation Summit, where leaders from more than 200 cities met with technology and service providers and academics to talk about new innovations that will improve the lives of citizens across the globe.

In conjunction with the conference, the National Institute of Standards and Technology (NIST) hosted its Global Cities Team Challenge (GCTC) Expo.  Throughout the events, one message rang out loud and clear: cities that collaborate will be more successful.

“It’s not what a Smart City is; it’s what a Smart City does.” - Dr. Sokwoo Rhee, Associate Director of Cyber-Physical Systems Program, NIST

One of the best ways cities can collaborate is to create Smart Cities projects that are replicable. Dr. Rhee encouraged cities to share their successes with one another to promote technological advancements.  As cities learn how to adopt technologies that benefit their citizens, their experience and knowledge can help other cities quickly adopt similar solutions.

“Collaboration is the ‘new competition’.” - Former Governor of Maryland, Martin O’Malley

Collaborations with the private sector were strongly encouraged. And not just between governments and companies, but between companies themselves.  The Mayor of Austin, Steve Adler, told the audience that a “Smart City is one that recognizes that they cannot act alone and that they need to work with others to approach technology.”

The presentation of nearly a hundred innovative projects underscored O’Malley’s and Adler’s messages.  Each is a joint effort of companies and local government partners. The exciting projects ranged from suits for emergency workers that garner biometric information to tracking devices that protect victims of domestic abuse. Throughout the conference, companies expressed excitement over sharing what they are accomplishing and looked for opportunities to partner with other companies to advance their initiatives.

Most companies recognize the value of specializing in what they do best and teaming up with companies that specialize in other technologies.  As a result, many Smart City solutions are the compilation of dove-tailed technologies delivered by a team of “best in class” companies.

“A Smart City thinks about how everyone plays into a solution.” - Mark Dowd, USDOT Assistant Secretary for Research and Technology

Not only should we work together to create solutions, but we should consider how the solutions will affect citizens and develop relationships between stakeholders.  This is a refreshing perspective as we put the excitement of new technologies aside and remember that the purpose of employing them is to better the lives of our citizens and improve our local economies.

“People live in cities. But, we live also in the world.  We are obligated to share what we have learned with the rest of the world.” - Jack Mikkers, Mayor of Veldhoven, The Netherlands

Every project presented specifically addressed a real challenge that a local government is facing and provided solutions that would have an impact on the people of that community.  As we continue to develop advanced technologies, let us remember to collaborate with other cities, companies, universities, organizations, and even countries, to improve lives and better our world.

 

 

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Tackling 4 common concerns about analytics for child well-being

Analytics can make a true difference in child well-being efforts. Flickr image by ann_jutatip

Analytics can make a true difference in child well-being efforts.
Flickr image by ann_jutatip

In my last blog post, I introduced common concerns I’ve heard about predictive analytics in child well-being efforts. In this post, I want to address those concerns and reassure leaders and advocates that predictive analytics can be a tremendous boon to our ability to help kids and ease the burden on caseworkers.

They concerns I hear most are about:

  • False Positives
  • Racial Bias/Disparity
  • Equation vs. Comprehensive Process
  • Current Tools are Sufficient

Concern:  Child well-being predictive analytic solutions may result in a high number of false positives, giving workers added work.

This concern is misguided. Sophisticated analytics, like SAS for Child Safety, actually has identified less than 5% of overall cases as being at the highest level of risk of maltreatment and/or fatality.  Current validated actuarial risk and needs assessment have routinely identified from 25-30% of cases as being highest risk. This increases the potential amount of false positives up to 500%.

Child safety models are built on a multitude of risk factors, many of which vary in intensity (not just Yes/No binary values). These risk factors are intuitive. The literature and common sense would agree on the peril a child is exposed to given extreme values of these risk factors, such as:

  • Scores of prior maltreatment allegations by child’s primary caregivers
  • Multiple victimization events in caretaker’s childhood
  • Low maternal age

To be in the highest risk segments, a child needs to have extreme values in a multitude of risk factors (not just one).  In such an environment, risk of general harm to a child is also elevated, not just risk of fatality.

