NLP in Insurance Trends and Current ApplicationFebruary 22, 2019
Today the insurance industry is on the disrupt cusp having embraced NLP, text analysis and AI just like the customer-service and legal industries. The large volumes of data generated by insurance companies with their various products, a large number of marketing channels, a massive customer database, and a spread of market over diverse geographies is astounding. This data has of recent been leveraged to provide meaningful trends, data and insights that are transforming, simplifying and improving business in areas like claims, customer-service, product planning and management, marketing, pricing and everything in between.
The Everest Company reports that analytics tools from third-party vendors are anticipated to grow four-fold by 2020. The value of the NLP market globally will be a whopping 16 billion$ by 2021 and tech titans like Salesforce, Google, Intel, Yahoo, and Apple already a large part of the investors.
Benefits of NLP to the Insurance industry:
Some of the accruing benefits are
- Meaningful data streamlining to the proper agent or department immaterial of geographical location is now a snap.
- Decisioning in various departments and by the agents is enabled by ensuring timely accurate and meaningful data helps them plan better while improving the C-Sat scores and user experiences.
- SLA delivery and response times are reduced improving customer services and their experiences.
- Fraudulent, multiple claims and account-activity can be effectively monitored and detected at the earliest.
The following segments of the Insurance chain benefit greatly
- Policy underwriting, maintaining and actuarial
- Relationship management of channels, clients, claims, finance, and HR.
- Security, fraud and corporate management.
Challenge areas and improvements seen:
Text analysis and NLP is the new buzzword with virtual assistants, chat-bots and such are replacing the personal touch and face-to-face interaction. This has helped the market grow as it reaches out to the masses and improves response times of queries, policy issue times, generation of reports and receipts and more that mean better customer-service and experiences.
Enterprise data access across geographies is a click away with adaption to NLP. Health data, customer profiling, cashless treatment facilities, smart recommendations of policies and such are examples of the betterments seen in the insurance sector brought about by data analytics, NLP and conversational interfaces like Google, and smart use of data to grow the business.
Channel management is another area where proper allocation and tracking of the various channels have improved by digitization, use of text analysis, and NLP across agents, digital channels, direct sales and brokers involved. Better products based on customer preferences, insights into improving marketing channels, training of agents, workforce allocation, policy servicing, and many more areas have changed and benefitted.
Customer retention was a huge challenge that has improved considerably with technological adaptations. Faster claim analysis and use of captured data for verification have been a contributive factor. Quicker underwriting, informed actuarial practices, better policy management, elimination of large workforces and insurance jargon, reduced labour costs, usage of data in daily transactions and better tracking have been some of the huge payouts the insurance industry benefits from through such embracing of technology.
Fraud detection and multiple claimants went undetected for long and are almost 10% of the European claims according to Insurance Europe. That has changed dramatically now with technology to prevent frauds and cyber security using the latest blockchain technology with AI, NLP and text analysis.
In parting data which is effectively democratized, analyzed and used can actually improve business value and customer retention through better experiences. The NLP and technology of text analysis are responsible for the disrupt that is present in the insurance sector.