Loan Notices Data Extraction

Our client was looking to expand their offerings and needed a solution for loan notice data management.​

Semantic Evolution implemented a loan notice data solution utilizing a user-friendly interface. The client was able to configure their own data model and create a fully integrated solution within two months.​

The solution was able to process 50,000 notices annually with a STP ratio of 95% or higher, resulting in significant improvements in productivity and efficiency. The implementation successfully streamlined data management and opened new business opportunities for the client.

Streamlining Insurance Report Processing

With new legislation requiring insurance companies to report solvency data, ratings agencies faced the challenge of efficiently extracting large volumes of data from SFRC Solvency Reports. The manual process was time-consuming and prone to errors.​

A semantic approach was taken to automate the process using a trained model called Semantic Extract. The model used table and hierarchy detection, stemming, and a user interface to pick up key data from the solvency reports.​

The implementation of the semantic approach resulted in 99.98% accuracy and an 87% time saving, making the data extraction process much more efficient and reliable.​

Automating Corporate Action Data Extraction

Our client faced the challenge of manually extracting key data points from seasonal, time-sensitive corporate action documents, which resulted in a dedicated team of 5 to handle unpredictable volumes.​

By using a semantic extractor (SE), the high volumes of documents could be processed in seconds, and the data could be seamlessly pushed into the database with no manual intervention. Validation rules were implemented to ensure data quality assurance.​

The implementation of the semantic extractor resulted in a 99.9% time saving and a 98% accuracy rate, making the data extraction process much more efficient and reliable.

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