What is automated underwriting?

Automated underwriting uses advanced technology to simplify risk assessment and pricing when developing insurance policies, removing the need for manual processes and operations. Automated underwriting uses AI/ML applications, robotic process automation (RPA), application programming interfaces (APIs), optical character recognition (OCR), and natural language processing (NLP). They assist in collecting and analyzing customer data to build a comprehensive risk profile used to streamline insurance decisions. These applications are usually part of an integrated technology infrastructure that helps manage the customer lifecycle, designed to follow a company’s underwriting rules to automate data collection, organization, and analysis efficiently. Data mining technologies help reduce redundancies in the underwriting process by helping to identify risk selection better, shortening the buying cycle by pricing policies in real-time.

Companies that leverage automated underwriting can reduce the cost associated with traditional paperwork and can improve accuracy when developing premiums. Advances in cognitive computing help extract customer information that is used to assess insurance pricing, such as text reading algorithms that can gather data from unstructured data sources. Predictive analytics streamline the decisioning process by using advanced data analysis to identify patterns in data sources that can be used to predict future risk.

Insurers leverage new data sources – like digital consent forms, health apps, social media, and Electronic Health Records (EHRs) – reducing reliance on customer paperwork and making the buying process less cumbersome. Companies can price policies for the customer by using predictions based on historical data sets that provide highly defined risk categorizations. With automated underwriting, insurers can develop new products that align more closely with a customer’s lifestyle, diet, and genetic profile.

Automated underwriting maximizes the insurance value chain by allowing insurers to forecast risk in an integrated system.

How does the technology work in automated underwriting?

Artificial intelligence makes up the backbone of the automation process and is supported by various technologies that include:

Robotic process automation (RPA): RPA collects data from different sources and creates a record, maintaining regulatory standards. The collected data can be analyzed to make a historical profile of customer claims, structuring premiums based on this data.
Application programming interfaces (APIs): APIs integrate all applications into one environment, allowing the underwriting automation system to sync with its customer relationship management platforms, IoT, and internal and external clouds.
Internet of Things (IoT): IoT can be used with sensors that record data points that are measured to calculate the risk associated with environments, activities, and other physical settings.
Natural language processing (NLP): NLP can simplify data analysis of documents and sources, helping to automate the data entry process by pulling out specific information from documents and reports used to support the decisioning process. Optical character recognition (OCR) is a similar technology to NLP and is used as an application to automate data extraction.

How does automated underwriting work to simplify risk assessment?

Automated underwriting systems use AI as the foundation of the IT infrastructure, acting as the primary tool that helps calculate variables and put together formulas used to make the insurance ecosystem more fluid, accurate, and integrated. Within a system, there are automated systems that use specially designed architectures for a given process – some measure an application’s risk while others can use integrated intelligence logic. In an insurance underwriting system, the risk analysis module is separate from the underwriting terms, helping to bring more balance to the system’s applications when data is shown as inconclusive.

The most efficient automated underwriting systems use analytics and human overview to get the best use out of the technology. Using an AI/ML platform architecture, insurance companies can quickly ingest and analyze multiple data points that are used to factor risk, helping customers purchase insurance policies faster.

Automated underwriting provides the following benefits:

Faster risk assessments: The Internet of Things (IoT) helps companies quickly assess and analyze critical data used to determine the level of risk calculated for each customer, helping make underwriting faster and easier.
Better compliance: An integrated IT framework provides uniformity to business rules and regulations, reducing manual errors and providing insights on compliance maintenance.
Less manual work: Automation creates guided workflows using available data to price risk, creating less repetition and freeing workers to concentrate on high-priority tasks.
Optimize resources: Advanced analytics decreases the complexity of operations for underwriters, allowing insurers to create sustainable business models that aid in delivering individualized insurance quotes.
Greater customer satisfaction: Customers receive highly procured insurance optimized by data, driven by AI/ML automation that reduces the amount of paperwork a customer must provide to an insurance company.

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