Customers Are Using Advanced Machine Learning and NLP Capabilities to Enable an Information-Driven Approach for Improved Business Operations and Smarter Decision-Making
Sinequa, a recognized leader in the AI-powered search and analytics market, announced that its Intelligent Search platform is helping 2.5 million digital workers leverage 5 billion documents and 100 billion records to extract insights and actionable information for improved business operations and smarter decision-making.
“In fact, ML is emerging as a critical leap in modernizing many workflows and legacy data infrastructure within banks and other financial institutions.”
“Pragmatic AI technology is making a significant impact within the financial services sector, where machine learning is transforming business processes related to customer service, personal finance, fraud detection, and risk management,” said Dan Faggela, CEO at Emerj, an Artificial Intelligence Research company. “In fact, ML is emerging as a critical leap in modernizing many workflows and legacy data infrastructure within banks and other financial institutions.”
In 2018, McKinsey surveyed 2,000 executives across 10 industries, and found that 47% of companies have embedded AI in their business processes. This represents a rapid increase in adoption: the 2017 study found that just 20% of respondents were using AI in a core part of their business. As a leader in the Intelligent Search space, Sinequa contributes to help organizations make the leap to AI by allowing users to leverage Sinequa’s Machine Learning and NLP capabilities to augment user’s human intelligence across the entire experience.
Our customers have gone beyond testing AI models in their labs. Some of the most powerful and real-world deployments in production to date are leveraging intelligent search capabilities in the following use cases:
Financial Services – Identification and Protection of Confidential Content
A global top 20 bank is using Sinequa’s integrated machine learning capabilities to automate the identification and classification of confidential content, in accordance with its internal policies and external regulations. Estimated dollar benefits are in excess of $50 million per year in productivity and compliance gains.
Customer Service – Streamlining Support Ticket Routing and Resolution
A global technology provider is applying Sinequa algorithms to automatically serve help desks by routing complex tickets to the relevant expert team. Immediate benefits include faster Total Time to Resolution (TTR) and support desk congestion relief – in many cases, the combined application of search and AI enables customers to find answers to questions before the phone even rings in a support center.
“The need for business decisions to be based on data is not news to any competitive organization. Until recently, extracting actionable information from data at scale required a significant investment of time and human analysis. Today, Machine Learning completely automates the process of sifting through enormous volumes of data and proposing actionable information to users enabling faster and better decisions,” explained Vincent Bodin, head of the Machine Learning Development Team at Sinequa. “Possibly the single most exciting development for Sinequa in the last year is the surge in customer machine learning projects, which contributed significant business value back to the respective organizations, especially in the financial services industry where confidentiality and privacy are critical issues.”