Fatality modeling is more narrowly focused. For each fatality there may be dozens or more near misses (injuries but not fatalities). However, data for serious near misses is not readily available. As terrible as it may be, a child who ends up in the ICU due to abuse is considered a false positive for a fatality risk model.

We have to remember that risk to child well-being is ongoing. In our research at SAS, we have observed children in extreme risk segments at the end of a study period that eventually become fatalities. That said, risk can also decrease over time as, for instance, a child’s age increases or there are changes in household composition.

Concern:  Predictive analytics may attribute to racial bias and disparity within communities.

Racial disparity is not apparent in fatality risk.  Literature and SAS’s own modeling in multiple jurisdictions has found no significant difference in maltreatment and/or fatality risk for black versus white households.   Of some interest was the fact that Hispanic households had a slightly lower fatality risk.

The fact is, race is vastly eclipsed by other risk factors like prior maltreatment allegations, making racial factors of little value in risk models.

Concern: Analytics is "just" a mathematical equation, no a comprehensive process 

Analytics actually IS a comprehensive process that helps generate a “golden record” of an individual. The child well-being models that support these efforts are based on historical data multiple factors and outcomes, such as fatalities, maltreatment, permanency, homelessness, etc. More accurate and complete data is modeled, allowing for workers to be more confident and proactive.

Collections of various factors determine risk, not individual facts or flags. This eliminates the one-size-fits-all approach to case response (e.g. children under 5 are high risk and require a full investigation), which is not proven out by the risk model.  A high risk score means there is a multitude of simultaneous risk factors present.  The risk score brings this to the attention of the case worker to help inform decisions, coupled with their professional judgement.

Concern: Current actuarial tools are sufficient, so why use analytics?

Case workers are also analytic models (biological not mathematical) and in general, not very good ones.  This is not because they aren’t dedicated, intelligent professionals, of course. However, judgement or consensus decision making is shown in the literature to underperform formal actuarial methods.

This is due to a number of factors, including high caseworker turnover rates and the associated variances in caseworker experience. Caseworkers can introduce bias in the scoring process when using actuarial methods like Structured Decision Making. In more extreme situations, there are examples of caseworkers manipulating actuarial tools to force certain actions like a home removal.

All of this could be helped by better information, but there’s a problem there, too. Caseworkers too often are provided with incorrect and/or overwhelming data. A combination of poor data quality and an inability to sift through what’s relevant and not amid an avalanche of data hinders decision-making.

Operational analytic models work across the risk spectrum and can also assist in screening and triaging cases so they can be routed based on available data.  In addition, data from multiple agencies can better inform the model and resulting decisions. Additional data is especially important in creating more accurate risk assessments in situations with limited report histories for a child.

The debate over analytics, actuarial tools and basic human judgement will continue, and it should. We should all work towards finding the balance of approaches that gives us the best chance to help kids. That said, I hope I’ve alleviated some of the more common concerns and look forward to continuing the discussion in the comments section.

 

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Addressing concerns of predictive analytics for child well-being

As state and local government leaders and community advocates explore how predictive analytics can improve child well-being outcomes, many questions and potential concerns surface.

It is critical to understand that government youth services agencies across the United States have used actuarial Risk & Need Assessment tools for many years. Many of these were developed using predictive analytics to identify “static” risk factors, such as low birth weight and mother was previously in foster care, that contribute to risk of maltreatment.

While these tools are a good starting point, there face implementation problems such as accuracy requirements, worker bias, amount of time to complete, etc.   With an operational analytic approach, analytics is continually assessing and reassessing data, making the system smarter. Ongoing assessment of risk and needs allows for both static and dynamic factors to be used when calculating risk of maltreatment.  This supports increased accuracy, real time risk scoring, limited worker bias, etc.

