Data excellence in insurance using AI
“The data-driven world will be always on, always tracking, always monitoring, always listening and always watching – because it will be always learning” (The Digitization of the World, IDC report).
The world of insurance has discovered its key to success – Data. On one hand, it is ubiquitous. Whether it is KYC process, claim form fillup, policy issuance, risk assessment or claim settlement in P&C, life, auto or health insurance, there is a continuous inflow of data across all functions and lines of insurance. As insurers increase their customer base at a rapid pace, the volume of data is also increasing at an exponential rate.
On the other hand, often insurers are not sure how to put data into best use. At best, they store data for compliance and regulatory requirements without realizing their hidden value. This inability to efficiently deal with a surfeit of data leads to a ripple effect in insurance:
- Data extraction from various unstructured and structured sources is manual and thus, highly inefficient.
- Lack of granular analysis of available data leads to high error rates, duplication, omissions, delays and inaccuracies in insurance functions.
- As a result, customer satisfaction takes a big setback.
- Clients turn to market alternatives, insurers lose business and relevance.
Due to the lack of proper tech knowhow and investment in relevant technology, data management is becoming a major bottleneck for insurers all over the world. They are slow to monetize the asset of unused data, brimming with potential. The trick is to achieve something the industry desires (a fresh insurance experience) by making sense of something it already has (data) with the help of something that they need to make the best use of (technology).
With proper use of digital disruptive technologies, data can be the new differentiator in insurance. The industry is waking up to this reality.
AI to the rescue
Sifting through a galaxy of data and deriving meaning out of it to fuel future decisions is a job for cognitive technologies that change the core of the industry. Artificial Intelligence (AI) and related technologies like machine learning (ML), natural language processing (NLP), robotic process automation (RPA), internet of things (IoT), etc. — holds the key to unlocking data excellence for insurers.
AI enables insurers to extract, connect and analyze data across various insurance processes to uncover, streamline and democratize insights across the insurance value chain with focus on quality and accuracy. This extraction-analysis-insights process enables insurers to:
- Bridge service gaps by having more meaningful engagements with clients.
- Identify user needs and behavior to make personalized promotions and enable consistent user experience.
- Facilitate real-time decision making by offering data-backed recommendations.
Tackling insurance dark data with intelligent automation
Structured data is something that has a predefined format, whereas unstructured data is information that is stored without any specific format. Structured insurance data includes names, addresses, credit card information, geolocation, etc., whereas information stored in the form of audio, video, image, files, emails, etc. is unstructured or semi-structured. Structured data can easily be extracted using RPA, which includes UI level screen-scraping and API interactions, usually resulting in just 30%-40% Straight Through Processing (STP) .
However, when it comes to semi-structured or unstructured data, which is also called dark data, insurers are turning to intelligent automation, which is a coming together of RPA and AI. For this, OCR, document extraction tools, ML or a combination of these capabilities are leveraged to bring structure to the unstructured and semi-structured data. This results in a much higher STP of 60%-80%. Thus, intelligent automation captures and unlocks the broad and ambiguous dark data hidden in insurance system silos to provide qualitative actionable insights into business operations, processes, customer expectations, insurance functions, and more.
Data excellence impact in insurance
With data excellence, insurers can have a deep impact across various functions, lines of business and strategies.
Insurers can experience major benefits from data excellence across the insurance value chain. In policy issuance, data extraction plays a major part, especially in billing. A carefully managed data pipeline can extract data from various sources to maintain state-wise taxes and fees to update billing information. Similarly, access to user data (like KYC) is crucial to endorse or cancel a policy. In the claims life cycle, accessing and analysing user and usage data can result in quick and effective claim resolution.
Data excellence also proves handy in underwriting as insurers can gather information from various systems and generate renewal premiums. ML can help underwriters leverage large volumes of data, resulting in in-depth review and efficient identification of fraud. AI-driven predictive analytics can use datasets from internal and external data for better risk analysis.
Imaginea helped a UK speciality insurance broker by deriving insights from unstructured data to help underwriters make informed decisions. While ML algorithms assisted in categorizing documents, OCR tools and NLP detected patterns and extracted data with 85% accuracy from those documents. The result was a self-serve underwriting dashboard that led to a 40% improvement in underwriting productivity.
In customer service, chatbots are revolutionizing interaction with customers and resolution of user issues. Even in this domain, chatbots gain their unique ability by collecting, analyzing, disseminating and utilizing the vast amount of data produced by its interaction with users.
For example, US insurer Anthem’s chatbot interviews patients about their medical history, symptoms, etc. and matches them with similar patient cases and licensed data resources to determine likely conditions. Lemonade’s AI-powered chatbot Jim reviews claims by cross-referencing them with policies in its database and running anti-fraud algorithms on them. By the end of 2022, data-powered chatbots are estimated to generate a savings of over $8 billion globally.
Lines of business
With the help of AI, data excellence in various lines of insurance can help insurers do the same things better and also do something radically different to sustain and grow in a highly competitive business environment.
Data mining is one of the prime components that helps in opening up opportunities for P&C insurers to come up with new offerings that create unprecedented value for customers.
Data excellence provides a fillip to the life insurance industry too as the latter has started to integrate health, wealth and lifestyle products in its portfolio. Leveraging of good quality user data accurately helps in personalizing offerings, financial data in underwriting/ pricing, and so on.
In health insurance, access to interoperable patient data can bring more efficiency to operations and make for a smoother user experience. Additionally, this can prevent delays in treatment and deaths. Data can also work wonders in the health insurance sector by detecting fraud claims, false medical records, improper billing, non-maintenance of medical and financial records, etc.
Similarly, health data through IoT wearables and monitoring devices can help assess risk, decide premiums and types of coverage. For instance, Long-term care insurer CNA and HealthTech firm GreatCall’s in-home remote monitoring solutions track daily activities like eating, sleeping, and movement to monitor customers’ health and provide timely interventions through predictive analytics.
Having control over unprecedented amounts of data also helps insurers come up with new-age and customer-centric policy offerings and pricing strategies. Telematics and IoT are already changing the insurance landscape by offering usage-based insurance. The ecosystem of wearables, monitoring systems and connected devices has made it possible to gather real-time data to profile a customer.
With AI, insurers are able to cut through the data density and review risk factors like driving behavior and heart rate to decide policy premiums. For example, MetroMile offers pay-per-mile car insurance by measuring the mileage using Metromile Plus, a device connected with the insured vehicle. Similarly, Esurance uses telematics technology to detect driver behavioral data like braking patterns, driving speed, etc. and offer usage-based insurance based on the data collected.
According to a report, in the next five years, data-driven telematics will have a 90% impact on insurance pricing, 80% on underwriting and 51% on claims management.
Data – the new differentiator in insurance
A handful of insurtechs and startups have understood the wide possibilities of effective data management, and are investing in disruptive technologies like AI to disrupt the market and innovate the industry. According to an IDC report, 45% of US insurers will be offering new data-driven customer-centric experiences in the near future.
For traditional insurers, this reality is a wake-up call. However, data excellence doesn’t only depend on having the right tech stack and a team of AI specialists. Incumbents must work together with startups, insurtechs and capable service providers to manage the enormous data that is piling up in the already data-driven industry to improve business processes, understand customer needs better, fix service gaps, manage customer relationships and improve decision making.
To know more about how data excellence can innovate the insurance industry, download our report here.