The future of underwriting: How data and technology are making insurance smarter
By Arti Mulik, Chief Technical Officer, Universal Sompo General Insurance
As advanced data analytics and digital technologies simplify complex underwriting workflows, the general insurance industry is moving towards adopting machine-first underwriting models that will slash process lead times even further
From manual underwriting processes that rely on historical risk pooling, general insurers are increasingly moving towards real time & predictive risk assessment. This shift has been made possible by Big Data, telematics, IoT and advanced digital technologies; replacing legacy technologies and fragmented workflows with automated underwriting models that are governed by expert underwriters.
As insurers ramp up technology adoption and fundamentally rewire their underwriting workflows, insurance related decision making is expected to get faster, more accurate and smarter, in turn benefitting all stakeholders in the Indian insurance ecosystem.
Real-time decisions that are governed by humans
At the crux of this tectonic change lies artificial intelligence (AI) wherein routine tasks associated with policy issuance are automated, even as human underwriters are brought in to assess recommendations at a later stage. This machine-human model is not only helping insurers to seamlessly connect various workflows under one intelligence interface, but is also unlocking new capabilities that are powering real-time decision-making like never before.
As work gets channelled based on the case complexity and necessary underwriting authority level, human underwriting agents will gradually perform more of an oversight and advisory role. Only complex or nuanced cases get escalated to expert human underwriters; while the bulk of new policy issuances gets handled through agentic AI and automated workflows.
Accurate underwriting and risk assessment
In addition to this technology framework, insurers in the general insurance space are increasingly relying on personalised data to make more accurate risk assessments. By analysing data from both traditional and electronic sources such as wearable devices, insurance companies are gaining deeper insights into individual habits, behaviour and even health regime patterns to price risk on a case-to-case basis.
Combining these data insights with machine learning (ML) algorithms has meant that insurers are now able to extend tailored insurance coverage options with attractive pricing structures; leading to improved insurance penetration and enhanced customer engagement. As data analytics power more precise underwriting decisions, ultimately both policyholders and insurers alike will benefit from pricing and efficiency gains along with tailored experiences through the customer lifecycle.
Making insurance more prevention-oriented
Across different general insurance categories such as health and motor insurance, insurers are deploying IoT and connected wearables or devices to analyse individual data with a risk mitigation or prevention mindset. On detecting abnormal or higher readings, be it health vitals via a fitness tracker or vehicle speed relayed by a GPS tracker, the insurer can send the policyholder user alerts to encourage better behaviour.
Likewise, insurance companies can incentivise policyholders that exhibit favourable health habits or usage patterns with lower premiums or additional policy benefits. As a result of this integrated data approach, insurers are also launching new products such as Pay as you drive (PAYD) motor insurance and rewarding policyholders with lower renewal premiums. Thus, as insurers work towards embracing digitalisation even further, policyholders will end up being the biggest benefactors of smarter insurance decision making.