Machines have learned and are still learning to speak our language. Not just in sci-fi movies, but in banks, hospitals, and call centers around the world. This trend isn’t about robots chatting at the vending machine — it’s a revolution in how computers understand and use human communication.
That’s the domain of natural language processing. It’s a vital area of the artificial intelligence takeover, which handles the process by which AI understands and utilizes unstructured language data between humans and technology.
To help you understand the technology better, this TechRepublic Premium article by Franklin Okeke explores the practical use cases of NLP and its growing importance in today’s digital environment.
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FINANCE AND BANKING
About 30% of NLP applications will be implemented within the banking, financial services, and insurance sectors by 2025, according to John Snow Labs. Historically, the banking industry has been a key driver in adopting AI technologies, particularly NLP, to enhance data automation processes. Some of the notable use cases include:
Fraud detection and risk assessment
NLP solutions can empower financial institutions to bolster their fraud detection capabilities and safeguard against financial crimes. This can be achieved by enabling automated analysis of vast amounts of unstructured data such as emails, transaction descriptions, social media, and customer communications.
The insurance sector, for instance, is among the most targeted for financial fraud in the U.S., with fraud figures reaching $309 billion per annum, which translates to nearly $1,000 per person.
NLP algorithms are being used by many insurance companies to help them identify suspicious patterns, anomalies, and fraudulent behaviors by examining the language used in these data sources for signs of deceit, phishing attempts, or identity theft.
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TIME SAVED: Crafting this content required 22 hours of dedicated writing, editing, research, and design.
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