How AI is Transforming Commercial Insurance Underwriting

Mar 02, 2021
Insurance | 7 min READ
The State of Underwriting in the Commercial Insurance Industry
Artificial intelligence (AI) has significantly disrupted the insurance industry in the last few years - especially in the retail and specialty lines. AI-driven underwriting has helped companies escape the operational and financial bottlenecks associated with manual underwriting. However, commercial insurance has seen limited AI adoption in the underwriting function - most commercial insurance providers deploy underwriting experts that deploy unique and non-standard processes to forecast the potential profitability against risk and the right parameters for making a quote.
Shyam Somani
Shyam Somani

Former Head

Insurance Solutions


Tej Sarup
Tej Sarup

Former Practice Head

AI & Big Data


In the commercial line, manual underwriting is dotted with piles of physical and digital paperwork, countless instances of rekeying data to check for application completeness, multiple back-and-forths between client representatives, decision-makers, and 3rd parties - not to mention a lengthy and cumbersome risk analysis process which relies on non-specialized software and browser searches to assess the risk profile of an application. However, such efforts only make room for procedural errors, quality issues, inefficient pricing, suboptimal loss ratios - at the same time, they also incur high costs against expensive person-hours while making inefficient use of expert talent. The result? Commercial insurance quotes take 2-3 days to reach the client in some of the most efficient manual underwriting-based operations.
Six Ways Artificial Intelligence is Transforming Commercial Insurance Underwriting
Insurance underwriting automation has already transformed the retail lines in this industry, and leaders now promise instant quote and issuance to their customers with attractive prices geared to retain customers. However, in the commercial lines, AI-based insurance underwriting is defining new pillars of operational and strategic excellence. Here are six ways AI can help commercial insurers achieve new heights of operational and strategic excellence.
1. Improved Underwriting Risk Management
In the commercial lines, every application posits a new set of risk variables that are becoming increasingly complicated to assess and account for in the risk analysis strategy with precision. Using artificially intelligent systems that assess an application profile against billions of data points accrued from 3rd party sources, underwriters can now gain visibility into the most relevant risk factors associated with a client profile. In specialty lines like cyber insurance, gaining complete visibility into the risk exposure of an enterprise's IT systems and appropriately translating these risks into profitable numbers for the business can be an impossible task for humans.
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The current commercial insurance sector is symptomatic of inefficient risk management. According to a McKinsey study, this is evident from a 23-28% difference in the loss ratios logged by the top and the bottom quintile of performers. AI-driven underwriting systems assist the underwriters by accurately quantifying unstructured and qualitative data points - like social media and news feeds, reliable statistics from public sources, and 3rd parties - and convey a comprehensive risk profile to the underwriters in a highly interpretable manner. This can drastically improve loss-ratios and standardize a 360-degree approach to risk analysis across the insurer's underwriting function.
2. Profitable and Fair Pricing
According to a McKinsey study, a small business owner looking for commercial P&C insurance received coverage amounts differing by a staggering 233% from five providers - for almost the same risk. At the same time, companies like AIG pay $75m every day due to losses arising from the commercial line alone. This demonstrates the pricing inefficiencies that benchmark the current landscape in the commercial lines, and more importantly, fail to capture the risk profile appropriately through a profitable pricing strategy. AI-driven underwriting brings complete risk visibility into a case. These factors are then used to suggest the best possible pricing options and coverage terms to the underwriters - that act as informed gatekeepers responsible for course corrections.
AI-driven underwriting analyses a risk profile in the purview of evolving geopolitical risks, more extensive ecosystem variables, social media sentiments, Geospatial Information Systems (GIS), and real-time data from IoT networks/3rd parties to appropriately appraise premiums while also accounting for retention conditions and customer delight within the pricing strategy. Most commercial lines have seen success with predictive ML solutions in rolling out fairly-priced quotes fast and achieving higher profitability through their pricing strategy.
3. A surge in Underwriting Efficiency
In the manual underwriting process, most high-value accounts take over weeks to receive a quote post-application. Underwriting is one of the most time and resource-intensive processes in the commercial insurance customer life-cycle. AI-assisted underwriting changes the equation through RPA and building intelligence into the system for light tasks while keeping human attention dedicated to the most complex tasks and for the final decisioning. For example, in conjunction with a minimally inquisitive digital application process and integrated application infrastructure, AI-driven underwriting systems can help underwriters issue a quote for high-value accounts on the date of application.
