In the traditional approach to tax filing, multiple teams of tax specialists source relevant data, and undertake analysis and reconciliation for tax computation. This people-intensive process requires a significant investment of time and effort for rigorous analysis and high quality review before tax filing on an industrial scale.

Infosys blends automation, digitization, and process standardization to transform tax computation for professional services organizations. Our solution leverages automation to extract relevant taxation data from structured, semi-structured, and unstructured documents across multiple sources. Further, we use bots and smart workflows to ensure accurate application of tax rates for diverse financial transactions.

The Infosys Automated Text Extraction (ATE) solution for tax computation capitalizes on Tesseract, an open-source optical character recognition (OCR) engine. It defines elements of tax data relevant for extraction from source documents. This versatile engine uses pre-trained long short-term memory (LSTM) to extract text from images. Significantly, it standardizes the input format of taxation data and eliminates encoding issues.

Data discovery and acquisition

Data discovery and acquisition

  • Identify documents from multiple sources
  • Extract document metadata
  • Create document catalogue
Data validation and analysis

Data validation and analysis

  • Identify and analyze initial attributes
  • Use pattern recognition techniques (NLP) to extract attributes
  • Adopt custom document classifier for attributes that do not follow a pattern
Solution development and configuration

Solution development and configuration

  • Create a repository of normalized attributes referenced in documents
  • Create templates with
  • custom attributes to ensure configurability
Training, review and implementation

Training, review and implementation

  • Train machine learning model using historical data
  • Undertake manual review of initial set of extracted attributes

Our automated solution for tax computation uses machine learning for extraction of data from diverse sources and for accurate reconciliation.

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Our automated tax computation solution capitalizes on machine learning to identify relevant data in a document (payslip, invoice, order form, client information form) and establish correlations with keyword patterns. The corresponding dataset is shared with a machine learning model to classify results. The outcomes can be exhibited as text / comma-separated values (CSV) / JavaScript Object Notation (JSON) summaries, which can be exposed as application programming interfaces (APIs) for downstream systems and processes.


Challenges & Solutions

Blockchain ensures accuracy in tax computation and traceability of taxation processes.

Automation approach blending natural language processing (NLP), machine learning and artificial intelligence (AI) minimizes human intervention and eliminates errors.

A data-first approach to taxation augments streamlining of processes to reconcile tax data.