New planned features are designed to help business users quickly start applying AI to find more precise document insights with less training time and data science skills
Businesses in financial services, insurance and legal services turn to Watson Discovery to help automate processes and enhance customer care
IBM today announced new natural language processing (NLP) enhancements planned for IBM Watson Discovery. These planned updates are designed to help business users in industries such as financial services, insurance and legal services enhance customer care and accelerate business processes by uncovering insights and synthesizing information from complex documents.
Businesses are increasingly turning to NLP and machine learning to help them comb through rising volumes of documents and data sets in a wide range of formats1. By applying AI to get document insights, business users can reduce research time and help their employees make more fact-driven decisions during complex, time sensitive tasks such as processing insurance claims, conducting financial analyses and reviewing legal agreements or contracts.
The new planned features that IBM announced today are designed to make it easier for Watson Discovery users to quickly customize the underlying NLP models on the unique language of their business. Stemming from NLP advancements developed by IBM Research, business users can train Watson Discovery to help read, understand and surface more precise insights from large sets of complex, industry-specific documents even if they don't have significant data science skills.
- Pre-trained document structure understanding: Watson Discovery's Smart Document Understanding feature, available now in the Plus, Enterprise and Premium plans, includes a new pre-trained model that is designed to automatically understand the visual structure and layout of a document without additional training from a developer or data scientist. This helps users quickly find answers that were previously hidden or difficult to find like text in complex table structures or images.
- Automatic text pattern detection: IBM has released a new advanced pattern creation feature in beta in the Plus, Premium and Enterprise plans that is designed to help users quickly identify business-specific text patterns within their documents. This is key for tasks like analyzing massive amounts of contracts or financial reports, which may report the same type of information, such as an increase or decrease in revenue, in different formats or using different phrases. Developed by IBM Research, it helps provide efficient ways of labeling data and training models. It is designed to start learning the underlying text patterns from as few as two examples and then refines the pattern based on user feedback. This helps users more rapidly train a model without manual and time-intensive tasks like defining rules and expressions.
- Advanced NLP customization capabilities: Training NLP models to identify highly customized, business-specific words and phrases – for example insurance claim forms may include specific claim reasons or affected products – is a time-consuming task that requires significant data prep, labeling, and orchestration. Models trained on generic data sets often fail to retrieve the right information. With a new custom entity extractor feature, now available in beta for Watson Discovery Premium users, IBM is simplifying this process by reducing the effort for data prep, simplifying labeling with active learning and bulk annotation capabilities, and enabling simple model deployment that can accelerate training time.
The planned updates announced today are part of a pipeline of developments stemming from IBM Research. For example, answer finding was recently made generally available in Watson Discovery and Watson Assistant's Search Skill. It is designed to help busy professionals and customers identify the precise insights they need.
"The stream of innovation coming to IBM Watson from IBM Research is why global businesses in the fields of financial services, insurance and legal services turn to IBM to help detect emerging business trends, gain operational efficiency and empower their workers to uncover new insights," said Daniel Hernandez, General Manager of Data and AI, IBM. "The pipeline of natural language processing innovations we're adding to Watson Discovery can continue to provide businesses with the capabilities to more easily extract the signal from the noise and better serve their customers and employees."
In addition to the new features announced today, IBM is highlighting how organizations in the legal services, financial services and insurance sectors use Watson Discovery's existing features to help automate and transform business processes.
Contract management can be a slow, manual and complex process. IBM business partner ContractPodAi, an award-winning provider of the AI-powered contract lifecycle management (CLM) led solution 'One Legal Platform', extended its end-to-end solution with several AI technologies including IBM Watson Discovery, among other providers. The solution helps simplify the complexities of contract management, automate mundane tasks and transform complicated workflows. Building on the strength of ContractPodAi's CLM solution, the no-code platform is designed to help in-house legal teams manage many legal scenarios, processes, or documents using the platform's pre-built and configurable applications, such as claims, RFP review, and IP portfolio management.
To learn more about Watson Discovery, please visit: https://www.ibm.com/cloud/watson-discovery
About IBM Watson
Watson is IBM's AI technology for business, helping organizations to better predict and shape future outcomes, automate complex processes, and optimize employees' time. Watson has evolved from an IBM Research project, to experimentation, to a scaled, open set of products that run anywhere. With more than 40,000 client engagements, Watson is being applied by leading global brands across a variety of industries to transform how people work. To learn more, visit: https://www.ibm.com/watson.