• Home |
  • Use case – Finance Industry | (NER-LLM) | Ruby

Use case – Finance Industry | (NER-LLM) | Ruby

Insurance Industry

Ruby (NER+LLM)

Insurance companies can leverage NLP tools to automate many claim processes, speeds up decision-making, reduce costs and errors, and improve customer satisfaction

Quick Email
info@virtualdev.net
Tell With US
+33 745 06 4951

Use Case

We explain some examples of how LLM models can be used to enhance processes for customers.

  • Customer service
  • Claim processing
  • Fraud detection
  • Virtual assistant
  • Employees

RUBY can automatically do this for you.

Even more interestingly, it provides support for multiple languages.

With RUBY you can process your physical documents such as claims, invoices, contracts, medical reports, using AI and extract the relevant information intelligently and quickly.

Customer Service

Customers will have questions about your company’s products and services and will expect a quick response. But due to the high volume of these types of inquiries, their inquiry may not be resolved in a timely manner.

Typically between 40% and 80% of typical customer service queries can be handled by a Chatbot.

An intelligent chatbot is able to understand the customer’s query can handle much of that load in a timely manner, increasing customer experience and satisfaction.

These Generative Chatbots can immediately answer questions about the company, its policies, billing, products, services and all kinds of information, redirecting customers to the right answers they need. This allows customer service staff to focus on more complex issues.

Claim processing

NLP-enabled chatbots can be used to help customers automatically fill out claims reports and collect the necessary documents.

 

They can also be trained to extract information such as details of damage or injuries sustained directly from the client’s report. Case personnel can use this information to verify the claim more quickly.

 

In this way, the submission and settlement of claims can be automated and made cheaper, reducing the time it takes to respond to the client and the work of the personnel assigned to the case.

 

1

Fast

2

Secure

3

Automatized

4

Simplified

Fraud detection

Unfortunately, some claims may be fraudulent and should be detected early

Manually reviewing emails, claims, documents and related forms for fraud is a complex task and can generate a high margin of error. Fortunately, this process can also be automated with NLP models.

These models are trained on applications that have been labeled as fraudulent or not. They then learn to identify applications with similar characteristics and assign scores to them based on the likelihood that they are fraud.
Applications deemed suspicious are reviewed by a final decision maker.

Virtual assistant

The number of procedures involved in claims processing can cause information to be scattered across different processes or platforms, as tools may be needed that cannot be obtained in one place.
For a client, this means a lot of inconvenience, as they have to fill out forms or send the same documents multiple times.

The number of procedures involved in claims processing can cause information to be scattered across different processes or platforms, as tools may be needed that cannot be obtained in one place.
For a client, this means a lot of inconvenience, as they have to fill out forms or send the same documents multiple times.

An AI-powered virtual assistant can help collect, process and review claims, verify policies, run claims through a fraud detection model, schedule payments and make the claim settlement payment, all in a matter of minutes.

A virtual assistant is an excellent way to integrate different areas to improve the claims process for customers.

Employees

Employees often have to analyze a large amount of data such as policies and other documents to assess the risks of insuring a client. In this case, speed and accuracy are highly important.

 

This process is often manual, slow and error-prone because of the large amount of data involved.

NLP models can extract key information such as names, dates, quantities, values, diagnoses and treatments from a claim management record or any required document.
Information that would otherwise have taken hours to find is now easily extracted.