Companies integrate with new cybersecurity systems on a regular basis, resulting in a large volume of unstructured notifications. Across vendors, notifications are not standardized and cannot be easily aggregated and monitored.
With DocDecode, cybersecurity notifications are aggregated, classified, and labeled with distinct insights. Users can monitor, analyze, and predict future incidents.
Transitioning away from LIBOR is going to be a complex, expensive, and multi-year process. DocDecode can optimize several underlying processes:
1. Categorize fallback policies into discrete groups. DocDecode can automatically identify the major groups of LIBOR fallback policies mentioned in mortgage and derivative contracts. Risk officers can focus on defining management strategies for the major groups of fallback policies. To facilitate optimal risk profiling, all idiosyncratic policies not captured in one of the major groups will be separately flagged for review.
2. Granular analysis of fallback policy. DocDecode can characterize patterns and concepts present within and across fallback policy groups. This granular analysis can speed up the search of contracts that match a given set of criteria and inform adequate management strategies.
DocDecode processes both scanned and digitized documents. The DocDecode interface empowers the end user to provide feedback back into the AI. In addition, non-technical users can train new types of AI reading machines to retrieve custom insights from financial contracts.
Structured Finance, Insurance
Claims specialists wrangle a variety of data points, both structured and unstructured. Automated solutions can assist with the analysis of structured data but machines cannot reason over unstructured data.
Users leverage DocDecode to convert unstructured claims documents into structured data and enable more accurate evaluation of open claims.