Insight Engines in Wealth: How to Build Tomorrow’s Opportunities Today

Insight Engines in Wealth

McKinsey feels pessimistic. In their recent report, On the cusp of change: North American wealth management in 2030McKinsey forecast the future of wealth management. It’s a useful, thoughtful report. But you don’t have to wait until 2030. Most of the opportunities they sketch can be built today, with an insight engine.

Unsurprisingly, McKinsey provides a useful framework to think about the future of wealth management. They ask three big questions:

  • What will happen to advice?
  • What will happen to advisors?
  • What will wealth management firms do?

Insight engines — available today — can help provide answers to several of these questions. For context, I will explain insight engines briefly, covering their origins and what they do. Then, we can move on to explore how insight engines apply to wealth management today.

Insight engines: enterprise search evolved

Insight engines are enterprise search evolved. Gartner retired the category of enterprise search in 2016. In 2017, they unwrapped Insight Engines to reflect the profound changes in customer needs and technology capabilities.

Insight engines differ from enterprise search both in what they offer and the technologies used. In their inaugural 2018 report, Gartner highlights how Insight Engines are different:

Insight engines are distinguished by their capability to deliver insights in context to the right person, in the right place, at the right time.”

And they explain how the underlying technologies differ as well:

“These capabilities stem from the use of artificial intelligence (AI) technologies, specifically natural-language processing, graph-based data structures, and machine learning.”

Sinequa, a provider of insight engines to financial institutions, has been a leader in Gartner’s Magic Quadrant for Insight Engines since the category began.

Sinequa evolves enterprise search and insight engines even further. Coupling two decades of research in natural language processing with the latest deep learning approaches means users get immediate, relevant, auto-improving answers to their questions. Users have a complete view of customers or products or risks or contracts or deals all within a single view, created instantly from the most up-to-date content.

Advice

On advice, McKinsey makes three predictions:

  1. Hyper-personalized advice model built on data and continuous access.
  2. Bite-sized “fit-nance.” This means developing a granular ability to track customer investments, education, retirement, and broader financial wellness.
  3. Big tech will capture a large share of industry economics by providing core technology infrastructure.

The best investment advice comes from distilling mounds of data down into recommendations tailored to the client’s risk appetite and return objective. Sinequa’s Insight Engine delivers the investment insights required. The platform can search across all data sources including internal and external, cloud and on-premise, along with structured and unstructured data. Sinequa simplifies assessing financial wellness by providing a unified view of client assets and liabilities, irrespective of where the data is stored.

Advisors

For advisors, McKinsey thinks their working lives will change in three ways:

  1. Advisors remit expands to provide coaching on broader wealth and life issues. And McKinsey expects the industry to shed a fifth of its total advisors.
  2. The face of the advisor will become much more diverse, spanning increased numbers of women, minorities, and mid-career changers.
  3. User ratings will become ubiquitous, making advisor performance transparent.

Increasing advisor productivity remains a perennial challenge. Things will get worse as the current generation of wealth advisors retire. Routine work needs automating, so advisors can focus on adding value through relationship management and advice. Sinequa’s Insight Engine augments wealth advisors by saving their time foraging for data. And it applies decades of R&D in natural language processing, so advisors don’t have to read reams of documents.

Wealth management firms

McKinsey expects wealth management firms to have to make the most changes:

  1. Industry talent becomes more digital as wealth firms function as technology platforms.
  2. Several-at-scale firms will serve everyone while the rest will focus on providing differentiated service to ultra- and high-net-worth clients.
  3. Operational excellence will be required to protect margins from increasing transparency and falling fees.
  4. Integrated banking-wealth management ecosystems will emerge.

Insight engines can help wealth management survive and succeed in several ways:

  • Accelerate wealth firms build-out of their technology platforms with reduced risk using Sinequa’s multi-use-case Insights Engine.
  • Provide a unified view of clients to provide differentiated service to the extreme expectations of ultra- and high-net-worth clients.
  • Achieve operational excellence by applying All the AlphasHistorically, the wealth management industry has over-focused on the most transient of the alphas – the quest for above-market returns or investment alpha. However, this has resulted in overlooking the value hidden inside other internal functions, such as distribution and service. Delivering exceptional performance (alpha) in these functions can create competitive advantages more durable than investment alpha.
  • Find information and insight across any ecosystem, irrespective of the type, number, or location of ecosystem partners.

