WNS Skense ingests data (both structured and unstructured), applies proprietary algorithms to generate contextualized information, and finally summarizes to create structured and harmonized data sets for business analysis. This automated data processing engine is powered by advanced Artificial Intelligence (AI) / Machine Learning (ML) techniques that provide businesses with quick, accurate and actionable insights.
WNS Skense is scalable, flexible and easy-to-deploy.
It simplifies data management across the three core dimensions of volume, variety and veracity.
Real-time data ingestion using custom connectors or Application Programming Interfaces (APIs)
Wider coverage of documents - TIFF, PNG, PDF, DOC, XLS
Customizable business rules, AI / ML models and ontologies to drive contextualization
Serverless deployment of ML models enabling high availability and ability to auto scale
Fully scalable and platform agnostic
Intuitive User Interface (UI) for human validation and customizable workflow
Built-in workflow visualization
Integrated decision engines and analytical models
WNS Skense automates the extraction, organization, cleansing and interpretation of data in any format and in large volumes.
WNS Skense works as a digital intelligent store combining data formats, domains, output formats and target systems to deliver significant value across the enterprise.
Improves ROI on investments in data science capabilities and tooling
Minimizes organizational risk arising from being people / system dependent or running siloed intellectual property
Reduces cost of operations by 40-60 percent in steady state
Improves ability to detect common data issues
Enables application of business rules on a harmonized output format for subsequent processing
Cost savings of $50Mn for a large FMCG company using cognitive data management for aligning quality assurance and product supply operations to product life cycle management and GxP specifications
Underwriting optimization by ~50% and overall accurancy levels of 85%+ for a leading broker in Lloyd’s of London through ML and Deep Learning (DL) led automated data extraction
Cost savings of ~$9Mn over 5 years, with an ROI of 16x (yearly volume of 270 million documents) through automated classification of shipment documents, followed by relevant information extraction
Processing time reduction by 50% and cost containment by 25-30% for a US regional bank using ML-driven financial spreading