5 SOV Insurance Broker Tools to Evaluate in 2024 and Beyond

Statement of value (SOV) ingestion tools provide businesses with the ability to quickly process risk exposure data. There are multiple tools available on the market that cater specifically to loading and standardizing information sent in spreadsheets. These tools provide a way to quickly import, standardize, enhance, and process data and offer features such as analytics, integration with downstream systems, and workflow.

In this comprehensive guide, we will delve into some of the top players specifically focused on processing insurance SOVs, beginning with Archipelago and then covering Ping Data Intelligence, Convr, Scrub AI, and UI Path. 

What Is SOV Ingestion?

SOV data ingestion is the process of updating property values and is the first step in insurance submission. It entails preparing a set of critical documents that outlines detailed information about the insured properties, including their locations, values, and associated risks.

SOV data is often shared and stored in spreadsheets or other data formats, such as PDFs, and is not standardized across end-insureds, brokers, and underwriters. When brokers leverage software to process submissions, SOV ingestion refers to the automated extraction, validation, and integration of this data into the final submission document that is then shared with the market.

Why Is SOV Ingestion Important?

SOV ingestion is vital for both underwriters and insurance brokers because it directly impacts the accuracy and efficiency of the insurance submission process.

For underwriters, having reliable and quickly accessible data from the SOV enables accurate risk assessment, informed decision-making, and appropriate premium setting. This reduces the time spent on manual data entry and minimizes errors, leading to faster turnaround times for policy issuance.

For insurance brokers, efficient SOV ingestion ensures that the data they submit is processed swiftly and correctly, allowing them to provide their clients with timely and competitive quotes. This enhances their ability to meet client needs, improve service quality, and maintain strong business relationships.

Breaking down the SOV process

When it comes to SOV ingestion, there are many steps that constitute an end-to-end process:

1. Reading various formats, including free-form text in spreadsheets: This capability is needed because insurance submissions often arrive in inconsistent formats, making it essential to accurately capture critical data regardless of how it is presented. The challenge lies in the diversity of these formats. Tables in spreadsheets and PDFs require either a human eye or sophisticated AI technologies to interpret and extract relevant information accurately. Handling this variety without losing data integrity is a complex and crucial task.
2. Standardizing data by matching it to the formats used by underwriters: This step is essential to ensure that the information fits the specific needs of underwriting systems. The challenge is the large volume of data and the need for precise mapping of the data to the corresponding cells. What makes this complex is the need to handle different data formats and content while staying accurate and complete. Mistakes in this step can lead to valuable data being lost, which can result in higher predicted losses.
3. Enhancing the data with additional relevant information: This enhancement is essential because it provides underwriters with a more comprehensive view of the insured properties, enabling better risk assessment and decision-making. The complexity arises when brokers need to plug in additional data such as COPE details, hazard mapping data, or geocoding information, which must be accurately matched and merged with the existing information. This process requires careful validation to ensure that the enhancements are both accurate and relevant.
4. Insight analysis: This analysis is essential for understanding the risk profile, spotting trends, and identifying potential issues. The main challenge here is handling and interpreting large amounts of data to produce useful insights. This requires advanced tools and methods to process the data efficiently and accurately, so underwriters and brokers can make informed, data-driven decisions.
5. Exporting the processed data, often through integrations with external systems: This step is vital because it allows users to utilize advanced simulations and analysis tools to refine their risk management strategies. The difficulty lies in ensuring seamless integration between systems, which requires compatibility in data formats and workflows. Any issues in exporting data can delay the underwriting process, making it crucial to maintain smooth and accurate data transfers.

SOV Ingestion Tool #1: Archipelago

Archipelago is a productivity suite built for insurance brokers. It’s the industry’s only platform that addresses the needs of both the individual end-insured and the leaders who manage the portfolios of multiple organizations.

Some of the most demanding risk professionals, such as Alliant, Gallagher, Brookfield trust Archipelago to help use data to achieve better outcomes. See their customer success stories here.



