Streamlining risk assessments in underwriting using Marvel.ai

What our customer encountered?

The insurer faced the following challenges in their underwriting department during meticulous and granular segmentation of risk analysis:

Extensive external data handling challenges in risk segmentation:

Handling large volume of external data for customer-specific risk analysis.
Manual extraction of granular unstructured data causes complications in data verification, quality assurance, and re-work due to duplicate entries.
Unavailability of comprehensive customer data, for future risk analysis

Other challenges

Lack of third-party data source integrations

Cross-referencing multiple applications for review
Involvement in non-core activities leading to avoidable delays.

The challenges resulted in:

Lengthy underwriting process
Unpredictability of fraudulent claims at an earlier stage
Increased customer acquisition cost
Substantial delay in policy issuance
Unsatisfactory customer experience

What our customer needed?

Intelligent document data extraction to speed up the underwriting process and improve their quote-to-bind ratio.

The Solution

Marvel.ai – an intelligent document data processor has been introduced to extract critical information for underwriters’ review from various data sources, including emails, scanned and native PDFs, and financial documents.

Through advanced AI, ML, and NLP algorithms, it rapidly extracts and categorizes intricate data sets for analysis from extensive documents, delivering structured, ready-to-use data that unlock valuable insights in just minutes.