by Paulo H Leocadio

In the Internet era, Big Data is term applied to datasets overflowing the boundaries of traditional database technologies. It brings together the power of computers and repositories with some of the largest inventories of ultra-individualized information to provide insight into every aspect of modern life and human behavior. This is a world where an estimated 2.5 quintillion bytes of data are being produced every day; ninety percent (90%) of the world’s data has been created in the last two years. It is also a world where huge challenges and gigantic opportunities are created for regulators.
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Data scientists, policymakers, and tax experts are looking into ways of using big data mechanisms, tools, and solutions to advance the study and reform of taxation. The debate on plans for comprehensive tax reforms are currently underway by US leaders, while they gain increased access to big data analytics to inform their policies, priorities, and strategies.

The scale and detail of data gathered by services or government agencies like IRS needs to be meaningful and reflect the reality of their user base. IRS is ubiquitous for the USA and its citizens. It is present in our daily lives, impacting life decisions and even routine. IRS can gather and organized data about most of the population and all types of businesses. For tax data, nearly every American citizen and corporate entity is responsible for paying and reporting various tax information, disclosing a considerable amount about themselves. That means an unimaginable amount of data is available for gathering, organizing, managing, and analyzing. This is the very definition of big data.

IRS receives and processes more than 250 million tax returns every year. Budget cut and workforce attrition has negatively impacted IRS capacity as it fights an estimated tax gap of more than $450 billion annually. Working smarter is the solution for more efficiency and more tools to battle tax fraud and tax evasion as identified by the IRS’ Criminal Investigation Division. 

Big data characteristics (for instance: volume, velocity, variety, veracity) also mean that big data employs significantly large storage space from diverse sources, stored in different formats, with different update intervals. Tax fraud analysis use of big data is a game changer as methods, techniques and technologies are released. Data mining through analytics is employed in the knowledge discovery in databases process, deploying predictive and descriptive tasks.

Through data mining, fraud investigation analyzes large volumes of data to discover unrecognized or unperceived patterns in data sets by leveraging statistical analysis and database technologies to find those patterns. 

Predictive tasks work with machine learning and related technologies to make a prediction for each observation resulting from data-mining. Prediction employs regression analysis to examine relationships between independent variables and dependent variables. Financial complexity demands the volume of variables provided by big data to make more accurate predictions. The statistical techniques for these include linear regression, multivariate linear regression, nonlinear regression, and multivariate nonlinear regression (as well as the more complex logistic regression, decision trees, and neural networks). Other, more complex predictive techniques of data mining appropriate to fraud detection or prevention include rule-based fuzzy reasoning, genetic algorithms, Bayesian belief networks and fuzzy neural networks.

Descriptive tasks, which include association rules and cluster analysis, describe the data under analysis. These tasks can be used to create models of behaviors (or transactions) that could fall under suspicious categories. Descriptive tasks might be types of association rule analysis including multilevel association rules, multidimensional association rules, and quantitative association rules. Association rule algorithms generate rules describing potentially fraudulent situations. Cluster analysis collects data into related subsets patterns, a discovery of patterns that can be used to discover or prevention financial fraud. 

Complex and large-scale analytics such as what IRS fraud detection employs, requires big data, or the use of multiple data sources. An audit executed to discover fraud would integrate large internal and external datasets (demographics, taxpayer or corporate profiles, previous filings, call center data, and audit histories). The data analyzed could include many years of historical data as well as external data. The volume and variety of data would be difficult to analyze without the analytics tool-set of big data and the work of data scientists.

There are sources indicating the deployment of “spiders” by the IRS (automated computer programs) to review social media sites. Reports have also indicated the adoption of phone tracking technologies (for example, “Stingray”, a cell site simulator). Also, IRS keeps considerable volumes of data through utilizing more traditional technologies (for example, NRP and Individual Master File database). Independently from the accuracy of these sources and reports, a solid conclusion is that IRS has access to many data sets. 

