Inaza Blog

Dive into how AI recognizes vehicle damage through deep learning trained on millions of images, providing reliable visual evidence in insurance claims.

Learn how insurers can scale from task-level tools to a full data platform that unites underwriting, claims, and fraud automation.

Learn why insurers should avoid “rip and replace” overhauls and instead adopt AI in focused, high-impact stages that deliver results from day one.

Understand how finance leaders can leverage AI-based invoice analysis to improve operational efficiency, control costs, and reduce claim leakage.

Discover the measurable savings and efficiency gains insurers achieve by automating loss run data entry and validation processes.

Upload fleet files to Inaza Central and get structured, validated data in seconds—or integrate the API directly into your underwriting workflow.

Explore how computer vision standardizes claim evaluation, ensuring fairness and transparency for policyholders and insurers alike.

Learn how insurers use AI image recognition to instantly classify and score vehicle damage, cutting claims assessment time while improving accuracy and consistency.

Learn how insurers can automate claims step-by-step, improving response times, fraud detection, and transparency without system disruption.

Learn how insurers can defend against generative AI-created invoices using Inaza’s multi-layer fraud detection model that spots synthetic content and suspicious file patterns instantly.

Learn how insurers can integrate AI through connectors and APIs, automating processes with zero disruption or retraining.

Learn how AI understands context and structure in complex loss runs—something OCR alone can’t achieve—enabling truly automated underwriting readiness.