What is Natural Language Processing (NLP)?

Natural language processing has been used in business for over a decade, but recent advances in transformer architectures have dramatically expanded what is possible. For regulated UK industries, NLP is particularly valuable because so much critical business data exists as unstructured text rather than neat database rows.

A compliance team at a mid-market financial advisory might receive hundreds of emails, chat transcripts, and call notes each week that need monitoring for conduct risk. NLP can classify these communications by topic, flag potential issues, and route them to the appropriate reviewer. What previously required a team reading every message can now be handled as an automated triage process with human review focused on flagged items.

In healthcare, NLP extracts structured data from clinical notes, referral letters, and discharge summaries. This enables better reporting, faster audit responses, and more accurate patient pathway analysis. A private healthcare provider processing thousands of consultant letters each month can use NLP to code procedures, extract diagnoses, and populate structured records automatically.

For legal firms, NLP powers contract analysis, due diligence review, and regulatory change monitoring. Rather than junior lawyers spending days reading through data rooms, NLP can identify the key documents, extract relevant clauses, and present a structured summary for senior review.

The distinction between NLP and large language models is worth understanding. NLP is the broader field. LLMs are one approach within it. Many practical business applications use simpler NLP techniques, such as named entity recognition or text classification, which are faster, cheaper, and more predictable than full LLMs. The right approach depends on the complexity of the task and the tolerance for variability in outputs.