Your people need information, not data. On average, they waste a day a week searching across silos, systems, and clouds for information. It’s pre-digital-age work. Learn how AI-Powered Search gives your employees the information and intelligence they need.
An IDC Infographic Sponsored By: Sinequa
The Top 5 Business Outcomes for Financial Institutions with AI-Powered Platforms
Cognitive search capabilities help financial institutions make the best use of their data, while creating insights that drive business opportunities.
Data Management Challenges
Top drivers of the data explosion in financial institutions:
Data regulations require financial institutions to make data more secure, accessible, and transparent.
Information-Driven Financial Institutions Connect and Contextualize Data Using AI-Powered Platforms
AI-powered search and analytic platforms give knowledge workers the ability to see data in context.
5 Business Outcomes for Financial Institutions Using an AI-Powered Platform
Reinforce protection of confidential documents and improve productivity at the same time using natural language processing and machine learning.
As assets shift out of active investments into passive investments, beat the market in investor care with service alpha.
Develop a complete view of the customer at every level of your bank, from the branch to the board.
See and connect patterns across many sources, including people, transactions, phone calls, email, and travel activity to find financial crime faster.
Minimize the risk of digital transformation and big data projects. Instead, understand data across legacy systems and silos. And turn unmanageable amounts of data into information that can be used to make valuable decisions.
Financial institutions can deliver personalized customer experiences, make quicker decisions, and adapt quicker to regulatory changes.
Written by guest blogger, David Schubmehl, IDC Research Director, Cognitive/Artificial
Intelligence Systems. Sponsored by Sinequa.
Over the last several years, I’ve spoken to many organizations that have all asked the same question: How can we most effectively make use of all of the research, documents, email, customer records and other information that our employees have collected over the years, especially those that are now retiring? In the past, organizations had corporate libraries and corporate librarians whose job it was to help collect, organize, and disseminate information to employees and staff when and where they needed it. That department and positions are long gone from most organizations today. Why have they gone? The rate of data and documents (including research papers, contracts, and even emails) has exploded, making this task impossible. But let’s be honest: even before today’s information explosion, no classification system could ever keep up with the fast pace of change in the economy. No one could have foreseen today’s most important questions, in content categories that did not exist until today. And with the baby boomers retiring at an ever-increasing rate, an urgent question must be asked: How do organizations get the most value from the vast amounts of information and knowledge that they’ve accumulated over decades?
IDC has identified the characteristics of organizations that are able to extract more value out of the information and the data available to them. Leader organizations make use of information access and analysis technologies to facilitate information access, retrieval, location, discovery, and sharing among their employees and other stakeholders. These insight leaders are characterized by:
- Strategic use of information extracted from both content and data assets
- Efficient access to unified and efficient access to information
- Effective query capabilities (including dashboards)
- Effective sharing and reuse of information among employees and other stakeholders
- Access to subject matter experts and to the accumulated expertise of the organization
- Effective leverage of relationships between information from different content and data sources
So how can artificial intelligence (AI) and machine learning affect information access and retrieval? The types of questions that are best answered by AI-enabled information access and retrieval tools are those that require input from many different data sources and often aren’t simple yes/no answers. In many cases, these types of questions rely on semantic reasoning where AI makes connections across an aggregated corpus of data and uses reasoning strategies to surface insights about entities and relationships. This is often done by building a broad-based searchable information index covering structured, unstructured, and semi-structured data across a range of topics (commonly called a knowledge base) and then using a knowledge graph that supports the AI based reasoning.
AI-enabled search systems facilitate the discovery, use, and informed collaboration during analysis and decision making. These technologies use information curation, machine learning, information retrieval, knowledge graphs, relevancy training, anomaly detection, and numerous other components to help workers answer questions, predict future events, surface unseen relationships and trends, provide recommendations, and take actions to fix issues.
Content analytics, natural language processing, and entity and relationship extraction are key components in dealing with enterprise information. According to IDC’s Global DataSphere model developed in 2018, of the 29 ZB of data creation, 88% is unstructured content that needs the aforementioned technologies to understand and extract the value from it. In addition, most of this content is stored in dozens, if not hundreds of individual silos, so repository connectors and content aggregation capabilities are also highly desired.
AI and machine learning provide actionable insights and can enable intelligent automation and decision making. Key technology and process considerations include:
- Gleaning insights from unstructured data and helping to “connect the dots” between previously unrelated data points
- Presenting actionable information in context to surface insights, inform decisions, and elevate productivity with an easy-to-use application
- Utilizing information handling technologies that can be used in large scale deployments in complex, heterogeneous, and data-sensitive environments
- Enriching content automatically and at scale
- Improving relevancy continuously over time, based on user actions driven by machine learning
- Improving understanding by intelligently analyzing unstructured content
IDC believes that the future for AI-based information access and retrieval systems is very bright, because the use of AI and machine learning coupled with next-generation content analysis technologies enable search systems to empower knowledge workers with the right information at the right time.
