The challenge

A leading insurtech passionate about the customer experience -- rather than the "them versus us" traditional mindset of the industry it disrupted -- hit a speedbump. A backlog of 30,000 unprocessed insurance records and invoices delayed payments and left many customers in the dark, wondering about the status of their claims.

That was a nonstarter for the Wall Street darling, whose younger, digitally savvy customers expected speedy and frictionless transactions. In this fast-moving sector, unhappy consumers can quickly defect to any other of the 1,500 insurtech companies worldwide. Artificial intelligence to power operational efficiency was a strategic imperative for this company serving customers in France, Netherlands, Germany, United Kingdom, and the United States.

Our solution

With artificial intelligence regarded as insurtech's secret weapon, the client engaged TTEC to deploy a human-driven, data annotation strategy to label medical records and invoices to improve the effectiveness and scalability of its AI. Data was analysed, classified, and tagged with high accuracy before it was imported to the client's optical character recognition (OCR) engine. The resulting improved algorithms enabled the system to issue claims payments automatically based on approved coverages.

Human intervention was essential to bolster AI. Without data correctly identified and annotated by knowledgeable people, a machine learning algorithm cannot compute the necessary attributes. The data review process required workers who could decipher medical terminology and diagnostic acronyms unique to this line of business, pet insurance. TTEC sourced, trained, and hired a team of 25 back-office associates in the Philippines. Many workers already had veterinary education or experience as vet technicians and all received training from the Pet Insurance 101 curriculum TTEC developed.

The results

The client's backlog of 30,000 medical records and invoices was reduced to zero, thanks to TTEC's off-shore team who reviewed, analysed, and annotated
data that enabled the client's OCR to process payments of pet insurance claims faster. Customer satisfaction rose as a result, particularly
among Millennials known for their willingness to spend on their pets and who expect their claims processed quickly.

The client saved EU900,000 in staffing costs because the data-annotation task is not customer-facing and did not require the skills and resources the client had allocated
to the job prior to engaging TTEC. By unburdening its U.S.-based, salaried workers (EU64,000 a year) of data annotation and transferring that task to well-qualified,
off-shore workers paid EU13 per hour, the client achieved labour savings quickly.

With high integrity data and smarter algorithms, the client's OCR and machine learning capabilities will continue to improve, enabling
faster processing and the excellent customer experience insurtech companies are expected to deliver.