Documents structured in seconds
Financial Information Providers
Financial information providers always need to provide added value for their clients. And much of their valuable information is in PDF form. The on-going challenge is how to continually improve their capture processes’ accuracy, speed, and cost effectiveness.
Zanran has worked with the world’s largest financial information providers. These firms use Zanran’s state-of-the-art AI capabilities to capture and convert the content of documents into machine- readable structured formats.
Auditors use PDF versions of Annual Reports and Financial Statements. The challenge is that the numbers within them need to be checked for errors and numerical consistency. In the past this was done manually using a calculator, or by crudely extracting into Excel.
Some large audit firms rely on outsourcing centres to keep costs down. This process still takes time, is expensive and is error prone. Zanran has radically overhauled this approach with its AI
Accounting firms are now using Zanran to automatically and accurately extract financial data from tables in financial documents. The extracted data becomes the starting point for a pipeline of automated checking and referencing processes.
Other firms are using Zanran’s ‘Automated Audit Checker’ solution that allows auditors to check and validate totals in tables very quickly. For an introduction to the Automated Audit Checker, please watch the video below.
Digital transformation is taking place in all industry verticals. It is about preparing businesses to operate in an increasingly digital world.
For most, digital transformation is a cultural shift to a more agile and more intelligent way of doing business. Increased access to digital data and technologies, such as advanced analytics and
artificial intelligence (AI), creates new technology segments such as data discovery and intelligent search.
Zanran helps companies to digitally transform and to expand their digital strategies; it helps them to turn their documents into machine-readable outputs at the simplest level, making them digital-ready.
Zanran prepares a company to take its next steps in digital transformation, allowing them to weave the structured data taken from reports and documents into their processes and workflows. This supports their RPA, analytics and data discovery goals.
A lot of archived scientific data is held in PDF form – scientific papers, drilling reports, clinical trial data, product test data, etc. In this unstructured format, it is hard to find and query the data.
Zanran’s AI engine extracts, digitises and labels this information – both text and tabular data - at scale. This makes the content available for querying and for a wide range of other downstream processes.
The automated extraction of medical facts and entity relationships (symptoms, diseases, molecular targets...) in documents enables facts to be digitised and embedded into a database structure (Knowledge Graphs) for querying, predictions and inference. In that context, Zanran's engine is an essential tool to capture and transfer scientific facts from documents to Knowledge Graphs.
There is a growing trend towards utilising RPA bots to do more manual and repetitive tasks, including data entry, screen-scraping, checking records, doing calculations, etc. . However, bots have limited capabilities; they can only handle simple tasks and actions, such as OCR and exporting data. Bots themselves cannot manage complex tasks like determining the structure and layout of unstructured documents and then extracting the data accurately.
Zanran's document automation engine automatically provides the required structured output data to the RPA platforms through connectors and REST APIs - for further value-add processing.