Simplify manual insurance document processes with Smart AI-enhanced Nanonets OCR API. Scan, extract, analyze and integrate your documents in real-time.
Here is a snapshot of the performance gain of our customers after using Nanonets for less than 3 months.
I would recommend Nanonets to companies who need an accurate and well functioning Passport OCR solution with great support, reasonable pricing, and flexible plans.
“Working with Nanonets has been super fluid. NanoNets helped us deploy the data capture workflow with Quickbooks in < 7 days from the contract signing date. This has directly impacted the finance team, who can now process 50% more invoices at the same time without hiring more people.”
Capture, Extract and Analyze data from important insurance documents while verifying all customer credentials against your database. Introduce speed, efficiency and accuracy to your insurance document processes.
Create custom rules for your processes and change rules to improve accuracy on the go. Nanonets maintains a complete log of all activities for audit requirements.
Streamline document processing of all your banking documents with ease
Get in touch with our experts and we’ll customise a solution for your business.
Insurance automation is the use of technology like Artificial Intelligence, Machine learning, Robotic process automation, intelligent automation, and more, to drive high profits, transform insurance metrics, improve the performance of the insurance processes, reduce the customer turnaround time, enhance security and eliminate manual data entry.
Using insurance automation software, insurance companies can move from being proactive to a customer-obsessed model.
RPA in insurance is using automated rule-based workflows and bots to eliminate manual data entry, verification, validation, approval, and data storage tasks. Robotic process automation can be used to process customer information, extract data from scanned documents,
Robotic process automation can be used to automate various aspects of insurance. Some of these insurance automation use cases are mentioned below:
Insurance automation software can collect complex data from various sources, identify fraud, estimate the loss, update data fields on customer profiles and provide a correct estimate of underwriting.
An insurance automation platform can ensure high accuracy of data, maintenance of changelogs, a complete library of data entries, and generate automated regulatory reports for stakeholders.
RPA insurance automation bots can be trained to interact with customers 24x7 and guide a customer to self-serve resolution articles and collect customer information for agents to review afterward.
When optical character recognition (OCR) and RPA are integrated, insurers may automatically understand the text from registration forms and direct the information to the necessary workstreams. As a result, insurance claims are processed 40% faster and with 0 error.
RPA systems can analyze customer emails and classify the content using various RPA systems can classify customer emails using deep learning, computational linguistics, sophisticated OCR, and analytics.
Policy cancellation involves performing multiple checks like tallying inception date, and cancellation dates, checking all policy terms, and providing cancellation confirmation to customers. RPA bots can be trained on custom rules to carry out the policy cancellation process smoothly.
Insurance companies can use Artificial intelligence for many use cases like improving security and compliance, optimizing customer onboarding, improving performance metrics, automating manual tasks like form registration, identity document verification, KYC verification, and more.
Check out our article on Insurance Automation.
The rising costs to serve, the growing regulations, and the increasing competition put a lot of pressure on the industry and will increase the adoption rate of technology in insurance. The future of insurance industry is moving towards intelligent insurance automation. Read more.
Insurance automation can be beneficial for insurers in the long run as it reduces costs to serve, improves margins, increases data protection, enhances security, reduces turn around time, optimizes resource utilization and improves the visibility of financial data across the organization.