If you work at a wealth management firm and would like to learn more about how you can build tomorrow’s opportunities today, please attend one of our briefings.

Here’s how it works. You choose how much time you want to spend and where you want to spend it. We have an Executive Briefing Center on West 30th in New York City or in Paris or we can come to your office. We customize each briefing to your objectives and business challenges. We’ll start the briefing sharing our perspectives on insight engines in financial engines, learn more about your business, and discuss topics tailored to you. To arrange a briefing, please contact us at info@sinequa.com and add the subject line “Wealth Briefing.”

 

 

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How Biopharmaceutical Companies Can Fish Relevant Information From A Sea Of Data

This article originally appeared in Bio-IT World

Content and data in the biopharmaceutical industry are complex and growing at an exponential rate. Terabytes from research and development, testing, lab reports, and patients reside in sources such as databases, emails, scientific publications, and medical records. Information that could be crucial to research can be found in emails, videos, recorded patient interviews, and social media.

school-of-fish

Extracting usable information from what’s available represents a tremendous opportunity, but the sheer volume presents a challenge as well. Add to that challenge the size of biopharmaceutical companies, with tens of thousands of R&D experts often distributed around the world, and the plethora of regulations that the industry must adhere to—and it’s difficult to see how anyone could bring all of that content and data together to make sense of it.

Information instrumental to developing the next blockbuster drug might be hidden anywhere, buried in a multitude of silos throughout the organization.

Companies that leverage automation to sift through all their content and data, in all its complexity and volume, to find relevant information have an edge in researching and developing new drugs and conducting clinical trials.

This is simply not a task that can be tackled by humans alone—there is just too much to go through. And common keyword searches are not enough, as they won’t tell you that a paper is relevant if the search terms don’t appear in it, or if a video has the answer unless the keywords are in the metadata of the video.

Today, companies can get help from insight engines, which leverage a combination of sophisticated indexing, artificial intelligence, and natural language processing for linguistic and semantic analyses to identify what a text is about, look for synonyms and extract related concepts. Gartner notes that insight engines, “enable richer indexes, more complex queries, elaborated relevancy methods, and multiple touchpoints for the delivery of data (for machines) and information (for people).” A proper insight engine does this at speed, across languages, and in all kinds of media.

For biopharmaceuticals, this is particularly powerful, allowing them to correlate and share research in all forms over widely distributed research teams. Here are several ways biopharma companies can use insight engines to accelerate their research.

Find A Network Of Experts

Many companies struggle to create the best teams for new projects because expertise is hidden in large, geographically-distributed organizations with multiple divisions. A drug repositioning project might require a range of experts on related drugs, molecules, and their mechanisms of action, medical experts, geneticists, and biochemists. Identifying those experts within a vast organization can be challenging. But insight engines can analyze thousands of documents and other digital artifacts to see who has experience with relevant projects.

The technology can go further, identifying which experts’ work is connected. If they appear together in a document, interact within a forum, or even communicate significantly via email, an insight engine can see that connection and deduce that the work is related. Companies can then create an “expert graph” of people whose work intersects to build future teams.

This technique can extend beyond the borders of the company, helping to identify the most promising collaboration partners outside the company in a given field, based on publicly available data, such as trial reports, patent filings and reports from previous collaboration projects.

Generate R&D News Alerts

Biopharma companies can also use insight engines to watch for new developments in drug research and stay on top of the latest trends. These news alerts can go beyond typical media sources to include scientific publications, clinical trial reports, and patent filings.

This capability can be used on SharePoint, Documentum, or other sources within a large company to surface relevant information. An insight engine ensures the right information gets to the right people in the right context, and in a timely way.

Optimize Clinical Trials

Clinical trials that stretch over many years generate millions of datasets for every drug and study provide a treasure trove of data. Biostatisticians can ensure they get a comprehensive list of patients having certain diseases within trials on a drug, something nearly impossible with traditional methods.

They can also search and analyze across many drugs and studies, across content and data silos. Over time, this allows biopharmaceutical companies’ growing number of clinical trials to become a valuable asset that can be easily leveraged across a growing number of use cases.

