Deep learning and the future of tax

Deborah Pianko Deborah Pianko
April 30, 2019 AI & Machine Learning

From autonomous vehicles to deepfake videos, there are many artificial intelligence and deep learning applications that are making people anxious. Do we want cars making split-second decisions between two tragic outcomes? Will we reach a point where it’s impossible to know whether people depicted in an online video are real?

Consider the idea of completely automated tax returns, with a computer predicting citizens’ tax liability and tax rates. Millions of people already put their trust in systems written by tax experts that function as a machine representation of the tax code. That is essentially how software like TurboTax works.

But what about entrusting tax returns to a computer-crafted inference (scoring) model from a deep neural network trained on millions of actual, past returns from other taxpayers?

It makes me a bit nervous, letting a computer predict my tax rate and liability with only minimal input from me. Yet while there are good reasons to be anxious, there is also great potential in leveraging AI and deep learning to help with the tax process.

Tax fraud has existed in many forms for year, but it has become particularly widespread with the huge increase in identity theft. According to the Identity Theft Resource Center’s 2018 End-of-Year Data Breach Report, while the number of U.S. data breaches decreased 2% to 1,244, the reported number of consumer records exposed containing sensitive personally identifiable information jumped 126% to nearly 450 million.

To defeat fraud controls at the state and federal levels, identity thieves need more data points. So, while the overall breaches have decreased, the drastic increase in exposed consumer records is alarming. Essentially, the fraudsters are collecting more data to improve the efficiency of their attacks.

You’ve probably heard the old saying, “garbage in, garbage out.” That also applies to deep learning fraud models  trained on historical tax return data. That training data contains everything from completely manufactured tax returns (used to commit refund fraud, following identity theft) to real citizens hiding their income and inflating their deductions. So the first priority should be ensuring tax bills are not based someone else’s trickery.

Second, we want our tax return to be just that — ours. It must be personalized for our individual situation. Not an assessment that is correct on average, across a population of individuals that are sort of like me. Neural networks used in deep learning are statistically impressive but individually unreliable.

Finally, taxpayers (and tax agencies) want transparency. We did not develop the logic of the tax algorithm — the neural network did. We do not own the logic, and therefore we cannot explain the decision of the algorithm to set a person’s tax rate at 20% or 25%.  When talking about deep learning, this is often referred to as the “black box” problem.

Compared to other types of data-driven analysis, the amount and quality of the data is more important with deep learning models. If a pattern is not in the data, it cannot be learned. Conversely, if bad patterns like fraud are in the data, they will be incorporated in the prediction. Additionally, bias and misrepresentations can be transferred to the final model.

For example, if gender or neighborhood is used as a predictor in the model, it is possible to derive higher tax rates for men or individuals living in particular neighborhoods. Even if statistically accurate on average, the model would incorporate factors that could be considered discriminatory.

Where could AI and deep learning help in tax?

While this picture of deep learning for tax preparation sounds bleak and ominous, there are places in tax administration where deep learning is appropriate and beneficial.

In a future world of computer-generated tax returns, taxation of personal and business income would be closer in concept to what is currently possible for property tax assessments, where AI and property appraisers work in tandem to derive a final value. Wake County, N.C., uses this approach. Not only does the system identify property value outliers that need to be investigated, it gives agencies an objective measure to back up their own appraisals, which is useful when citizens appeal.

The strength of deep learning is not to capture logic, but to identify patterns. Instead of trying to train an algorithm to predict tax liability or tax rate, let’s train it to detect fraudulent returns, predict taxpayers who are likely to call in for assistance or dynamically personalize the taxpayer self-service portal based on which type of taxpayer is logged in.

Let’s train algorithms to spot fraud by training them on tax returns previously classified by a human expert as legitimate or fraudulent. Teach them what fraud “looks like” on average. Once trained, the algorithm can determine the probability that any tax return (or bank account, tax preparer or group of tax returns) is fraudulent.

To address transparency concerns, we need “white box” models that inform the auditor or fraud investigator why the algorithm flagged the item as possible fraud. These are excellent and typical applications of machine learning and are used today by many advanced tax authorities around the globe.

  • Experfy Insights

    Top articles, research, podcasts, webinars and more delivered to you monthly.

  • Deborah Pianko

    Tags
    Artificial Intelligence
    © 2021, Experfy Inc. All rights reserved.
    Leave a Comment
    Next Post
    The promise of Artificial Intelligence for ERP

    The promise of Artificial Intelligence for ERP

    Leave a Reply Cancel reply

    Your email address will not be published. Required fields are marked *

    More in AI & Machine Learning
    AI & Machine Learning,Future of Work
    AI’s Role in the Future of Work

    Artificial intelligence is shaping the future of work around the world in virtually every field. The role AI will play in employment in the years ahead is dynamic and collaborative. Rather than eliminating jobs altogether, AI will augment the capabilities and resources of employees and businesses, allowing them to do more with less. In more

    5 MINUTES READ Continue Reading »
    AI & Machine Learning
    How Can AI Help Improve Legal Services Delivery?

    Everybody is discussing Artificial Intelligence (AI) and machine learning, and some legal professionals are already leveraging these technological capabilities.  AI is not the future expectation; it is the present reality.  Aside from law, AI is widely used in various fields such as transportation and manufacturing, education, employment, defense, health care, business intelligence, robotics, and so

    5 MINUTES READ Continue Reading »
    AI & Machine Learning
    5 AI Applications Changing the Energy Industry

    The energy industry faces some significant challenges, but AI applications could help. Increasing demand, population expansion, and climate change necessitate creative solutions that could fundamentally alter how businesses generate and utilize electricity. Industry researchers looking for ways to solve these problems have turned to data and new data-processing technology. Artificial intelligence, in particular — and

    3 MINUTES READ Continue Reading »

    About Us

    Incubated in Harvard Innovation Lab, Experfy specializes in pipelining and deploying the world's best AI and engineering talent at breakneck speed, with exceptional focus on quality and compliance. Enterprises and governments also leverage our award-winning SaaS platform to build their own customized future of work solutions such as talent clouds.

    Join Us At

    Contact Us

    1700 West Park Drive, Suite 190
    Westborough, MA 01581

    Email: support@experfy.com

    Toll Free: (844) EXPERFY or
    (844) 397-3739

    © 2025, Experfy Inc. All rights reserved.