Intuit Tax Knowledge Engine

Intuit just published a technical overview of their new Tax Knowledge Engine, the key innovation to make TurboTax smarter and more personalized for 37M+ consumers. First they listed key limitations for the traditional approach that are common for many rules-based systems. Then they describe a fundamental paradigm shift in representing complicated tax compliance calculations and rules at scale via knowledge graphs and connect associated user data together, instead of hard-coding tax logic in procedural programming code. Link The key limitations for the traditional approach:

    • Procedural programming – done by programmers who rely on tax domain experts for the logic spec.
    • Tops-down sequential execution – have to re-calculate the entire return even with a single input change.
    • All inputs required for complete calc – need to collect all information upfront.
    • Implicit explainability hidden in code – cost prohibitive to explain calc logic explicitly for each customer.

The key innovation of the Tax Knowledge Engine is to capture expert knowledge on tax domain into the following two knowledge graphs:

  • Calc Graph: a comprehensive graph with tens of thousands calculation statements represented as interconnected calc modules, with each as a calc function node connecting input and output data nodes together.
  • Completeness Graph: a special type of graph to represent complicated decision tree logic to determine applicability of specific tax topics, such as eligibility of earned income credit, based on user data.

This entry was posted in Artificial Intelligence, Business Rules, Explanations, Human-Machine Interaction, Knowledge Representation, Logic and AI. Bookmark the permalink.

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