Accueil » Stackathon Segments » Customer Self-Service Quote/Bind/Issue

The following vendor(s) were chosen

by the Stackathon broker participants

as their top choice(s) in Customer Self-Service Quote/Bind/Issue

Quotey

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Apollo

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Trufla truMobile

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Nude Solutions

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Calefy

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Coalition

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Boxx Insurance

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Customer Self Serve Quote/Rate/Issue

Scoring Criteria
Ease of Access and Navigation (5 points)
Description: The simplicity with which customers can access and navigate the online quoting, binding, and policy issuance platform. This includes both
desktop and mobile interfaces.
Considerations: How intuitive and user-friendly is the self-service portal for customers looking to get quotes, bind policies, and issue documents? Are the steps
to get a quote clear, and can the customer easily proceed to binding and policy issuance without broker intervention? Does the platform work seamlessly
across devices, ensuring an accessible experience on both desktop and mobile?


Speed and Accuracy (5 points)
Description: The efficiency and precision with which the platform generates quotes based on the customer’s provided information.
Considerations: How quickly can the platform generate quotes? Are the quotes accurate and compliant with underwriting rules? Does the system provide
clear, real-time updates on premiums, discounts, and product options? Is it easy for customers to adjust coverage details and get an updated quote?


Binding and Policy Issuance (5 points)
Description: The platform’s ability to allow customers to complete the policy binding and issuance process independently, without the need for broker
involvement.
Considerations: Can customers quickly and securely bind policies after receiving a quote? How seamless is the transition from quoting to binding? Does the
system handle electronic document generation, signatures, and policy issuance in an automated, customer-friendly way?


Payment and Billing Integration (5 points)
Description: The system’s capability to integrate with billing and payment platforms, allowing customers to complete their purchase immediately.
Considerations: Can customers easily make payments online through a variety of options (credit card, bank transfer, etc.)? How smoothly does the system
process payments.

Customization and Flexibility of Coverage (5 points)
Description: The platform’s ability to offer customizable coverage options and pricing for different insurance products based on customer needs.
Considerations: Can customers tailor their coverage during the quoting process? Is there flexibility in adding endorsements, selecting different coverage
levels, or adjusting deductibles? How well does the platform guide customers in choosing the right products or upselling additional coverage options?


Security and Compliance (5 points)
Description: The security measures in place to protect customer data and ensure compliance with regulations during the online quoting and policy issuance
process.
Considerations: Does the platform offer secure login, encryption, and transaction protocols to protect customer information? Is it compliant with global and
regional data protection laws like GDPR or PIPEDA? How well does it handle sensitive customer and policy data during the quote, bind, and issuance stages?


Pricing and Value for Money (5 Points)
Description: The cost-effectiveness of the system in relation to its features, performance, and value provided.
Considerations: Is the pricing competitive, flexible, and justified by the system’s features, performance, and return on investment?

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Aligning with Regulatory Frameworks and Standards

Principles will align with international AI guidelines (e.g., ISO 42001, OECD frameworks) and industry-specific regulations (e.g., RIBO, OSFI, AMF) upheld by stakeholders. Adherence to legal standards will help organizations navigate jurisdictional requirements, promote sustainable practices, and prevent misuse of AI data

Encouraging Responsible Al Innovation

Principles will encourage the broker community to innovate responsibly by developing AI systems that prioritize consumer well-being, inclusivity, and fairness, while also assessing the societal, environmental, and economic impacts of their AI solutions

Promoting Accountability in AI Oversight

AI principles will reinforce accountability across all levels of organizations and third-party collaborators. By defining roles, ensure human oversight in AI processes, this enhances traceability, enables informed decision-making, and embeds mechanisms for ethical redress when errors or adverse outcomes occur

Ensuring Consumer Trust and Fairness

AI governance principles support commitment to transparency, fairness, and accountability. By requiring explainable outcomes and proactive consumer communication, it fosters trust among the broader broker community, their clients, and external stakeholders

Supporting Ethical Standards and Stakeholder Collaboration

By incorporating AI governance principles, broker members can align with its mission of fostering an ethical culture among its stakeholders Address biases, safeguard consumer protection, and promote inclusivity, which will reinforce commitment to ethical AI practices in collaboration with industry stakeholders, regulators, and third-party solution providers

PoC Use Case Overview: Al-Assisted Coverage Discovery & Gap Analysis

Technical Requirements

AI models with context on industry benchmarks and policy structures to interpret existing policy terms, endorsements, and clauses.

Integration with BMS to retrieve client profiles, exposure information, and historical policy data.

Data ingestion and continuous updates to ensure alignment with typical coverage patterns and industry guidance.

Data security and compliance features to protect client information.

Functional Scope

Analyze client-submitted data, including exposures, business context, and other relevant information.

Extract and interpret existing policy terms (e.g., endorsements, exclusions, clauses).

Benchmark against typical coverage patterns and industry guidance to identify coverage gaps.

Prioritize identified coverage gaps and provide rationale based on considerations such as industry standards and risk.

Recommend relevant products and coverage options tailored to the client profile, and generate summaries for client discussions.

AI-Powered Client Onboarding & Data Intake – Overview:
Proof of Concept (PoC) Use Case

Technical Requirements

BMS Integration: Integration with BMS for secure data capture and storage.

Applied ARS Integration: Integration with Applied ARS to enable automated quote generation.

Dedicated Parsers for Renewal: Document parsing capability for 5–6 carrier renewal documents with high accuracy.

Extensibility Framework: Modular architecture to support future enhancements and additional automation.

Functional Scope

AI Chatbot for Client Onboarding: Collect client information, answer onboarding questions, and guide users through the onboarding process.

Data Collection & Storage: Capture and store collected data directly in the broker’s BMS.

Document Processing: Enable clients to upload renewal documents and extract key data to accelerate the onboarding process.

Quotes Generation: Generate quotes based on collected information.