There’s a certain myth that floats around office halls and boardroom PowerPoint: that artificial intelligence is the future. The truth is more layered. AI isn’t some shimmering mirage on the horizon—it’s already here, buried in your dashboards, humming through your databases, and learning faster than most of your team. And while some businesses are still figuring out how to spell “machine learning,” others are already building competitive moats out of it, transforming chaotic rivers of raw data into clear, actionable insights that sharpen every decision.

You know the feeling: spreadsheets stacked like leaning towers, databases so deep they need their own zip code, and analysts stretched thin trying to make sense of it all. Here’s where AI thrives—not as a magical fix, but as a grinder. By automating tedious, repetitive tasks like data cleaning, normalization, and integration, machine learning models allow your team to stop sifting and start thinking. It’s the shift from grunt work to groundwork, where machines handle the rinse cycle and humans handle the reasoning.​

The human eye is good at spotting trends. But give it a few million rows of customer transactions and even your sharpest analyst turns foggy. AI models, however, feed on that volume. Whether you’re looking for consumer behavior patterns, operational bottlenecks, or anomalies in financials, these systems can surface what would otherwise be invisible. And because they learn as they go, their performance improves over time—think of it as the only team member who gets smarter the more overwhelmed you are.

Gut instinct still has a place in business—but let’s be real, it shouldn’t be driving your quarterly forecast. AI, specifically predictive analytics powered by machine learning, allows companies to look ahead with better lenses. Instead of relying on historical trends and finger-crossing, you’re running simulations based on real-time data inputs. The result? Inventory gets leaner, marketing gets sharper, and leadership decisions become a little less like dart-throwing and a little more like chess.

There’s a fine line between “tailored experience” and “how do they know I like oat milk?” AI makes it possible to walk that line with more grace. By crunching user behavior data across platforms, smart systems can customize offers, content, and interactions without making customers feel like they’re being watched. For businesses, it’s not just about engagement—it’s about relevancy. And relevancy, in a crowded market, is what separates the brand you remember from the one you scroll past.

Data breaches aren’t just costly—they’re radioactive. AI-driven security tools analyze behavior, not just access logs. That means they can spot unusual activity patterns—a login at 3 a.m. from an unknown device, or a sudden surge in data downloads—and trigger alerts before damage spreads. Think of it as your cyber watchdog, one that never sleeps and doesn’t need coffee. For businesses handling sensitive data, this proactive approach is the difference between “almost happened” and headline news.

Here’s the part that often gets lost in the hype: AI doesn’t replace human judgment—it depends on it. You still need people to ask the right questions, interpret outcomes, and course-correct when models drift. The real win isn’t AI running your business—it’s AI amplifying your best thinking. The most successful companies are the ones who understand this partnership, investing in upskilling their teams while deploying the tech to do what it does best: process, learn, optimize.

It’s one thing to use AI tools—it’s another to understand what’s happening under the hood. That’s where a structured education in data analytics and big data integration becomes less of a luxury and more of a power move. Earning an online master’s degree in data analytics doesn’t just teach you the technical nuts and bolts; it sharpens your fluency in the language of data science, theory, and real-world application. And because the program lives online, you don’t have to hit pause on your business to get smarter—you learn while you lead, building both your company and your capability at the same time.

You don’t have to be Google to use AI. You don’t even need a data science department. What you do need is an openness to reimagining how your business processes, understands, and leverages data. Machine learning isn’t some overcomplicated promise—it’s a practical tool that, when used wisely, can help you see farther, move faster, and compete smarter. ​

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/