Below is a comparison/contrast of actuarial tools vs. and operational analytic solution such as SAS for Child Safety:

WillJonesblog-Actuarial-Operational-childwellbeing

I worked in child well-being for 22 years, and have met with countless leaders and community advocates to discuss advance analytic approaches and their applicability to human services. Several concerns have been raised consistently that can be addressed with data driven and research based responses.  These include:

  • False Positives
  • Racial Bias/Disparity
  • Equation vs. Comprehensive Process
  • Current Tools are Sufficient

In my next posts, I will dive deeper into these concerns as well as findings from my peers in SAS Solutions on Demand, who are engaged in pioneering work on child welfare analytics. I will explain why leaders and child well-being advocates can feel confident in predictive analytics, and its ability to help kids and ease the burden on caseworkers.

Do you have concerns beyond these, or heard of others? Please share in the comments.

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Celebrate (?!) Tax Day

Embrace the inevitability of Tax Day! Image by Flickr user Paul Stumpr

Embrace the inevitability of Tax Day!
Image by Flickr user Paul Stumpr

Happy Tax Day, America!

Today marks our annual ritual of filing tax returns in the United States.  And complaining about taxes.  And cursing the IRS (even though it's misguided to shoot the messenger, in my opinion).

Think you know a lot about taxes?  Let's travel back in time to 1913, when the current version of the US income tax code was first enacted.  Here are four fun tax facts to ponder:

  1. The first US individual income tax return was 4 pages in length... including instructions.  Don't believe me?  Check it out!
  2. Tax brackets have ranged from a low of 0.375% (1929) to a high of 94% (1944/45).  The highest "low" bracket?  It was 23% during World War II (1944/45).  The lowest "high" bracket?  It was 7% from 1913 through 1915.  (For argument's sake, we'll omit the 0% bracket for very low income taxpayers.)
  3. Think the tax code has too many deductions?  Deductions have been part of the US tax code from Day 1.  Here are the "Original 8" deductions from the original US income tax law (summarized here for brevity):
    • Business expenses
    • Interest paid on "indebtedness"
    • "All national, state, county, school, and municipal taxes paid..."
    • Losses "incurred in trade or arising from fires, storms, or shipwreck"
    • "Debts... ascertained to be worthless and charged off within the year"
    • Depreciation
    • Dividends
    • Withholding amounts
  4. Since 1913, the number of returns filed with the IRS has grown by 4,120%!  A mere 357,598 returns were filed in 1914 (for tax year 1913).  Now, during peak filing season, the IRS processes more returns than that in a single day and receives almost 150 million individual income tax returns each year.

It's pretty clear that the present-day US tax code -- and how it is administered -- is vastly different than what Congress enacted in 1913.  (BONUS FACT! Tax Day was originally March 1st.) Times change...

The challenges tax administrators face today are daunting.  Globalization of commerce.  The rise of technology and data analytics.  A change in attitudes about civic responsibility. The good news is that there are a lot of smart people who think about how to help tax administrators solve these challenges.  Like who?

So, on this day of angst and financial stress, be sure to check out what these experts have to say about our modern Tax Day.  They have ideas that solve problems that William Henry Osborn (IRS Commissioner in 1913) could have never imagined would exist.

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State government center of analytics goes nowhere without user engagement

"I find your lack of faith in a center of analytics disturbing." Note: This is not the way to engage users. Image by Flickr user Ripster55

"I find your lack of faith in a center of analytics disturbing." Note: This is not the way to engage users.
Image by Flickr user Ripster55

In this third post about a government center of analytics, the focus is on creating an environment that enables successful implementation, and perhaps even more importantly, successful adoption of new analytic solutions. (Check out "Hey, government of [insert state], where's your center of analytics?" and "4 keys to building a state government enterprise analytics system", if you missed them.)

While analytic solutions can bring significant benefit to business decision makers, those benefits are only realized if the solutions is actually used to impact business decisions.  New analytic solutions represent change.  And change can create both anticipation and trepidation.