Pros:

• Highest Data Quality: Archipelago delivers the highest quality data, as proven by feedback from top risk managers.
• Simplifying the Complex: Archipelago Pre-Check generates automatic data improvement recommendations in a presentation-ready format, making complex tasks more manageable.
• Workflow Tracking: Archipelago SOV Manager provides the necessary workflows for value collection and mid-term updates, ensuring efficient management.
• Alerts: Archipelago automatically flags insured-to-value, AML-to-TIV, and other outliers, helping to identify potential issues early.
• AI Prefill: Archipelago’s AI identifies gaps and reconciles data using leading industry sources.
• Scalable: Capable of handling SOVs ranging from hundreds of thousands to just a few records, Archipelago is designed to scale with your needs.
• Integrated with Leading Risk Modeling Platforms: Seamless integration with Moody’s Analytics (RMS) and Verisk EES (AIR) enhances risk assessment capabilities.
• Integrated with Insurer Systems: Direct integration into leading insurer systems ensures smooth data flow and operational efficiency.
• Integrated with RMIS Systems: Provides bidirectional synchronization with leading RMIS systems such as Origami, ensuring data consistency.
• SOC 2 Compliant: Archipelago is fully certified to meet the information security needs of the most demanding clients.
• White-Glove Support Offered: Archipelago is known for delivering premium support to organizations seeking top-tier service.
• Flexible Pricing: Archipelago is now available to individual brokers on a per-seat license basis, starting at $400 per month.

Cons:

• Priced Per User: The full Archipelago suite is currently available only through an annual per-user subscription. While affordably priced, it may not be ideal for those needing to process just a few dozen records.
• Property Risk Focused: Archipelago specializes in property data. While casualty support is expected in 2025, other lines of business do not yet have announced support dates.

#2: Ping Data Intelligence

Ping Data Intelligence is a purpose-built SOV ingestion tool. With its three modular products—SOV Fixer, Ping Data, and Ping Geocoding—the company is focused on addressing the needs of clients who are not looking for an end-to-end solution.

Ping's process involves brokers sending the SOV file via email, which is then processed and cleansed by the SOV Fixer module. Once the data is refined, the cleansed SOV is sent back to the broker for further use.

Pros:

• Easy to Get Started: Getting started is as simple as sending an email with an SOV attachment.
  
• Offers an API: Ping provides an API that developers can use to process data, in addition to email-based submissions.
  
• Data Enhancement: The company offers dozens of data integrations for records processed through its SOV Fixer product.
  
• Modular: Individual stages of processing, such as the Ping Geocoding service, are available separately, providing flexibility.
  
• Data Organization and Accessibility: Ping Analytics stores all customer data in a third-party warehouse provided by Snowflake, making it easily accessible for various analytical tasks, including SOV processing.
  
• Integration Capabilities: The software allows for direct API integration with platforms like AIR and RMS, facilitating seamless data flow while enhancing operational efficiency.
  

Cons:

• Pricing Metered by Data: Ping may not be the best choice for organizations needing high-volume processing because pricing is based on data usage.
  
• Limited Compliance: Ping does not currently state that it has the ability to conform to industry standards such as SOC 2, which may be a concern for organizations requiring rigorous end-to-end information security.
  
• Lack of Complete User Experience: Ping’s offerings are data-focused, and the company does not currently provide applications that facilitate end-user workflows or built-in analytics.
  
• Integration Challenges: While some integration capabilities are built-in, integrating with systems such as RMIS applications may require significant adjustments.
  
• Resource Requirements: Implementing and maintaining the software may require dedicated IT resources, which could be a burden for organizations lacking sufficient technical support.
  

#3: Convr

Convr is an AI-driven underwriting platform specifically designed for the commercial property and casualty (P&C) insurance sector. While not exclusively focused on SOV processing, Convr's platform offers capabilities that can be applied to this task. It uses artificial intelligence and machine learning to digitize and analyze submission data, including SOVs, to enhance underwriting productivity and accuracy.

