According to a recent McKinsey report, underwriters spend 30 to 40% of their time on non-core activities such as data entry and administrative tasks. This reliance on manual processes not only slows down decision-making but also increases the risk of costly errors and missing critical information due to data inconsistencies.
Advanced tools like Feathery enable seamless data integration into existing systems, significantly reducing manual input and errors. By automating the extraction and analysis of data from various unstructured sources, underwriters can streamline their workflows and improve accuracy.
The Unstructured Data Challenges of Underwriting
1. Unstructured Documents
Handling unstructured documents is a major challenge in underwriting. These documents, often in varied formats, require extensive manual processing, which is time-consuming and prone to errors.
According to Datamation, 80 to 90% of all data generated worldwide is unstructured e.g. images, emails, HTML content, policy documents, phone calls etc.
2. Collecting data from multiple sources
Underwriters need to gather data from brokers, insured parties, and other sources, often leading to fragmented and inconsistent information.
3. Loss run analysis
A critical part of underwriting, loss run analysis involves reviewing all past claims made by the insured. This data is typically unstructured and requires significant effort to process.
4. Schedule of covered items
Underwriters must review and manage lists of items covered by the insurance policy, such as multiple vehicles or properties, which adds to the complexity.
5. Requested Coverage Types
Different coverage types for various accidents or natural disasters need to be meticulously documented and analyzed.
6. Carrier submission/application PDFs
Brokers often submit client information using different carriers' formats, necessitating manual extraction and reformatting by carrier staff, leading to inefficiencies and errors.
7. The back-and-forth between carriers and brokers
Correcting missing or inaccurate data is a significant pain point. Incomplete data hampers the underwriting process, leading to suboptimal decision-making. For instance, without a comprehensive driving history, an insurer cannot accurately assess risk, potentially leading to higher premiums or denied coverage.
8. The Underwriter Retention Challenge
Manual processes and repetitive tasks can lead to underwriter dissatisfaction and higher turnover rates. Automation can alleviate this by streamlining workflows and allowing underwriters to focus on more strategic tasks.
A PwC study found that 55% of financial services executives view talent acquisition and retention challenges as the primary obstacles to their companies' growth.
What Is The Solution?
Let’s take a look at the most common solutions out there such as RPA (Robotic Process Automation) and BPO (Business Process Outsourcing) and explore their pros and cons.
RPA Solutions
RPA solutions are designed to automate repetitive tasks by mimicking human interactions with digital systems. However, they are often brittle because each rule and workflow needs to be meticulously hard-coded.
This rigidity makes them less adaptable to changes in data formats or processes, requiring constant updates and maintenance. Additionally, RPAs are limited in handling unstructured data, which is common in underwriting, making them less effective for comprehensive automation needs.
BPO
BPO involves delegating manual tasks, such as data entry and document processing, to external service providers. While this can reduce the immediate workload on in-house staff, it introduces several challenges. Security risks are a primary concern, as sensitive data is handled outside the organization.
Relying on human labor can also lead to inconsistencies and inaccuracies, especially with unstructured data. Quality control becomes more challenging, and the turnaround time can be longer compared to automated solutions.
Underwriting Automation with Feathery
- Handling Complex and Unstructured Document
Feathery can read and extract data from any type of PDF, including submissions from different carriers and formats.
For example, if you need to process a car insurance claim, Feathery can extract essential details like vehicle identification numbers, accident reports, and repair estimates, pinpointing exactly where each piece of information is located within the document.
Feathery extracts fields like revenue and contract details, even from complex tabular data or handwritten entries. This capability ensures underwriters have all the necessary information for their quoting process.
- Managing Missing Information
Feathery not only extracts data but also identifies when critical information is missing. It can automatically notify brokers or agents about the missing fields, ensuring that all required data is collected promptly. This feature minimizes the back-and-forth communication typically needed to gather complete information, streamlining the underwriting process.
- Data Formatting Flexibility
Feathery allows users to export extracted data into various formats, such as Excel, CSV, or directly into databases. This flexibility means that the data can be easily manipulated, analyzed, and integrated into existing workflows.
For example, underwriters can generate detailed reports or pivot tables in Excel to analyze risk factors quickly.
- Deep System Integrations
Feathery integrates seamlessly with numerous systems used within the insurance industry. Whether it’s Policy Management Systems like Guidewire, Agency Management Systems such as Epic and Vertafore, or CRMs like Salesforce, Feathery ensures that the extracted data flows smoothly into these platforms.
This integration eliminates the need for manual data transfer, reduces the risk of errors, and enhances overall efficiency by providing underwriters with immediate access to the necessary information within their familiar systems.
- Automating Data Intake for Quoting Processes
Feathery assists leading insurance carriers and MGAs like MassMutual, Secura, and Hiscox by automating the data intake process from brokers and agents, regardless of the format. Feathery extracts the necessary data for quoting from various documents and integrates it into systems such as policy management systems, rating engines, spreadsheets, internal portals, and CRMs.
This automation speeds up the quoting process and reduces the time underwriters spend on manual data entry.
- Processing Scanned and Low-Quality Documents
Feathery can read data from grainy scanned documents, such as supplemental applications. It accurately extracts fields like the number of employees and annual revenues, which are essential for quoting. This capability reduces the time and effort required to process poor-quality documents manually.
- Integrating Data from Accord Forms
Feathery can extract necessary information from standardized insurance forms like Accord 125. It captures data for various lines of business, including commercial general liability, ensuring that all required fields are filled accurately for the quoting process.
- Automating Decision-Making with No-Code Rules
Feathery allows underwriters to define auto-decisioning logic through a no-code editor. This feature enables automated acceptance or rejection of applications based on predefined business rules, such as industry exclusions. Feathery also supports API integrations to enrich applicant data with external sources like Salesforce or geolocation services.
- Writing Data to External Systems
Feathery can write extracted data to various systems, including CRMs like Salesforce and AMS systems like Applied Epic. It supports mapping submission data to system fields and can generate PDFs, Word documents, or Excel sheets based on the collected data. Feathery's browser extension also enables autofill of internal quoting portals, further enhancing data integration and workflow efficiency.
Case Studies
MassMutual
Feathery played a crucial role in transforming MassMutual’s data intake process for their commercial benefits line of business. Previously, the intake of producer licenses and various documents such as PDFs and CSVs was a labor-intensive, manual task that took hours per quote.
By automating this process, Feathery enabled MassMutual to significantly reduce the time spent on data entry and processing. The system seamlessly extracted necessary information from diverse document formats, ensuring that all relevant data was accurately captured and integrated into their internal systems.
"Our goal was to eliminate the tedious manual processes that bog down underwriters. By automating the intake and processing of documents, we're helping companies like MassMutual save valuable time and focus on what really matters—assessing risk and making informed decisions," — Zack Khan, Co-Founder, Feathery.
Secura
Feathery helped the digital team at Secura to automate the intake and quoting process for Commercial Property and Auto Insurance.
“Our automation tools have empowered Secura’s team to focus on strategic tasks by streamlining their data intake and quoting processes.” — Peter Dun, Founder & CEO, Feathery.
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