Questions about how analytics will impact job security, create more work, reveal information that may be less than positive, and other concerns can impact willingness to embrace new technology.  By recognizing these challenges, a center of analytics team can facilitate more successful implementations.

Engage the business users

It’s important to understand the business need and how access and insight to enterprise data can improve an organization decisions and service of citizens. But remember that an agency leader’s, and a front-line worker’s, idea of what is needed may differ.  Acknowledging these different perspectives helps ensure that the right people are involved early and often so the analytics will meet everyone’s needs. Here are some tips to engage business users.

  • Avoid developing analytics for the sake of analytics. Ensure there is measurable benefit for the user.
  • Keep it simple. Fix the critical problems first to show value and then expand to more advanced analytic capabilities.
  • Allow the user to “see and feel” how the analytic solution will work through tool demonstrations and building prototypes.
  • Build an advisory team of end users to ensure the final outcome works well for the user community.

And keep in mind that no analytic solution can replace the knowledge and expertise of the business user.  Analytics cannot make a decision about how to act on a business problem – but it can help equip the business user with the right information to make the decision.

Learn a new language

One of the best phrases I’ve heard was shared by the Oregon Youth Authority when discussing their analytics approach to support juvenile justice reform – “learn a new language”.  The point of the message was that business users of an analytic solution and developers of an analytic solution each speak different languages based on prior experiences and knowledge.  When striving to build a new solution, finding a common new language for mutual communication is critical to the adoption and integration of a new solution into business processes.

If the users of the solution find it challenging or frustrating to use, if they haven’t been involved in learning the new language of the solution, they will return back to what they’ve always known and the solution may become shelfware.  Regular, iterative engagement, training, and education can help institutionalize the new language, increasing the chances of the analytics becoming embedded in business process and changing business outcomes.

Operationalize the result

A focus on change management can help organizations understand how analytics will help make current processes and searches for information data more efficient.  Who wouldn’t want to be freed up from tedious administrative tasks to focus on more challenging and meaningful activities?

But that line of thinking can change slowly. Training and adoption efforts should be a planned and iterative activity.  Find the champions and innovators in an organization – those people who like a new opportunity.  Tap into their enthusiasm to encourage others.  Pilot the solution to work out the kinks before deploying to a more expansive user community.  These activities can help identify, mitigate and resolve inhibitors to usage of the new solution.

Learn, refine, repeat

Finally, recognize that developing and using analytic solutions is a learning process.  Once the user community can use their analysis, they begin to understand what else they might be able to do with the data.  Models are refined, new processes developed and additional data sources added.  Analytics allows us to be proactive, creative and better informed.

Watch for the final post in this series about finding the value in analytics!

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Tax returns, identity theft and hackers – Oh my!

Welcome to Tax Week, identity thieves!  Photo by Flickr user Aranami

Welcome to Tax Week, identity thieves!
Photo by Flickr user Aranami

Monday, April 18th is Tax Day, aka, National Identity Theft Day. OK, that part’s not true, but as millions of taxpayers go online to file taxes, it may as well be. The majority of taxpaying citizens file online, using services such as the popular TurboTax, H & R Block, and TaxSlayer . Even the IRS now offers its own free online tax filing service, Free File.  While online filing provides a convenient and more expedited means to submit tax documentation, it also introduces challenges akin to a massive clearance special on the Dark Web. With all that personally identifiable information (PII) flying around the web, you (almost) can’t blame criminal enterprises. They’re kids in a candy store… They just can’t help themselves.

In all seriousness, identity theft and fraud are real problems that far too many of us have faced in recent years, thanks to the “internet of everything”.  Never before have we enjoyed the ability to recon a vacation property via Google Earth, log into our bank account to check our balances or pay bills, “chat” with colleagues over instant message, purchase that miniature horse pet door, or binge watch another season of Homeland – right from that one spot on the couch you haven’t moved from…. for days.  And as convenient as all that may be, the very same technology that allows us to avoid human contact for weeks on end, also provides a very attractive vehicle for criminal activity.  The same benefits we enjoy while not cyber stalking an ex on Facebook, cyber criminals and hackers enjoy while “breaking into” financial institutions, the Department of Defense, retailers and even the IRS.  And this time of year, it’s like Cyber Christmas.

