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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Cracked Conversations: What to Do When Chatbots Aren’t Enough

Enterprise Search to Compliment Your Chatbot ExperienceBy: Robert Smith, Sales Engineer and John Finneran, Product Marketing

Conversational AI, or chatbot, vendors, are everywhere, deafening customers with the promise of AI-Powered solutions for their customer service needs.  According to Capterra, 158 companies currently offer chatbot software.  In Forrester’s evaluation of the emerging market for conversational AI for customer service for Q2 2019, the analyst firm identified the 14 most significant providers in the category – [24]7.ai, Avaamo, Cognigy, eGain, Indenta Technologies, Interactions, IPsoft, Kore.ai, LogMeIn, Nuance Communications, Omilia, Saleforce and Verint.

This makes understanding what works best to improve customer experience hard.

Chatbots work best guiding users along straightforward, well-defined conversational paths.  If a customer asks new, unpredicted questions the typical chatbot gets confused. More complex questions require complementary solutions.  

Sinequa offers one such complementary solution – Enterprise Search that can work with chatbots to help customers and employees find what they need.

We have spoken with a number of companies ranging from those considering the technology, to building prototypes, to deploying chatbots in customer-facing applications.

Several of the concerns about the value produced by chatbot deployments

  • Slow Conversation speeds
  • Conversation path-sets grow larger and longer
  • Low accuracy because the chatbot was unable to answer and was unable to maintain the chat
  • High development effort with too many expert hours spent conceiving, designing, deploying, and maintaining those conversational paths.

Some Reasons Why?

Chatbots work best when guiding a well-defined type of user through a set of preconceived conversational paths.

The typical chatbot’s tooling provides a graphical interface, and some testing capabilities; conceiving, designing, deploying, and maintaining those conversational paths will be up to you.

  • When you consider how many paths a user might take, multiplied by the number of user types, it can grow to an astonishing amount of work.
  • When chatbots have a lot of this work to do, they tend to slow down compromising, the chat experience
  • Most requests for information are ‘ad-hoc’ and therefore not well-suited for a pre-planned and pre-built conversation flow.

When Do Chatbots Make Sense?

An example is a chatbot at your local bank

  • They have a limited set of offerings for users to choose from
    • E.g. checking, savings, mortgages, lines of credit
  • Those offerings have a limited number of actions
    • Checking deposit, transfer, bill pay, balance inquiry
  • The site is often for reference, not as much for execution
    • To actually open up an account type, you typically have to apply in-person

If you can’t narrow the scope to specific user-types and paths like these, then the outcome of multi-step “chats” is by definition, less predictable, leading to a higher failure rate.

This also makes it difficult for some chatbots to get a PTO (Permit to Operate), because companies have not let applications go into production that couldn’t guarantee outcomes.  This is to avoid “Rogue AI” situations, among other things.

Addressing the Challenge

Enterprise Search, like Sinequa’s, leverages natural language processing (NLP) to get users the most relevant content, without the chatbot’s requirement that the conversational path be designed, built and maintained.

Where chatbot interactions are sometimes helpful, that chatbot can connect to enterprise search; when the chatbot gets a user’s request for information, the chatbot can refine and forward the request to the underlying Sinequa search, then channel the results back to the user’s conversation.

In Short

By using chatbots and a powerful enterprise search platform together for the jobs they were designed for, you can deliver profitable and productive solutions that enhance both customer and employee experiences.

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Sinequa Releases Next Generation User Experience Framework

Sinequa invested significantly in the latest release of its product to evolve the way the platform presents insights to end users, automatically adapting to each user’s device. This required a complete overhaul of the user interface framework to a responsive design based on Angular 7 that opens up application development on the Sinequa platform to a much wider audience.

Angular Framework

“We are very grateful for the practical insights provided by our customer base and partner network, we could not have done it without them,” said Philippe Motet, vice president of engineering at Sinequa . “This next generation framework will open up the floodgates for application developers worldwide to benefit from the power of the Sinequa platform quickly and without a hefty learning curve to enhance the user experience.“

Based on the newly released responsive design framework, Sinequa tackled the revamping of its platform user interface with the help of a strategic UI design consultancy. This initiative resulted in a simplified and streamlined end user experience powered by redesigned UI components purposefully calibrated to align with end user needs.

This represents the most visible evolution of the Sinequa platform and adds to a growing list of innovations released by the company. Other recent innovations released by Sinequa include numerous enhancements to its linguistic processing engine as well as deep integration with market leading platforms like Spark for supervised machine learning and Tensorflow for unsupervised deep learning.

