AI And Machine Learning: Is This The Future Of InsurTech?
As we always say, the world is changing and insurance is changing with it. This change is being driven by customer expectation and technological advancement. To be competitive, insurance companies need more customer insights and the ability to turn these insights into actions, which need focused effort and expertise.
Most of the insurance companies struggle in this area which is why Insurtech start-ups play a key role. They are able to move faster and identify these gaps and provide solutions.
The bulk of these solutions are fuelled by the use of Artificial Intelligence (AI). In fact, it’s not wrong to say that AI is playing a key role in enabling Insurtech start-ups to bring “smartness” in insurance.
In order to understand the role of AI, we need to understand what AI is and what it is not. Contrary to general perception, all AI techniques don’t automatically learn from the data. AI can be divided into two high-level categories:
Machine Learning (ML): Techniques that automatically learn from the data. All predictive models fall in this category. Generally, this is what business users understand when they hear “AI”. ML-based solutions can add value to insurers — irrespective of the mode of delivery — delivered as a standalone model (standalone AI), or delivered as a part of a process/service/product (embedded AI).
Symbolic AI (SAI): Techniques that don’t automatically learn from the data. Human experts are needed to create the business rules. Underwriting or claim rules coded in IT systems are examples of this category. Insurers already have in-house capabilities of creating and implementing complex business rules. Hence, SAI packaged as ML and delivered in standalone AI mode is highly unlikely to survive through the later stages of AI hype cycle. Real value can only be added through embedded AI mode.
So let’s categorize the gaps in four high-level categories and see how AI is enabling start-ups to address these gaps:
1. Data Gaps: A data gap is created when some data fields are needed for data/analytics-based decisions but the insurer is not able to capture them.
Players are attempting to provide external data about the customers. They are leveraging ML-based deduplication and linking technologies to identify a unique customer and then provide additional data about him/her from external data sources.
Some players are helping insurers digitize their internal data by improving data capture at each stage of insurance operations. For example, Optical Character Recognition (OCR) and the Natural Language Processing (NLP) are used to capture and logically store data from physical documents.
2. Process Gaps: A process gap is created when new technologies having the potential to transform one/more steps in the insurance value chain become available, but the insurer is not able to adopt it.
Building standalone ML-based predictive models for different stages of the insurance value chain to predict propensities related to fraud, cross-sell/up-sell, retention, claims, etc. is one of the quickest ways to enter Insurtech space and hence is one of the most crowded areas.
In the last couple of years, embedding AI in processes/services/products to deliver an “intelligent” package has become an area which is attracting a lot of attention and it’s expected to continue this year as well.
Robotic Process Automation (RPA) players are using SAI to create a large set of complex rules to improve the degree of automation in insurance processes.
Blockchain players are primarily relying on a different IT technology (distributed ledger) and aspects related to smart contracts (in reality they are simplified contracts) which are handled through SAI.
There is a growing appetite among insurers to accept automated analytics on claims images/videos, customer's voice, claims summary reports, etc. Cloud-based predictive services leverage deep learning to train ML models on unstructured data sources like images, texts, videos, and voice. These pre-trained models are then offered to insurers in an off the shelf package.
3.Product Gaps: A product gap is created when new technologies, changing lifestyles and changing business models create new risks or new ways of addressing old risks.
Internet of Things (IoT) start-ups offering usage-based insurance (UBI) solutions such as telematics for motor and health insurance leverage a wide range of ML algorithms to normalize and analyze the big data which is generated every second.
Start-ups supporting agricultural insurance operations use weather and crop data collected through satellites, drones, and weather monitoring stations. ML algorithms are used to normalize and analyze this data.
4. Customer Interaction Gaps: Emerging technologies have changed customer behaviors and expectations. This creates a gap in customer-facing insurance operations such as distribution, policy servicing, and claim settlement.
NLP based ML techniques are enabling chatbots to understand customers’ queries. Then, SAI based rules are employed to find appropriate answers to their queries.
SAI is enabling online/app based distribution platforms to recommend the most suitable insurance products quickly by asking an intelligently ordered minimum set of questions. ML-based algorithms then predict the purchase preferences of the given customer and appropriately customize the insurance offering.