From across the world, or the basement next door, bad actors could be submitting fraudulent tax returns with the information stolen from tax preparing software websites or the IRS itself.  The data that can be obtained from tax documents is the cybercrime “holy grail.”  Far more valuable than a person’s bank account info, or a credit card stolen from a retailer’s point of sale, tax data exposes so much of one’s personal life, including family members and their PII and employment information. With these golden nuggets, a motivated bad guy can steal an identity by applying for new credit lines and causing a whole mess of liability issues for the unlucky victim to unravel.

While I have never personally endured such an ordeal I have heard from many that it’s so challenging to recover from that the punishment for actually committing a petty crime is a seemingly better outcome. Considering this, I am surprised to still see audits that cite continuing security vulnerabilities in e-filing systems, additional data breaches and even unresolved vulnerabilities in software months after detection.

While there is evidence government agencies are facing decreasing budgets, while cyber criminals continue to become more advanced, responsible parties seem to be outraged at such “lax security”. But t the fact of the matter is – without adequate priorities, funding and oversight, we cannot expect our sensitive information to protect itself.

Right now, as tens of millions of taxpayers transmit their PII over the web, criminals are planning and executing to commit fraud.  We know that tax data is highly valuable, and identity theft is debilitating for a victim, yet the ability to verify true identities is still difficult.  As the 2015 tax dollars are reconciled, it is my hope that all the “responsible parties” take notice of the risk citizens face with their tax information, and take the necessary steps (priorities, planning, funding, execution and oversight) to ensure the best possible safeguards in protecting it.

 

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Gamifying tax preparation is the biggest threat to the US tax system

Who would have thought this could lead to the gamification of tax evasion? The Odyssey 2 was basically the GoBots of 80's gaming systems. Photo by Flickr user moparx

Who would have thought this could lead to the gamification of tax evasion? The Odyssey 2 was basically the GoBots of 80's gaming systems. Photo by Flickr user moparx

Tax preparation software has encouraged the gamification of tax evasion, making it tantalizingly simple to bump up the value of a tax refund. This is alarming, but it's easy to see how we got here.

I love classic video games.

When I was a kid, Atari made the best games, but hard core fans may remember ColecoVision. Or maybe Odyssey 2? My brother Brendan and best friend Lee played K.C. Munchkin on an Odyssey 2 for hours on end. We had epic battles to determine who was best. The competitions got heated. We bickered. We developed (secret) strategies. We practiced after school and on weekends. All in a quest to get the highest score.

Little did we know that we were training ourselves to become tax cheats.

Huh?!!

Yep, there is an intriguing connection between Space Invaders and tax evaders.

Fast forward from 1982 to 2016. Video games have evolved from child’s play into a $93 billion a year industry.

The influence of these games is broader than the immediate market for them. Concepts from video games have found their way into many aspects of society. This phenomenon even has a name – gamification. Dictionary.com says that gamification is “the process of turning an activity or task into a game or something resembling a game.”

Ever put a purchase on a credit card, just to get enough points for a free flight? That’s gamification.  Wear a pedometer, track the number of steps you take, and try to beat what you did last week?  That’s gamification.

Here’s another example, just in time for Tax Week 2016. Ever use tax software to file your taxes? I have, and the elements of gamification are hard to miss. Here’s my experience.

I enter the information from my W2. The software shows me a really cool animated “refund calculator” at the top of the screen. It tells me I owe $3,054. Next, I enter my wife’s W2. The refund calculator fires up again… now I owe $4,490. “Damn! That can’t be right!”, I mumble to myself.

