How to Improve Quote to Bind Ratios With AI

The quote to bind ratio is an essential metric for insurers, MGAs, and carriers that tracks the number of customers who progress from receiving a quote to buying a policy.

On a practical level, it's no mystery that insurers, particularly property and casualty insurers, find it difficult to improve this ratio since they face difficulties like daunting submission volumes, manual data extraction, and inadequate risk triage.

But AI is changing that– it’s a key enabler for improving quote to bind ratio, helping insurers automate submission intake, prioritize the best opportunities first, and streamline underwriting workflows. AI has such a profound impact that a recent survey by PwC found that 52 percent of surveyed insurance CEOs believe AI will make their company more profitable this year. 

With AI-powered automation, insurers can considerably raise their quote to bind ratios, driving efficiency and profitability. 

Key Challenges in the Submissions and Quoting Process

1. High Volume of Submissions

The sheer volume of submissions received makes efficient processing challenging. Most submissions are processed manually, which is slow and error-prone. Underwriters, therefore, spend too much time on low-probability risks when their time would be better spent on high-value opportunities that fit their risk appetite.

2. Manual Data Extraction and Validation

Submission documents exist in numerous formats, such as emails, PDFs, and spreadsheets. Manually extracting, verifying, and structuring this information is time-consuming and error prone. Moreover, key risk information is often missing or non-standardized, which makes it more difficult for underwriters to make informed decisions. 

3. Slow and Inefficient Risk Triage

Manual processing and inconsistent data inputs directly contribute to slow and inefficient risk triage. When submissions arrive in varying formats—emails, PDFs, spreadsheets—and require manual extraction and validation, underwriters are forced to spend time just getting to the starting line. 

This lack of structure makes it difficult to quickly identify which risks are worth pursuing. As a result, too much time is spent on low-value submissions, slowing down decision-making and ultimately hurting underwriting profitability.

4. Lack of Real-Time Insights on Quote to Bind Performance

Most insurers lack a centralized way of tracking and analyzing how underwriting teams handle submissions. Without visibility into key metrics, it's hard to streamline underwriting strategies, measure success, or identify areas for improvement.

How AI Improves Quote to Bind Ratios

Automating Submission Intake and Processing

P&C underwriters waste hours reviewing and manually inputting submission information, delaying the quote process. Additionally, incomplete or unstructured data holds up underwriting decisions.

AI-powered automation simplifies submission intake via data extraction and organization from web portals, PDFs, emails, and more. NLP and machine learning guarantee that all the required data points are captured before an underwriter sees a file. Solutions such as Brisc’s AI-powered Submissions Agent extract and verify data on your behalf, significantly decreasing manual work, accelerating the quoting process, and enhancing the quality of submissions that reach underwriters.

Intelligent Submission Triage for Prioritizing High-Value Opportunities

With multiple submissions, underwriters require a structured method of prioritizing the most promising opportunities. Most insurers lack an effective triage process and, as a result, spend time on low-value deals and let high-potential opportunities fall between the cracks.

AI risk triage allows underwriters to rank submissions against pre-established parameters such as underwriting appetite, risk profile, and historical conversion rates. Machine learning algorithms can predict which submissions will likely convert into bound policies so that underwriting teams can prioritize the most promising opportunities. AI-powered submission triage can intelligently surface the highest-value risks, enabling underwriters to maximize their conversion rates and profitability.

Faster and More Accurate Risk Assessment

Underwriters often rely on incomplete or fragmented data sources, making risk assessment slow and imprecise. Without the insights provided by AI, risk selection and pricing can be unintegrated, leading to poorer-quality decision-making.

AI consolidates structured and unstructured data, providing data-driven, real-time intelligence and improving underwriting accuracy. Predictive analytics can augment risk evaluation processes, allowing for improved pricing and selection and highlighting missing or contradictory data. For example, Brisc Insights enables underwriters to search, filter, and analyze underwriting data, pushing maximum pricing models and accurately facilitating risk evaluations.

Reducing Operational Costs and Increasing Underwriting Efficiency

Legacy underwriting workflows often comprise multiple disconnected systems, which create inefficiencies and higher operational expenses. Manual processes generate bottlenecks, slowing down deal flow and reducing productivity. 

AI consolidates underwriting into one system via automated processes. In fact, automated underwriting platforms will underwrite more than 70% of personal lines applications this year without human intervention, reducing underwriting expenses by 45%. Solutions like Brisc help automate and simplify submission intake, risk analysis, and decision-making while reducing costs and improving overall efficiency.

The Competitive Advantage of AI-Driven Submissions Automation

With faster, more accurate quoting, AI-based automation can increase quote to bind ratios, binding more policies using the same underwriting capacity. This translates into higher profitability and better operating efficiency.

AI also enhances risk selection, reducing loss ratios by keeping insurers focused on the right risks. AI is not replacing underwriters but augmenting their capabilities, allowing them to make better decisions and liberating them from tedious administrative tasks.

This year, it’s expected that insurers with mature AI systems will report:

• Combined ratios seven to 10 points better than industry averages.
• Customer acquisition costs 40% lower than traditional channels.
• Customer retention rates 15 percentage points higher than non-AI peers.
• Loss ratios of five to seven points better than competitors.

Final Thoughts

Maximizing quote to bind ratios is a significant goal for carriers, MGAs, and insurers trying to realize optimal efficiency and profitability. AI-driven automation transforms underwriting through submission intake automation, enriching risk assessment and prioritizing high-potential opportunities.

Brisc’s AI-powered Submissions solution enables insurers to automate data extraction, intelligently triage submissions, and gain real-time risk insights faster and with more precision than ever before.

Ready to transform your underwriting process? Learn more about Brisc’s AI-driven Submissions Agent today.