IRS is cross-referencing and mining these data sets to execute run pattern recognition algorithms so that trends can be identified enabling the understanding of the relationships in the data. IRS has employed several advanced techniques and tools in these efforts (including anomaly detection, advanced clustering and neural networks), with the objective of improving case selection and coordination among IRS divisions. Data analytics and predictive policing will help the IRS identify tax-reporting anomalies and identify tax evasion on a larger scale.

Within the accounting and tax law profession, big data and analytics are associated with automation. Offloading data management and processing power to computers translates to less manual labor to dissect numbers, construct models, and conduct independent analysis. This does not at all the end of opportunities for those tax professionals. It represents instead new beginnings, fresh opportunities, new knowledge, and a renewed importance of the experts working with these machines.

The partnership between data scientists, programmers and law professionals with masters in law in taxation (LLM) degrees is necessary to ensure the right questions are being asked about the wealth of information found through big data, to interpret the feedback derived from algorithms and data queries, and to provide guidance for the development of future policies.

In the accountant profession, Master of Taxation is offered for those professionals seeking advancement in the field. Like previous example, this expertise is important to hoard the data being collected, managed, and organized, to ensure that the analytic potential of available technology tools is leveraged optimally. As big data enables policymakers to be rely more on scientific methods while working on tax reform, experts in tax law and accounting continue to be needed to make the new robust information accessible, meaningful, and useful.

Big data umbrella with the analytic capabilities it provides, has already demonstrated disruptive for different industries. In the internet age, very few entities (people, businesses, governments) living in the 21st century are isolated from the impact of big data. Tax processes, policies and practices are no exception. Taxpayers are looking for solutions and opportunities to make tax planning and compliance more seamless and automatic. Government, likewise, is investing in new ways to collect, organize, and utilize big data to enforce and reform taxes in the United States. Human creativity together with with analytic capabilities of modern technology, represent a brand-new era for taxation.

Over the last ten-year period, IRS investments in big data analytics will result in good return, in areas such a international tax enforcement through the collaboration with international tax enforcement efforts (country funded programs, document leaks, among others). Information reporting and information-sharing agreements have led to important structural changes in the global collaboration of tax-related information. These results will only further strengthen the new initiatives (highlight to the Joint Chiefs of Global Tax Enforcement, known as the “J5”).

Positive results produced by those investments will pave the path to enable new efforts to focus on areas that are ripe for development. Certainly, the one example at the top of anyone’s list is Cryptocurrency. Cryptocurrency-related tax compliance is unknown, most likely enormous. According to IRS reports, less than 1,000 taxpayers reported gains with cryptocurrencies like Bitcoin during the 2013-2015 period. The IRS is actively mining newly received data from actions on different exchanges servicing the USA.

For the IRS, the future of fighting tax fraud has arrived. Among the different processes, tools and efforts, IRS has embraced big data analytics, only seen the tip of the iceberg has surfaced. With a reported year-over-year 400 percent increase in tax fraud detection and more than 1,000 percent increase in the identification of proceeds from other financial crimes, IRS is likely to increase its skates on its bet on big data, big data technologies, and tools. 

Freeman, J. B. (2019, January). The IRS and Big Data: The Future of Fighting Tax Fraud. Today's CPA, pp. 5-6.

Klasing, D. (2019, September 2). How the IRS Uses Big Data Analytics to Catch (and Punish) Tax Evaders. Retrieved from Klasing Associates: https://klasing-associates.com/irs-uses-big-data-analytics-catch-punish-tax-evaders/

Malaszczyk, K., & Purcell, B. M. (2018, June). Big data analytics in tax fraud detection. Journal of Finance and Accountancy, 1-10.

Villanova University. (2020). Big Data and Tax Reform. Retrieved from Villanova University Tax and Business Online: https://taxandbusinessonline.villanova.edu/blog/big-data-and-tax-reform/

How many coding resources are available these days? Coding is in vogue but the options are overwhelming. This new and unpublished article by Code Wizards HQ is for my readers who are interested in kids' coding education but not really sure where they want to start. 

​A recent study found that 76% of parents want their children to end up in STEM-related careers. And with an ever-growing number of jobs requiring knowledge of new technologies, it isn’t very hard to see why. From manufacturing to marketing, most jobs today are dependent on computers for their success. And computers are dependent upon code.
 
