According to McKinsey, employees spend 1.8 hours every day – 9.3 hours per week, on average – searching and gathering information’.
This is even more true in M&A Due Diligence where one of the main objectives is to find information, which can be challenging. The time pressure is especially high during the due diligence stage which increases the likelihood of missing certain information.
That is where Machine Learning (ML) comes in. This whitepaper explains why NLP approaches to unstructured data (documents) are so effective.
Points of discussion include:
- Traditional Search Methods: How do we traditionally search for information? What are the limitations?
- Machine Learning-Based Search: Highlighting how ML-based search works in Finding Libor Clauses (Case Study)
- Data vs Process: Enabling the Machine Learning process, without relying on external data.