Document AI[1] or Document Intelligence is a technology that uses natural language processing (NLP) and machine learning (ML) to train computer models to simulate a human review of documents.

NLP enables the computer system to grasp the relations between contents of documents, including the contextual nuances of the language within them, before extracting the information and insights contained in the documents. The technology can then categorize and organize the documents themselves.[2]

Document AI is used to process and parse forms, tables, receipts, invoices, tax forms, contracts, loan agreements, financial reports, or any semi-structured documents.

One of the applications of Document AI is in real estate. A model that unifies existing approaches and leads to the development of a document classification standard and automated information extraction through AI will become instrumental for enhanced and innovative business models and products in real estate. The availability of up-to-date and complete documentation for transactions, such as real estate investments, can help reduce safety margins due to better estimated risks.[3]

Key Features

Document AI uses machine learning to extract information from documents in digital and print forms. Document AI is able to identify text, characters, and images in different languages, thus enabling users to gain insights from unstructured documents. Using the data from the documents allows Document AI users to make better and faster decisions regarding the documents. The technology makes the process of analyzing documents more efficient by automating and validating the data for the workflows.

Common Uses

  • Freeing up employees for higher-value tasks.
  • Using AI to check for anomalies in new invoices from old customers.
  • Spotting fake currency and fraudulent checks.
  • Fast-tracking the mortgage workflow process.
  • Automating the monitoring of loan portfolios to manage credit risks.
  • Enabling firms to automate the impact assessment of regulatory changes on their contracts.
  • Analyzing previously inaccessible data siloed in documents to make informed business decisions.
  • Streamlining the consumption of receipts on a worldwide scale.
  • Increasing the reliability of business information by decreasing errors resulting from manual data entry.

References

  1. ↑ Cui, Lei; Xu, Yiheng; Lv, Tengchao; Wei, Furu (2021). "Document AI: Benchmarks, Models and Applications". arXiv:2111.08609 [cs.CL].
  2. ↑ "Why Digitizing Documents has been Accelerated by COVID-19 Pandemic". eWEEK. 15 January 2021. Retrieved 2021-02-11.
  3. ↑ Bodenbender, Mario; Kurzrock, Björn-Martin; Müller, Philipp Maximilian (April 2019). "Broad application of artificial intelligence for document classification, information extraction and predictive analytics in real estate". Journal of General Management. 44 (3): 170–179. doi:10.1177/0306307018823113. ISSN 0306-3070.
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