With furrowed brow, I turn to other documents in my pile. I grab my mortgage interest statement and enter the information. The refund calculator begins to flash and turn. But wait! This time the number is going DOWN! $4,000… $2,000… it begins to slow, as it settles on $1,334.

Next up? Property taxes. I live in New York, so this is a big number. I enter the figures, and as I press the enter button, my eyes move quickly to the refund calculator. It starts to move…. $1,000… $500… it turns GREEN!... $500… $1,000… settles on a refund of $1,083! I start to get excited.

Now to a pile of charitable donations. They are all small amounts. $50 for cancer research. $100 for Covenant House. $30 for St. Baldrick’s. Each time I enter a donation, my refund goes up by a few dollars. I like that I can make my refund amount go up, just by pressing a few buttons. It's fun... almost like a video game.

A thought crosses my mind. What happens if I add a donation for $5,000? What would THAT do for my refund? I change the cancer research donation from $50 to $5,000. The refund calculator goes wild. “Wow!”, I think to myself. “Wouldn’t it be nice to have a refund like that?!”

That's a reaction that should be concerning to tax administrators.

I could continue with my story, but by now, you see the point. Tax software firms have gamified the chore of filing taxes. That’s good, because it eases the burden of filing. But, gamification has big consequences. Everybody wants a big refund. Gamification makes it easy to engineer one for yourself.

Think gamification of tax evasion isn’t a big deal? Consider that 27 million filers use tax software to file their taxes. That makes it amazingly easy for a large segment of taxpayers to get a refund amount larger than they should.

To me, that makes tax preparation software the single biggest threat to the integrity of the US tax system. It's easy to (literally) 'game the system.'  Tax administrators and policy makers should be greatly concerned about the effect of gamification on compliance.

As fraud fighter, and as a founding member of the video game generation, I sure am concerned. You should be, too.

NOTE: The author dutifully complies with all tax laws, and the tax line item figures presented in this blog are for illustrative purposes only.

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The underground economy, aka can you spare $2 trillion?

Helpful map of tax havens, in case you want to contribute to the underground economy.

Helpful map of tax havens. You too can contribute to the underground economy!

Welcome to the dreaded week when procrastinators embrace their fates as responsible taxpayers. While you and I may be paying our taxes, to no surprise, as much as $2 trillion in the underground economy will go unreported this year in the U.S.  While that number may seem shocking, and the percentage is higher than some countries, it's still low compared to countries like Greece, Italy and Romania, where 30-48% of all business is part of the underground economy.  No wonder Greece is failing at yet another bailout.

It's one thing to let economists, even ones that study this on an ongoing basis, weigh in on the problem of the underground economy.  However, the IRS themselves admit that the "tax gap" is at least $450 billion and only 83.1% of taxes owed are reported and paid.  Or at least was, the last time they reported on it in 2012.  I can't wait to see how much it's grown by the time they update it early next year.  Stay tuned!

Okay, so it's one thing to talk about this in big numbers, another to bring it home.  What the heck is this underground economy anyway?  The answer is many different things.  It ranges from a construction company that does part of its business through official bids and payments, but also does "side projects" with cash payment for a significant discount, to individuals working a second (or first) job for cash "under the table" with nothing reported.  It's restaurants that use "zappers" to delete some of their credit card transactions, to people that rent out houses or rooms on AirBNB, HomeAway and VRBO and don't report it as income.  But it's not just little people and small businesses playing these games.  It's just that the big boys play it differently - through methods like shell companies and offshore tax havens that range between dodgy and outright criminal.  In recent weeks, that was pushed into the limelight as the "Panama Papers" were released.  Fallout from that has already taken down Iceland's Prime Minister.

All of this avoids not only the federal taxes, but state income and sales tax as well.  That doesn't just hurt some big government that might be thousands of miles away and wasting your money, but hits home very directly.  The school your children goes to becomes underfunded, cuts teachers and hurts education and the future.  The bridges around the state are in severe disrepair, crumbling, and sometimes just falling into the river.