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Accelerating GDPR Compliance with an Information-Driven Approach

With GDPR in full effect, Ovum has published a new white paper featuring Sinequa and outlining how cognitive search can be a compliance accelerator.  The full white paper can be found at http://go.sinequa.com/white-paper-gdpr-ovum-2018.html.

ovum-gdprThe white paper, written by Ovum senior analyst Paige Bartley, outlines the challenges of aligning existing business objectives with GDPR compliance and how companies can accelerate GDPR compliance with an information-driven approach.

There is a new precedent with GDPR for enterprise control of data of all kinds, both structured and unstructured. Gaining better control of data is a deceptively simple concept, but requires numerous capabilities, such as the ability to search for data across silos and granularly manage who has access to what. Sophisticated search and analytics capabilities, spanning organizational silos, are key to both facilitating compliance and driving informational value.

According to the white paper: “GDPR compliance requirements should not be thought of as an antithetical force against enterprise initiatives to leverage and analyze data. The more the enterprise can align its compliance obligations with existing business objectives – such as the demand to provide excellent customer service via a 360° understanding of customer desires – the more it can benefit from the perceived ‘burden’ of GDPR compliance. Enterprise-wide search is central to these capabilities and will help the enterprise gain a centralized view of subjects and topics in the increasingly distributed IT ecosystem.”

As an example of this concept, the white paper highlights Sinequa’s platform deployment at Stibbe, a global, full-service law firm with an internationally oriented commercial practice. Stibbe implemented Sinequa’s Cognitive Search & Analytics Platform to provide secure, unified access to millions of documents and legal matters. The result was a holistic view of the firm’s informational landscape, with the ability to quickly retrieve all material related to individual topics, cases or clients.

This Ovum white paper gets at the heart of Sinequa’s approach to GDPR and data analytics. By taking control of data and unleashing it as a strategic tool, organizations can become information-driven in a way that meets compliance requirements while accelerating innovation and creating a key competitive advantage.

The full white paper can be found at http://go.sinequa.com/white-paper-gdpr-ovum-2018.html

 

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Sinequa Helps Box Customers To Be Information-Driven

noiseMany customers that use Box for cloud content management are typically large, geographically distributed organizations. The four scenarios below describe common ways that Sinequa helps these customers leverage their enterprise information to become information-driven.

Increase the Signal, Decrease the Noise
Customers who have migrated even a portion of their enterprise content to Box have made a significant step.  Workers in their organization can no doubt share and collaborate more easily than ever before; they no doubt have reduced email overhead; and they are probably working the way they want to given all of the friendly integrations with Box, including Outlook, Office365, Google Docs and the like.   However, being in the cloud does not automatically mean the valuable “signals” in your data rise above the “noise”.  Messy data migrated to the cloud is still messy data.  Sinequa helps workers quickly narrow in on the information and insights necessary to do their job effectively and with confidence.  By analyzing the content and enriching it using natural language processing and machine learning algorithms, Box users can quickly find the information and insights they need to be effective and responsive.

Connect Data

connect-data

Many Box customers run their business with other enterprise applications and information repositories, all of which contain data and content related to the information
stored in Box.  Sinequa brings advanced analytics and cognitive techniques to “connect” the data and bring context across all of the various enterprise sources, whether they be in the cloud or on premise.  By connecting the data, knowledge workers can better navigate and see how the data and connect fit together along topical lines, regardless of how many repositories make up the enterprise information landscape.

Identify Knowledge & Expertise

Screen Shot 2017-10-13 at 2.40.37 PMAs previously mentioned, many Box customers are large (or even very large) geographically distributed organizations with expertise in a wide variety of subject matter areas.  In these organizations, specific experts are difficult to identify given the size and distributed nature of the organization.  This is a modern problem that requires a modern solution.  As users store content and collaborate within Box, Sinequa’s advanced cognitive capabilities analyze that content to determine not only the areas of expertise across the organization but who the specific experts are and surfaces that information to end users.  This connects people across geographic and departmental boundaries, accelerating innovation and elevating the performance of the overall organization.

Leverage 360º Views

Screen Shot 2017-10-13 at 2.42.23 PM

Think of all the “entities” that are critical to Box customers running their business.  These business entities include customers, either specific individuals (B2C) or accounts (B2B), products, parts, drugs, diseases, financial securities, regulations, etc.  Having all of the enterprise data virtually connected by Sinequa makes it possibly to provide a unified “360º View” of these various entities to bring all of the right information to the right person at the right time.
As you can see, leveraging Sinequa to contextualize the information within Box and other enterprise repositories not only boosts productivity and keeps knowledge workers in the flow but has repeatedly proven to enhance customer service, improve regulatory compliance and increase revenue within different areas of the business.  Achieving these benefits positively impacts the bottom line and serves as validation that an organization has become truly information-driven.
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