Deep Learning x Tax : A Logical Revolution

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Deep Learning has remarkable potential to revolutionize tax globally. This ability to not only synthesize complicated international tax laws in real-time but create meaningful extrapolations of tax positions – individual, business and government – will result in a revolutionary new way to approach tax. The result is more accurate, efficient and adaptable solutions for complex tax related challenges than were previously possible by humans alone.

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But what is Deep Learning and why is it so powerful ?

Deep learning uses neural networks with multiple layers (thus the “deep”), so it can learn not just simple statistical patterns, but can learn subtler 'patterns within patterns'. These neural networks attempt to simulate the neural thinking behavior of the human brain - allowing it to “learn” from large amounts of unstructured data. While a neural network with a single layer can still make approximate predictions, additional hidden layers can help to optimize and refine for accuracy.

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Deep learning drives many artificial intelligence (AI) applications and services that improve automation, performing analytical and physical tasks without human intervention. Deep learning technology lies behind everyday products and services (such as digital assistants, voice-enabled TV remotes, and credit card fraud detection) as well as emerging technologies (such as self-driving cars).

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Deep learning eliminates much of data pre-processing that is typically involved with machine learning. These algorithms can ingest and process unstructured data, like text and images, and it automates feature extraction, removing a large degree of dependency on human experts. The ability of DL algorithms to ‘simultaneously process’ vast amounts of unstructured data – for instance international tax laws – means that they can process a significantly larger amount of information that.

 

This is something impossible for humans to do within the time frame, let alone globally.

 

Intricate Tax Laws meet Algorithm.

Data is agnostic. The grumpy old mathematician would say: all data is the same – a random forest works the same way whether it’s a biology dataset or a finance dataset. Data has no opinion.

The underlying data that forms the training set is the important driver of the outcome of the model. Therefore with literally infinite compute available globally, well trained models can verify and authenticate information in real-time. Combined with a human element to verify (and identify ‘hallucinations’) these models begin to learn with more use and better feedback.

 

What about Generative AI ?

Generative AI Generative AI, particularly in the form of generative adversarial networks (GANs), offers innovative solutions for synthesizing complicated tax law scenarios. GANs can generate synthetic tax law cases, scenarios, or precedents, simulating various tax-related situations. This enables tax professionals, policymakers, and educators to explore hypothetical cases and analyze their implications.

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Additionally, GANs can be used to generate hypothetical tax law changes and evaluate their potential impact on businesses and economies. This not only reduces the manual effort required for scenario analysis but also enables better preparedness for potential legislative alterations.

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A business for example may use GANs to formulate projections on their current financial performance and strategic alignment depending on the outcome of tax related optimisation. This forward looking lens is capable of not only predicting current compliance related activities, but models multivariate options depending on what changes may also be likely to occur. 
 

How is Deep Learning Superior to Humans in Tax ?
 

1. Speed and Scalability: Deep learning systems can process vast amounts of tax law documents rapidly, making them superior in terms of efficiency and scalability compared to human efforts. The ability to ‘cross-evaluate’ multivariate scenarios in real-time promises to optimise tax positions in a way that was previously impossible.
    

2. Consistency: Deep learning models provide consistent interpretations and analyses, avoiding the potential for human bias and errors that can arise due to fatigue, changing interpretations, or variations in expertise. The quality of human expertise also depends on availability, training and experience – whereas with deep learning models, the modelling becomes increasingly ‘learns’ consistent behaviours through usage.
    

3. Adaptability: These technologies can quickly adapt to changes in tax laws, staying up to date with new regulations and adjustments, ensuring compliance and accurate interpretations. Especially regarding international tax compliance – where expertise may be scarce – the ability to scenario build and rapidly iterate on changes, allows multiple options to be presented.
    

The advantages of deep learning over human capabilities in terms of speed, accuracy, consistency, scalability, and multilingualism are profound. As technology continues to evolve, the future of managing complex international tax laws lies in harnessing the power of deep learning, promising more efficient, reliable, and cost-effective tax compliance and planning solutions for individuals and organizations alike.

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A symbiotic relationship between humans and AI systems clearly represents the optimal approach for navigating the ever-evolving landscape of tax laws, ultimately benefiting individuals, organizations, and governments worldwide.

 

Peter Toumbourou
Charleston Advisory Group
2023