Why is this a significant and growing issue?  Well, part of it comes back to a sense of right and wrong within a given country.  This comes back to that study I mentioned earlier that showed how badly Greece is doing.  Countries in northern Europe tend to rank the highest in this area - think Finland, Sweden and Norway.  A high sense of community and social responsibility equals a low rate of fraud, tax evasion and underground economy.  Now think for a moment.  How many people do you know that shave a little off their taxes?  If not now, in the past.  The answer is telling.  Another great example is a recent study that showed 1 in 5 employees worldwide would sell their work passwords to a third party. The U.S. fared well below average in that respect, with a full 27% willing to do so.

What can be done about this, outside of trying to teach morality in school?  Start using government and external data wisely.  Bring together sets of information that data aggregators like TransUnion have, along with information from utilities, government social programs, licenses and taxing agencies.  Then, layer strong analytics on top of it.  Start drilling down from that $2 trillion in high level economic activity that's missing and begin finding the gaps.  Belgium took this approach to help deal with fraud in their version of a sales tax, known as value added tax, or VAT, and eliminated 98% of the problem.  The HMRC, an equivalent to the IRS in the UK that handles income and sales (VAT) tax also undertook similar steps.  The problem is real, quantified and growing.  Instead of saying "Hey Brother, can you spare $2 trillion", let's go find it and collect it.

Care to join the conversation? Reply to this blog directly, or reach out on Twitter @carlhammersburg

 

 

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Tax fraud detection & good reporting can save your job

Don't get these cookies this year. Use fraud analytics. Image by Flickr user m01229

Don't get these cookies this year. Use tax fraud analytics.
Image by Flickr user m01229

Tax fraud doesn’t just steal money from taxpayers, it can strip people of their livelihoods and reputations.

When a manager in a tax agency’s property tax division stole nearly $50M in property tax refunds, the media and external auditors asked:  “How did no one notice this?”  The woman started small, then took larger and larger property tax refunds over a 15 year span.

Her supervisors were terminated because it happened on their watch. They didn’t put the right controls and reporting in place to prevent tax fraud, or even recognize it was happening.  These 20-year agency veterans didn’t know what they didn’t know – and ended up as collateral damage.  The in-house economists did notice the woman’s average property tax refund was trending upward but attributed it to increasing property values during the real estate boom.

The real problem was simply a lack of good reporting.  If they’d had more advanced reporting capabilities and tax fraud detection analytics in place they would have been able to figure out pretty fast that rising property values were not responsible. It wouldn’t have gone on for 15 years.  In fact, it probably wouldn’t have gone on for more than a few months before it was discovered.

The kicker? The tax agency didn’t find it themselves. She was caught when a Bank of America bank teller noticed something suspicious and called the FBI.

In another case, four Oregon Department of Revenue workers were punished after a woman successfully claimed a $2.1 million state tax refund. The employees failed to examine her return, not once but four times. The embarrassing oversights were blamed on an overwhelming workload.

Good reporting from a solid tax fraud analytics solution helps ease the burden on tax agencies, and would have revealed these fraudsters quickly. So, with jobs and reputations on the line, why aren’t more tax agencies adopting these technologies? To answer that, we have to look at how we got here…

Most of the internal systems that tax agencies run on are at least 20 years old. Tax agencies are now replacing them in favor of commercial off-the-shelf products which use business rules engines to do their heavy lifting.  These systems are a bit more out-of-the-box but still require several years, a very large financial investment (e.g. $30-50M), and teams of 30 or more people to implement at a typical state tax agency.  These projects consume tax agencies’ attention, financial resources and their best staff.  They’re trying to implement these new systems while, simultaneously, fighting identity theft and other types of tax fraud.  With battles raging on multiple fronts, tax agency workers often feel overwhelmed and disheartened.

