Case Study: BFSI
Effective Document Retrieval System results in 90% Boost in productivity
In the paperless world, finding the right document could not only be a challenge but also an impediment in efficiency. Once a document is found the other challenge is to summarize large documents and HTML pages retrieved from a particular search.
One of the largest Investment management organizations in the World wanted an effective way to solve the Document Retrieval Problem, they leveraged the cognitive expertise of Abzooba. The first step was to introduce Crawlers to extract data from internal documents and external websites eg: Bloomsberg, BlackRock etc. A type of Deep Neural Networks (DNN) was used for Convolutional Deep Structures Semantic Models (CDSSM) and a combination of Convolutional and Recurrent Neural Networks (CNN + RNN) with Vector Space Models (VSMs) was the AI footprint which served the basis od DNN. The system was also programmed for self-learning and mature with every search as a continuous feedback mechanism.
The search reduced manual effort and increased productivity by 90% while searching for relevant business content from stock-pile.