That’s probably why so many parents are hoping to get their kids into coding.
 
As many as 85% of parents are encouraging their children to learn to code, according to one study. But despite wanting their kids to learn, many parents are at a loss about the best resources available to help kids to code. Where can a parent start when they want to help their child dive into this new field, especially if they don’t know much about coding themselves? We're here to help! 
 
We’ll help you narrow down the options by explaining the different categories of resources out there. Then, you can decide which resources best meet your needs.

One of the best all-around methods for teaching kids to code is taking a coding class. Coding classes come in a variety of formats and price ranges, each with their own advantages and disadvantages. That can make it tricky to pick the right coding class.
 
For instance, in-person classes have teachers that regularly interact with their students. This is a huge advantage because it means that students can receive real-time feedback on their questions! However, many live coding classes have limited schedules and require you to live nearby. Depending on where you live, it can be hard to find coding classes near you.
 
Online classes, on the other hand, allow for flexibility in both scheduling and location, but are often pre-recorded. That can limit their ability to respond to questions quickly, or even at all!
 
Of course, there are other classes that combine the best of both worlds by hosting live classes on an online platform. These allow for flexibility of location and real-time feedback. 

Coding apps can be a great supplemental resource for kids learning to code. Many kid’s coding apps use bright colors, animations, and fun gameplay to encourage kids to learn the basics of coding. They can be great for getting an understanding of what a conditional or a loop is, but few of them get into the nitty-gritty of real-world coding.
 
If you are wanting to get the most out of coding apps, it's better to use them in conjunction with an established coding course. That way, the base concepts taught in the coding apps can immediately be used in a more and more specific context.

Another useful supplemental activity is a coding challenge.
 
Coding challenges, or hackathons, are hosted events where coders get the chance to simultaneously reach outside their coding comfort zone and show off their skills. It adds a competitive element to the learning process, which can be a great motivator for many kids.
 
Usually, there is a specific challenge or set of challenges that must be overcome through the use of code. The difficulty levels can vary greatly between challenges, but that’s part of the fun! Challenges are often teacher-directed events, too. That way, even if a challenge proves too difficult for a kid to figure out on their own, the teacher is there to help them learn and progress.

​While coding classes are the fullest featured and most effective way to learn the intricacies of coding, they do take a while to get through. Sometimes, it can be an effective use of time to move quickly through the basics so you can spend your time on the more advanced skills.
 
Which is where coding camps come in.
 
Coding camps, or coding bootcamps, are intensive courses designed to get their students through the basics. That way they can focus on the lessons that they really want to be learning. They save valuable time by teaching a wide variety of coding skills quickly.
 
Since they only last for a short period of time, they are a great fit for students who have a chunk of free time available to dedicate to learning a new skill. Summer break is a great time for this.
 
More intensive coding camps can help students to master the basics quickly and take higher-level classes sooner. It's a great way to accelerate their learning.

In today’s connected world, it may seem odd to mention books as being a good coding resource, but there are some definite advantages to the medium.
 
For one, no one can be online all the time, and books can be a great resource even when screen time is over. Books tend to be a lot more in-depth than many online guides because the authors know that all the necessary information needs to be included upfront. There’s not the option to link to throw in a link to supplemental material.
 
And, it can be a lot easier to flip between a physical book and what’s happening on the screen without losing your place. Though, that can be a matter of preference.
 
Of course, the effectiveness of any book as a coding resource depends greatly on which book you’re using. It can be difficult to know if a specific book will cover the things your kid wants to learn. Make sure to choose a book that’s listed as a reliable resource for kids to get the most value.

​No matter where your kid ends up on their coding journey, there are amazing benefits to learning to code. And that will only become more true as time goes on. 
 
Coding, as a field, isn’t going anywhere anytime soon, and the list of amazing resources is only continuing to grow. These 5 types of resources are a great way to get started, but there are so many other great resources out there. We encourage you to find somewhere to start and explore from there!