A side-effect of having antiquated tax systems for so many years is that internal reporting is limited or non-existent. Tax agencies have struggled to get real-time reports for management that tell the story about what is going on in the agency.  They might have basic reports of inventories and even some executive dashboards. However, getting their data into more advanced reporting tools - including tools that do advanced analytics, statistics, and forecasting - has been a struggle for them.  Yet, it’s not for lack of wanting.

The alarming tax fraud examples above are the tip of the iceberg. The moral of those stories is that top-notch reporting and fraud detection analytics aren’t “nice to haves”. They are necessities for executives and anyone in a position of managing others, or an organization. In fact, your job might depend upon it.

 

 

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In defense of tax agencies: Refund fraud causing delays

By Pictures of Money, Flickr

By Pictures of Money, Flickr

With tax week quickly approaching, tax agencies have been issuing press releases alerting the public they’re holding tax refunds for review longer than in years past. This is a departure for tax agencies.  Tax agencies have traditionally lived and died by refund cycle time.  Refund cycle time, or getting refunds out in 7 days or less, has been a key performance indicator for them.

Things are shifting. Tax refund fraud – and specifically the use of stolen or false identities to obtain tax refunds – has risen the past 5 years.  It’s such a notorious problem that tax agencies have no choice but to communicate more openly with the public about their internal stressors (and processes) related to fraud detection.  Tax agencies are admitting they need more time to issue refunds. Taxpayers are frustrated, forcing agencies to defend against claims of ineptitude.

These claims are completely unfounded.  Rather, the delays signal tax agencies are getting BETTER at issuing refunds – not worse.

As the refund fraud problem has grown, tax agencies have been shifting their resources away from improving “core” operations (e.g. tax return bar coding and returned-mail automation) to tools that deter and detect refund fraud.  For better or worse, tax agencies have morphed into full-time, year-round criminal investigation shops. It’s almost as if the IRS has magically turned into the FBI overnight.  This new responsibility often lies within the agency’s criminal investigation division (CID), which is typically small relative to the overall size of a tax agency. The people who work in CID are trained as law enforcement officers - not computer scientists. They are trained to carry weapons – not to write database queries.  So what is a tax agency to do when more and more often the first evidence of the crime shows up in the data?

Tax agencies are arming CID teams with something other than weapons. They’re giving them easy-to-use, tax fraud detection solutions.  These solutions scan terabytes of tax data on a nightly basis and look for patterns of taxpayer behavior that correlate highly with tax refund fraud.  When the CID team comes in the next morning the fraud cases are ready for them to triage and investigate.  These tools replace what used to be a pile of chicken scratch-covered sticky notes on an investigator’s desk.

So, then why the big refund delay?  Shouldn’t it be faster to detect refund fraud?  Well, in fact, it is faster.  Way faster.

The use of fraud analytics is extraordinarily prevalent in the banking industry.  Every time you use your credit card, your bank is running real-time anomaly detection models to see if the transaction seems abnormal for you. SAS does this for large banking institutions for every single card swipe, every day, across the globe.  Within seconds the answer is returned to the point of sale.  The service level agreements with one of our banking clients require us to send that signal back in less than 4/10ths of a second.  This is done for 4.5 million transactions a day.  This gives you a sense for the amount of data that an advanced analytics fraud detection solution can handle, and just how quickly it can provide an answer.

So the fraud detection system is not the source of the lag time.  Fraud analytics done right should never add time to your refund cycle.  The lag time is a result of how many more fraud alerts are being generated.  Therefore, refund delays should give the public confidence in their tax agency.  It’s a measure of how well their taxpayer dollars are being protected by those who were hired to do just that.

The general consensus in the tax industry is that refund delay in favor of fraud detection is a change for the better and one that is here to stay.  Maybe next year we can stop calling them “refund delays” – and just call them what they are:  refunds done right.

To learn more, join the International Institute for Analytics and SAS for a webinar on April 21st. During Analytics to Fight Tax Fraud, I’ll talk about how some tax agencies are getting ahead of the curve and using advanced analytics to detect refund fraud.

 

 

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