All of these uses can lead to biopharma companies developing new drugs more quickly and getting them to market faster—necessary as these companies face tremendous pressure to innovate quickly and develop new promising drugs as patents for older drugs expire. With insight engines, they can make every part of the journey more efficient, from research, to clinical trials, to regulatory processes, presenting incredible opportunities for everyone in this field.

 

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Sinequa Named a Leader in the Gartner Magic Quadrant for Insight Engines: With Leadership Comes Responsibility

We at Sinequa are excited and humbled to be declared a Leader by Gartner in its 2018 Magic Quadrant for Insight Engines for the second consecutive time. A complimentary copy of the report can be accessed from the Sinequa website at http://go.sinequa.com/gartner-magic-quadrant-2018.html. (more…)

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Sinequa’s Insight Engine Helps Atos Differentiate by Providing Intelligent Digital Workplace Capabilities

A big congrats to our longtime strategic partner Atos who was named a leader in the Magic Quadrant for Managed Workplace Services (MWS). Gartner calls out Sinequa as a key supporting technology.

Atos-Sinequa-Gartner

Gartner’s Magic Quadrant for MWS, North America, recognizes leaders in enabling sourcing and vendor management leaders to select the right partner in the rapidly changing market, which focuses on using MWS to increase staff engagement, drive productivity and enable digital benefits.

As a recognized global leader in digital transformation, Atos provides an end-to-end solution to transform the employee experience. By combining skills tightly, from advisory to consulting and design thinking through to business and vertical solutions, including applications to the digital workplace platform, Atos has the skills in place to offer a complete solution to our joint customers to deliver an end-to-end workspace transformation. In terms of Cognitive technologies, Atos differentiates itself by integrating Sinequa’s insights engine. The partnership brings together Sinequa’s cognitive search & analytics platform and Atos’s business consulting and IT services expertise  to change the way people access applications, data and help, improving end user productivity and user experiences, whilst reducing cost and ensuring security and compliance.

We are excited to be working with a leading system integrator recognized for setting the tone in the digital workplace space and can’t wait to see where our partnership takes us in the future.

People at these digital workplaces need information, not just data. While information must often be comprehensive to be valuable –  like in a 360° view of a customer – it must also be relevant. People have no time to sift through tons of information to get to the insights that guide their actions. To help organizations sift through the abundance of information, data coverage must be total, and the delivery of insight must be intelligent and selective. This delivery of information must also match the expectations of today’s digital worker, who wants answers in seconds rather than hours or even minutes.

In this new generation of the digital workforce, there are certain tips that address the challenges of catering to this always connected society, including being proactive in delivering information and tackling unstructured data.

 

 

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Cognitive Search Brings the Power of AI to Enterprise Search

Forrester, one of the leading analyst firms, defines Cognitive Search in a recent report¹ as: The new generation of enterprise search that employs AI technologies such as natural language processing and machine learning to ingest, understand, organize, and query digital content from multiple data sources. Here is a shorter version, easy to memorize: Cognitive Search = Search + NLP + AI/ML
Of course, “search” in this equation is not the old keyword search but high-performance search integrating different kinds of analytics. Natural Language Processing (NLP) is not just statistical treatment of languages but comprises deep linguistic and semantic analysis. And AI is not just “sprinkled” on an old search framework but part of an integrated, scalable, end-to-end architecture.

AI Needs Data, Lots of Data
For AI and ML algorithms to work well, they need to be fed with as much data you can get at. A cognitive search platform must access the vast majority of data sources of an enterprise: internal and external data of all types, data on premises and in the cloud. Hence the system must be highly scalable.

Continuous Enrichment
Cognitive Search uses NLP and machine learning to accumulate knowledge about structured and unstructured data and about user preferences and behavior. That is how users get ever more relevant information in their work context. To accumulate knowledge, a cognitive search platform needs a repository for this knowledge. We call that a “Logical Data Warehouse” (LDW).

The Strength of Combination
To produce the best possible results, the different analytical methods must be combined, not just executed in isolation of each other. For example, machine learning algorithms deliver much better results much faster if they work on textual data for which linguistic and semantic analyses have already extracted concepts and relationships between concepts.

Whitepaper-kmworld-07-2017Get your copy of the full paper here and learn more about current use cases of cognitive search and AI at large information-driven companies.

(1) Forrester Wave: Cognitive Search & Knowledge Discovery Solutions, Q2 2017
Read the full report on https://www.sinequa.com/forrester-wave-2017/

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