Also, AI-driven underwriting calls for a process redesign in the pre-implementation stage, which introduces standard operating procedures, coherence, and builds best practices into the underwriting process - for example, automatically assessing application completeness or pricing coverages such that the customer keeps coming back. Standardization builds predictability into day-to-day operations, while RPA technologies like intake automation enable the completion of tedious and repetitive tasks like data collation, find-and-rekey, checklist-based assessments, and other quality assurance measures at the touch of a button. Lastly, AI-driven underwriting systems are not bottlenecked by capacity and bring inherently low bias into underwriting decisioning.
4. High Underwriting Profitability
According to Mckinsey, some of the best underwriters learn to strike the right balance between profitability and risk exposure while readjusting their approach based on other factors like supply and demand, evolving economic and geopolitical dynamics the overall risk appetite. As a result, profitability becomes a question of steering the overall portfolio along with the right metrics. Therefore, AI-driven underwriting systems should be augmented with insights that empower the underwriters rather than replicate their judgment and speed up the core workflow that underpins the underwriting process.
AI-based underwriting
However, AI-based underwriting also brings profitability by helping underwriters deliver improved loss ratios, a higher churn rate, quotes that convert better and ultimately optimize the overall resource utilization in the underwriting function. As a result, achieving profitability from implementing such systems also requires insurers to leverage lasting partnerships with tech leaders that specialize in high-impact transformations. Moreover, with the underwriter's evolving function as a business builder and a digital manager in an AI-first insurance enterprise, companies can now achieve improved expense ratios and better employee experience along with an AI-assisted underwriting transformation roadmap.
5. Better Levels of Customer Loyalty
AI-based underwriting systems enable insurers to deliver a better customer experience at the sales stage and build loyalty from the first contact with the customers. By automating low-complexity tasks and freeing up underwriters for more complex interactions with the customer, insurers can strategize a long-term retention roadmap based on personalized account servicing, lucrative pricing models premised on risk-sharing, and actionable loss control strategies. Leading commercial insurers are already upskilling their underwriters to take on more complex roles and to adapt to highly satisfying and work enabling digital platforms that leverage AI to streamline the underwriting process and post-sales services.
However, the most crucial benefit that AI-driven underwriting brings towards customer retention is to recommend coverage specifics and premiums based on thousands of data points that account for the customer's preferences and ultimately enable underwriters to deliver quotes that convert. Lastly, underwriting automation enables underwriters to speed through the risk analysis process and seamlessly a highly personalized experience that accounts for the customer's coverage preferences, rewards them through risk-sharing models, and aligns their premiums with the actuals of the risk profile.
6. Rise in New Business Acquisition and Cross-sell Opportunities
As AI-driven underwriting systems integrate into the larger insurance value chain, insights extracted from cross-platform visibility and centralized data lakes can not only create new cross-sell opportunities but also empower underwriters with unique insights that help deliver experiences that delight - for example, by mapping a customer's journey from the questions they typed into an NLP and AI-powered chatbot to their online application, the underwriters can be informed of the customer's intentions before they get on a call with them to customize their coverage. Moreover, integrated AI-driven systems can also empower the underwriters to approach the customers with a plan tailored to their needs even before they have filled out an application.
Lastly, given the disruptive nature of the use cases of AI in the commercial lines, insurers must also have their C-level executives on the lookout for attractive acquisition opportunities. Insurtechs have been delivering increasingly innovative value propositions in an industry where the adoption of disruptive technologies has been relatively slow. Therefore, legacy insurers have seen acquisition as a ripe alternative in periods where business-as-usual has failed to deliver, primarily because of new value creation opportunities that entail the acquisition strategy - like creating new workforce capabilities through cross-skilled collaboration. Such models have enabled leading insurers to bring new data capabilities into their underwriting process and chart new roadmaps of profitability and competitive advantage.
How is Artificial Intelligence Transforming Insurance Underwriting
How is Artificial Intelligence Transforming Insurance Underwriting
To recap, AI can help insurers improve underwriting capacity by over 50-60%, reduce risk exposure, achieve better loss and expense ratios, boost customer response, and deliver quotes that sell. Beyond underwriting, AI and machine learning have shown promise in several other areas, like intelligent automation of claims settlements, innovative loss control mechanisms, and improving the post-sales engagement strategy. However, underwriting innovation remains one of the top priorities for insurers looking to boost their revenues and bring a tectonic improvement to their operations.
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