The A-Z of Automated Insurance Underwriting
Mar 15, 2021
Insurance | 11 min READ
Underwriting represents one of the most intricate processes in the insurance industry - from the perspective of the involved logic, the data inputs that define the process, and the underwriters that actually output the decision on whether or not a risk should be undertaken. In the last few years, manual underwriting has suffered significant setbacks. Symptomatic of high turnaround times, fragmented customer journeys, high cost of operations, and linearly correlated growth of operating costs with scale in manual underwriting processes, insurers are now looking for the competitive benefits that automated underwriting is known to bring to the industry.
Shyam Somani
Shyam Somani

Former Head

Insurance Solutions


Milind Sathe
Milind Sathe


Insurance Vertical


What is Automated Underwriting in Insurance?
Traditionally, insurance companies relied on underwriting experts and risk managers for risk assessment, premium pricing, and insurance application decisions. However, these processes were paper-based until the 2000s, after which insurers began adopting a variety of digital tools to support these processes through manual underwriting. However, manual underwriting models come to the company at major costs - the cost of underwriting can range as high as $50-130/hr, multiplying on a headcount basis in some lines of business, and the underwriting process can take anywhere from days to weeks. Moreover, scaling up with manual underwriting comes at high fixed costs in addition to increased operational overheads.
Automated insurance underwriting was designed to solve these problems. It relies on an integrated IT infrastructure, a comprehensive customer (and/or ecosystem) data footprint, and the company's underwriting rules to automate the underwriting process. In very simple terms, automated underwriting leverages artificial intelligence (AI), robotic process automation (RPA), natural language processing (NLP), optical character recognition (OCR), and accesses real-time data from 3rd parties using APIs - in order to map the risk profile associated with an asset or a scenario in the purview of your company's business rules. This results in instant decisioning on applications that earlier took days to assess - in addition, automated underwriting improves accuracy, eliminates bias, and significantly reduces costs associated with a traditionally cost-heavy process in this industry.
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How Does Automated Underwriting in Insurance Work
To understand how automated underwriting works, imagine a black box, which takes in all the data associated with an application and outputs a premium price associated with the coverage of a certain risk. So what is happening inside the black box? First, an automated insurance system must be supplied with the business rules that align with an insurer's risk management strategy and the overall business strategy. While some automated insurance systems allow insurers to implement their underwriting process by tweaking the settings in an on-prem or cloud-based application, new age systems rely on the insurer's past data and learn sophisticated rules and knowledge used by their underwriters.
Automated underwriting relies on a baseline degree of IT maturity in order to make it profitable and extract maximal value for the insurer's bottom-line. As more and more insurers are going digital and taking up an ecosystem view in their IT strategy, automated underwriting systems can be implemented faster by making use of API-based design to pipeline the application data and environment variables surrounding a risk. Underwriting rules learned from the insurer's past records are then applied to this data to output a decision on the application, the associated degree of risk, amount of coverage, and the cost of premium based on the risk profile that an application represents.
How Do Automated Underwriting Systems in Insurance Work
At the heart of automated underwriting systems is AI, which mimics the most mathematically sophisticated and experienced underwriters' capabilities in an insurance ecosystem. To make this possible, various automated systems make use of different architectures - while some rely on separate intelligent modules to measure the risk of an application and predict the output variables, others make use of integrated intelligence logic to do the same. However, risk analysis and determination of underwriting terms are usually present as separate problems inside an insurance underwriting system - specifically because of the benefits that a separate risk analysis module brings for applications on which your underwriting system is inconclusive.
The design and nature of intelligence within an automated underwriting system depends on the line of business that it aims to serve. For instance, while evolutionary algorithms and fuzzy logic rule engines demonstrate better health insurance underwriting performance, markovian models make more sense in the auto insurance line. Moreover, some product lines also rely on language inference systems to infer quantitative parameters from qualitative parameters.
