Intelligent Underwriting

Underwriting teams face slow quote-to-bind cycles and inconsistent risk decisions. Manual intake, late-stage checks, and conservative pricing buffers drive leakage.

Underwriting operations are slowed by fragmented submission intake, manual validation, and incomplete data across documents and broker inputs. Risk assessment often relies on static rules and coarse rating factors, with limited use of behavioral or historical signals. Fraud checks are frequently siloed or performed late, leading to rework and false positives. Pricing teams compensate for uncertainty with conservative buffers that are weakly tied to actual risk. Decisions can vary across underwriters, and it is difficult to provide transparent, audit-ready justification for accept/refer/decline outcomes.

Unify intake, validation, risk scoring, fraud checks, and pricing into one workflow. Deliver consistent, explainable accept/refer/decline recommendations with audit-ready traceability.

The solution automates the end-to-end underwriting flow by ingesting structured and unstructured submission data, validating completeness and consistency, and enriching records with relevant historical and behavioral signals. It generates predictive risk and fraud scores early in the process and applies technical pricing guardrails aligned to expected loss targets. A decisioning layer recommends accept/refer/decline outcomes with clear drivers and confidence indicators, enabling straight-through processing for low-risk submissions and routing exceptions for human review. All actions and outputs are logged for traceability to support regulatory and audit requirements.

Delivers faster, more consistent underwriting by automating intake, improving risk and pricing precision, and providing clear, audit-ready decision rationale.

Pricing and selection decisions align to expected loss, reducing adverse selection and over-buffered premiums.

Straight-through processing automates low-risk submissions, cutting manual validation cycles and back-and-forth with brokers.

Clear decision drivers, confidence scores, and audit logs support regulator scrutiny and consistent underwriting outcomes.

Automates ingestion of structured and unstructured submission data, performs completeness and consistency checks, and enriches underwriting records with relevant historical signals. The workflow reduces re-keying and broker back-and-forth by producing a normalized, decision-ready submission packet. Built on AWS serverless components for scale and reliability, it supports consistent intake standards across teams and regions.

Generates risk and fraud insights early, applies configurable pricing guardrails aligned to portfolio targets, and produces transparent decision recommendations (accept/refer/decline) with confidence indicators and clear drivers. Decision traces and artifacts are retained for auditability, enabling consistent outcomes and easier justification to regulators, internal governance teams, and customers.

Brillio enables faster, more consistent underwriting decisions on AWS by orchestrating intake, validation, risk scoring, fraud signals, and pricing guardrails in a single workflow. Using serverless services for orchestration and integration, the solution scales to peak submission volumes while maintaining traceability and control. AI services support early risk and fraud insights, and decisioning produces auditable accept/refer/decline recommendations with confidence indicators. Data and artifacts are securely stored for governance and reporting, helping underwriting teams reduce manual rework, tighten loss ratio control, and improve explainability for regulators and customers.

AWS Native Services Utilized: AWS Lambda, Amazon Bedrock, Amazon Textract

A large national insurer faced slow quote-to-bind cycles driven by manual submission intake, inconsistent validation, late fraud checks, and conservative pricing buffers. Underwriting outcomes varied across teams, and decision rationales were difficult to defend during audits and compliance reviews.

Brillio implemented an AWS-based underwriting workflow that automated intake and validation, introduced early risk and fraud scoring, applied pricing guardrails aligned to loss targets, and generated explainable accept/refer/decline recommendations. Exceptions were routed to underwriters with full context.

The insurer reduced manual rework and improved underwriting throughput for low-risk submissions, while maintaining human oversight for complex cases. Pricing became more tightly linked to risk, and decision rationale became consistent and audit-ready, improving regulatory confidence.