The bottom line is this: enabled by machine learning–based automation, there will be a massive change in the way data and content is managed and analyzed to provide advisory services and support or automate decision making across the enterprise. Using information-driven technologies and processes, the scope of knowledge work, advisory services, and decisions that will benefit from automation will expand exponentially based on intelligent AI-driven systems like those that Sinequa is offering.
For more information on using AI to be an information leader, I invite you to read the IDC Infographic, Become Information Driven, sponsored by Sinequa at https://www.sinequa.com/become-information-driven-sinequa/
Sinequa has again been named a leader in “The Forrester™ Wave: Cognitive Search, Q2 2019.” The report is available for download here.
For global enterprises with complex use cases, Sinequa stands out as the right platform, according to the latest Forrester™ Wave1.
Let’s take a closer look and unpack some of the underlying details….
“Sinequa augments intelligence en masse […] to help organizations become ‘information driven’ by augmenting the intelligence of every employee.”
This statement astutely captures Sinequa’s view that it’s not about the data, the software, or analytics, it’s about the people. This perspective lies at the heart of our tagline, Become Information-Driven, which implies that organizations should leverage modern technology to power solutions that help individual knowledge workers accomplish their goals in order for the entire organization to perform at peak levels and outcompete in their market.
“[Sinequa] accomplishes this by surfacing knowledge, uncovering insights, and connecting experts via its cognitive search technology.”
These are indeed critical capabilities that should transcend the current (or future) state of the customer’s IT environment. As Sinequa-powered solutions can (and should!) be deployed wherever the relevant content and data resides – whether it be on-premise, in private or public clouds, or as a hybrid model – Sinequa customers can choose the deployment model that best aligns with their strategy and security policies, particularly when it comes to hosting sensitive data in the cloud.
“Sinequa expertly balances using its own NLU technology in combination with the latest open source ML technology.”
At its core, the Sinequa platform employs technology that automates the interpretation of meaning and applies structure to unstructured content. It intelligently combines deep natural language processing, rich semantic analysis, advanced entity extraction, and machine learning technology. This combination distills meaning from unstructured content and removes the noise, producing a clean and enriched index that consistently provides relevant information in context. In recent years, Machine Learning technology has been expertly woven into the platform to enhance understanding of complex data through experience instead of (or along with) codified business rules. Cohesively integrating these technologies into the Sinequa platform has effectively produced a production-grade machine learning platform for any enterprise.
“[Sinequa] has significant industry expertise in and offers solutions for life sciences, financial services, and manufacturing.”
This is no accident. With over a decade of experience, Sinequa understands the attributes of our most successful customers and realizes that they thrive with Sinequa because:
- They are geographically-distributed and knowledge-centric
- They have ambitious business goals and sophisticated IT environments
- They routinely collaborate internally across geographies and across lines of business to drive decisions
- They depend on highly diverse, high value content and data in different languages to drive their business forward
- They either work with Systems Integrators for project execution or have their own in-house resources with a dedicated focus on systems integration
At Sinequa, we realize that a large share of our customers match these criteria and occupy the verticals pointed out by Forrester – i.e. life sciences, financial services, and manufacturing. After all, this is where Sinequa’s core competencies and unique capabilities can make the biggest impact.
1 Forrester Research, Inc., “The Forrester Wave™: Cognitive Search, Q2 2019” by Mike Gualtieri, with Srividya Sridharan and Elizabeth Hoberman.
Today, we announced that Sinequa is featured in a new IDC Technology Spotlight report: Financial Services Organizations: Extracting Powerful Insights with AI-Powered Platforms. The report, written by Steven D’Alfonso, research director, IDC Financial Insights, and David Schubmehl, research director, Cognitive/AI Systems, highlights the importance of AI-powered platforms in their ability to extract insights from data as well as the need for financial services organizations (FSOs) to improve their capabilities to derive insights from the data they possess.
According to the report, collecting and maintaining increased amounts of data related to their clients and portfolios can provide major opportunities to improve the customer experience and increase revenue while reducing risk. But at the same time, too much data can be a cognitive drain on analysts and knowledge workers. This increasing need to collect data from multiple applications requires FSO stakeholders to organize and provision their data in ways that allow analysts to extract meaningful insights. AI can help FSOs mature from being data-driven to being information-driven.
“Over the years, Sinequa has continued to expand its footprint within leading financial institutions such as Credit Agricole, DZ Bank, LCL, Navy Federal Credit Union, and U.S. Bank as our platform enables them to tackle the challenges highlighted in this report,” said Scott Parker, director of product marketing at Sinequa. “By offering a broad-based AI-powered platform including search, content analytics, semantic understanding and auto categorization technologies, Sinequa provides relevant insights to users in their work environments, while supporting a range of machine learning algorithms and capabilities to improve findability and relevance, allowing FSOs to access the information they need when they need it.”
With the demand for AI technologies that enable intelligent analytics increasing every year, IDC estimates that “by 2022 spending on AI technologies will grow to over $8 billion, up from $2 billion in 2017.” Sinequa has in the past offered a flexible information collection, access and analysis architecture and now provides cognitive capabilities, such as machine learning, natural language processing, improved relevance and better decision support, while offering intuitive user and data interaction capabilities.
To learn more, click here or on the banner below to sign up for the webinar.