How To Measure The Success Of Underwriting Automation in Insurance
According to Mckinsey, the best-automated underwriting systems combine the best of analytics and human judgment to maximize the value for an insurer. As a result, assessing a system based on the degree of intervention it requires in underwriting alone is not the right approach to assessing such a system's effectiveness. Instead, automated underwriting must be measured along with these three parameters:
  • How it augments the operating model: While some product lines in retail require instant quotes, others like P&C or commercial need more specific outputs from an automated underwriting system at various points of the sales cycle. Given the product line, how many cases are automated?
  • Maintenance resource requirements: Some implementations of automated underwriting systems can be resource-intensive to maintain - for example, expensive talent to treat the bias introduced by the training data, keeping the pipelining intact, and tweaking system variables in line with the insurer's portfolio and risk steering strategies.
  • Change in loss ratios and underwriting expenses: Ultimately, the success of automated underwriting must also be measured on the direct financial benefits it brings to the insurer. Measuring the change in loss ratios for automated decisions and the overall spend on underwriting.
How is Artificial Intelligence Transforming Insurance Underwriting
Measuring Underwriting Automation Effectiness in Insurance
Generally, automated underwriting systems bring millions of dollars in cost benefits, reduce turnaround time by over 95%, and can infer thousands of unique rules when implemented in large-scale contexts.
How Accurate Is Automated Underwriting in Insurance
When implementing automated underwriting systems, insurers usually take a staggered approach to increasing the fraction of cases that can be automated. Most implementations bring near-complete accuracy (up to 100%) to the automated applications - the cases on which the system remains indecisive are rerouted to expert underwriters. However, the best implementations aim to bring complete accuracy to the automated applications while minimizing the fraction of cases that require human intervention.
However, there is another aspect to accuracy when it comes to underwriting. Mckinsey found that in the P&C line, a small business owner could get coverages that varied by 233% - against premiums that varied by only 24% across five providers. In automated systems, optimal performance can be achieved by improving the input parameters and fine-tuning the risk analysis model to account for these parameters. For example, if a driver clocks thousands of miles on a personal vehicle without a history of accidents or claims, then such a customer could be offered reduced premiums based on this data. Therefore, optimized premium and coverage amounts are a crucial aspect of a high-accuracy underwriting process - and ultimately depends on how accurately the model assesses the risk associated with an asset/scenario in reality.
Why Use Automated Underwriting System in Insurance
Automated underwriting brings several benefits to insurers that currently rely on manual underwriting. While reduced underwriting expenses are a direct incentive for insurers to leverage automated underwriting, here are a few more benefits:
  • Utilize resources in an optimal manner: While the complexity of applications may vary over the course of your operations, your underwriters are ultimately trained to handle the most complex of them. By automating underwriting for low-complexity cases, your underwriters tend to focus on the most difficult problems while the system takes care of the business-as-usual.
  • Bring cutting-edge CX to your offerings: Speed of response is one of the first things that customers want from their businesses today. Automated underwriting helps you render decisions on applications within seconds - with the added scope of personalizing premiums and coverage by the customer's preferences while building profitability for the business in a sustainable fashion.
  • Additional benefits: Automated underwriting also brings uniformity to the business rules that drive day-to-day operations and reduces manual errors involved in the process. Moreover, it also brings real-time visibility to your risk exposure, augments top-level decisions with live KPIs and manual processes with signals that can reduce efforts associated with quantitative analysis in complex application scenarios.
Insurance Underwriting Automation Examples in Insurance
Here are some of the most innovative underwriting automation examples from the industry:
  • One of the first underwriting automation efforts were made in the 1990s -where a fuzzy logic rules engine (FLRE) was designed to infer rules from a health insurer's past underwriting records, and an evolutionary algorithm was used to optimize the model's performance. Post-implementation, 19% of applications were handled by the system with a zero-touch process.
  • In a recent underwriting automation implementation, a term insurance provider relied on complex machine learning models to automate underwriting for most applications. However, it also leveraged a risk assessment mortality model to infer whether an application is eligible for the automated processing track or not - thereby delivering on their CX promises in a more honest fashion.