Pros:

• AI-Driven Accuracy: Convr leverages machine learning algorithms to enhance data accuracy and minimize errors, making it a reliable choice for insurers and brokers.
  
• Automated Data Extraction: The platform automatically extracts data from various document formats, significantly reducing the need for manual input.
  
• Customizable Workflows: Convr allows users to create customized workflows that can be tailored to specific underwriting processes, providing flexibility in how submissions are handled.
  
• Scalable Solution: Convr is designed to scale with the needs of both small and large organizations, accommodating a wide range of submission volumes.
  
• Real-Time Data Validation: The platform offers real-time data validation, ensuring that submissions are accurate and complete before they are processed.
  

Cons:

• Limited Integration Options: Convr’s integration capabilities are more limited than those of some competitors, which could pose challenges for organizations with complex tech stacks.
  
• Higher Learning Curve: The advanced features and customization options may require a steeper learning curve for users who are less familiar with AI-driven tools.
  
• Pricing Structure: Convr’s pricing can be on the higher side, especially for smaller organizations or those with limited submission volumes.
  

#4: Scrub AI

Scrub AI is a specialized platform focused on cleaning and standardizing insurance data, particularly for SOVs. It’s designed to work seamlessly with existing systems, enhancing data quality before it reaches underwriting.

Pros:

• Data Cleansing Expertise: Scrub AI is specifically designed to clean and standardize SOV data, reducing the risk of errors in underwriting.
  
• Seamless Integration: The platform is built to integrate smoothly with existing insurance systems, minimizing disruption and ensuring continuity in operations.
  
• Automated Workflows: Scrub AI automates data cleaning processes, saving time and improving efficiency for insurance brokers and underwriters.
  
• Real-Time Feedback: The platform provides real-time feedback on data quality, allowing users to make immediate corrections as needed.
  
• Flexible Deployment: Scrub AI can be deployed either on-premises or in the cloud, providing flexibility to meet the specific needs of different organizations.
  

Cons:

• Niche Focus: Scrub AI’s focus on data cleansing may limit its utility for organizations looking for a more comprehensive SOV management solution.
  
• Limited Analytics: The platform does not offer extensive analytics capabilities, which may be a drawback for those seeking deeper insights into their data.
  
• Lack of End-to-End Solution: Scrub AI does not provide a full end-to-end SOV processing suite, so users may need to integrate it with other tools to complete the submission process.
  

#5: UiPath

UiPath is a leading robotic process automation (RPA) platform that can be tailored to handle SOV ingestion and processing tasks. Known for its versatility, UiPath can be customized to fit various data processing needs within the insurance industry.

Pros:

• Robust Automation: UiPath’s RPA capabilities allow for the automation of repetitive tasks, reducing manual effort and improving accuracy in SOV processing.
  
• Highly Customizable: The platform is highly customizable, enabling users to create specific workflows tailored to their unique processing requirements.
  
• Integration Flexibility: UiPath offers extensive integration options with a wide range of systems, making it suitable for complex insurance environments.
  
• Scalability: UiPath can scale from small projects to large enterprise-level implementations, accommodating organizations of all sizes.
  
• Low-Code/No-Code Interface: The platform offers a low-code/no-code interface, making it accessible to users with varying levels of technical expertise.
  

Cons:

• Complex Setup: Setting up and configuring UiPath for specific SOV processing tasks can be complex and may require specialized expertise.
  
• Higher Implementation Costs: Due to its extensive customization and integration capabilities, the implementation costs for UiPath can be higher than other platforms.
  
• Maintenance Requirements: Maintaining RPA processes can require ongoing support, which may be resource-intensive for organizations without dedicated IT teams.

Tired of spending days collecting, cleansing and validating submission data? Get your SOV improved with AI for free.