  • A leading global reinsurance leader recently rolled out an insurance underwriting automation capability targeted at insurance providers operating in the African markets - where the features leveraged by underwriting automation stand largely missing/irrelevant/unrecorded. To make automated underwriting possible, the company deployed complex extrapolation models that are fine-tuned to output underwriting decisions based on limited features and as little as 15,000-20,000 data points.
Insurance Underwriting Automation Regulations in Insurance
Despite the range of benefits that insurance underwriting can bring to legacy insurers competing against insurtechs today, meeting compliance standards remains the fifth most important barrier to adopting and implementing these systems. Here are few ways in which regulations can affect underwriting automation:
Data sourcing
Automated underwriting relies on a plethora of data points that are collated from multiple sources and input to the system. However, insurers operating in a global context often find data ownership and transference rules limiting the scope of their systems. Such rules are further complicated by alternating administrations affecting a different take on existing geopolitical issues in various countries - and consequently, introducing and lifting new rules of cross-border data flows over time. Data privacy concerns and sourcing data from 3rd parties can also give rise to trust issues, and securing this data and preventing its misuse can place high data security burdens on insurers in terms of costs.
Data residency rules
Some of the most restrictive data residency rules require insurers to store the personal data of the customers natively within the country of operations. However, in other cases, these rules vary within states too - making it difficult to achieve uniformity in deployment, and often requiring workarounds that introduce inefficiencies and delays in the implementation - which is especially the case with retail lines like health and term, which utilize highly personal (and sometimes, genetic) data.
Other issues that affect automated underwriting from a compliance perspective include sanction and embargo requirements, identity validation systems, and data traceability requirements.
What Technologies Empower Automated Insurance Underwriting?
Apart from artificially intelligent systems that sit at the heart of underwriting automation, there are a number of technologies that make end-to-end automation possible in the underwriting function:
RPA in Insurance Underwriting
RPA automates the repetitive tasks that dot the underwriting process at various points - for example, gathering the right data points from multiple sources, recording the results, and maintaining logs for fulfilling compliance requirements.
IoT in Insurance Underwriting
IoT is leveraged to capture product or service-specific data points which are then used to augment the risk analysis and decisioning process. For example, by placing sensors on a vehicle to improve auto insurance decision boundaries or a network of sensors that capture environment variables in commercial lines.
Application Programming Interfaces in Insurance Underwriting
APIs make it possible to create an integrated application environment. Using APIs, insurers can extend an underwriting automation system's worldview to its CRM platforms, private and public clouds, IoT networks, and other third-party data sources.
Automated Insurance Underwriting – Technology Enablers
Automated Insurance Underwriting – Technology Enablers
How To Build Your Automated Insurance Underwriting Roadmap?
In order to capture maximum value from an automated underwriting implementation, insurers must align the implementation priorities along with the most efficient adoption and improvement roadmaps. Most insurers take a staggered approach to automating their product lines based on the complexity spread of insurance applications. It has been shown that automating underwriting for the least complex applications can help insurers realize the returns on their investments soon after the implementation - a noticeable effect being a reduction in requirements of expensive underwriting experts for handling the same volume of applications.
McKinsey reinstates the importance of implementing end-to-end automation from the get-go - following which, the implementation can be further improved by incorporating new data sources and building more sophisticated intelligence systems that either augment cases requiring manual intervention or handling high-complexity applications. Underwriting innovations can be followed up with other approaches that reduce the cost of data acquisition and/or improve risk classification. Underwriting automation increases onboarding metrics significantly - which builds further incentive to innovate the post-binding experience and improve retention. Automated insurance can set insurers on a new trajectory of growth beyond cost savings, efficiency, and revenue growth.
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