Finance & Banking: Collecting High Value from Cognitive Search and Content Analytics

In today’s rapidly changing technology climate, financial services customers expect their banks, insurance companies and asset management providers to know them. It’s expected that providers know about recent transactions, account details, and even anticipate future needs. But this can be challenging with the numerous silos of content in which customer data resides. With a 360° view of the customer through cognitive search and analytics, financial services organizations can deliver the customer experience that provides more value, drives increased sales and meets rapidly evolving customer expectations.

As an example, Crédit Agricole, one of the largest banks in the world, has launched an ambitious project to deliver a new digital workplace, offering a 360° view of customers to its representatives as well as to the customers themselves. The bank’s more than 60,000 internal users will be able to know the exact situation of the customer in front of them, to find the most relevant offerings for the customer and the corresponding procedures. The customers connecting to the bank’s online service also find themselves in a similar “work place” that allows them to know the current status of all their business with the bank including accounts, contracts, records of transactions, share portfolio and share prices, banking charges, additional services, and more.

This comprehensive “work place” is created through inclusive enterprise search and analytics of all of the bank’s data sources. From CRM and account transaction applications to external sources such as stock exchange data, corporate websites, financial and trading news-feeds, the bank can provide a complete customer picture from which to deliver robust service, new offerings and build increased customer satisfaction.

Cognitive Search and Analytics platforms index all the structured and unstructured data sources and create a semantically enriched index, optimized for performance in dealing with user search queries.  In fact, some search and analytics solutions even offer as many as 150 smart connectors, ‘out of the box,’ that can seamlessly connect multiple sources of data.  These companies integrate your company’s and industry specific dictionaries allowing the information to be integrated and indexed, putting your specific knowledge ‘under the hood’ of one platform – making it an intelligent partner for workers looking for business insight at their digital workplace.

See here how Sinequa’s Cognitive & Analytics platform brings business value to Finance and Banking organizations.

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Uncovering Business Insights Through Cognitive Search

Uncovering-Business-Insights-Through-Cognitive-Search-Sinequa

Big Data. It’s among the most pressing challenges — and opportunities — for today’s solution providers. Enterprise data, be it structured in databases and enterprise applications or unstructured textual data from documents (including contracts, letters, emails, news-feeds, websites, and more) or videos and images, contains a wealth of content that, if searched and analyzed with cognitive intelligence, can deliver valuable insights for the customers you serve.

It’s common today to have numerous silos, both on premise and in the cloud, of content in which critical data resides. From customer records and contracts to financial data and emails, data silos often take many different shapes and forms without the ability to “talk” to one another. If only a 360 degree view of this data were available at the employees’ finger tips. This could provide deeper customer insight, increased sales opportunities, and greater customer loyalty with the ability to meet rapidly evolving customer expectations.

With cognitive search and analytics, this goal can be achieved. Leveraging Machine Learning algorithms and advanced natural language processing (NLP), cognitive search and analytics solutions enable customers to embark on ambitious Big Data projects with the opportunity to extract relevant information from the volumes of content they retain.

In fact, some search and analytics solutions even offer as many as 150 smart connectors, out of the box that can seamlessly connect to multiple sources of data. This works to integrate your customers’ industry specific dictionaries allowing the information to be indexed, putting their specific knowledge under the hood of one platform — making it an intelligent partner for anyone searching for relevant information for his/her subject.

To efficiently leverage Big Data for your customers, consider an advanced search and analytics platform that delivers these five critical elements.

  1. Cognitive search with a combination of indexing, natural language processing and machine learning. For a search and analytics solution to be effective, it needs to understand the natural language as it’s spoken across ever major language. This will help to deal with unstructured content such as email and document files. It should also leverage machine learning algorithms so that it can learn as it progresses, delivering more value and insight with each new volume it analyzes. This is what one analyst firm defines as Cognitive Search which allows organizations to create an increasingly relevant corpus of knowledge from all sources of unstructured and structured data that use naturalistic or concealed query interfaces to deliver knowledge to people via text, speech, visualizations, and/or sensory feedback.
  2. Extensive connections for comprehensive indexing. To make the most of the multiple silos of data throughout the organization, a search and analytics solution needs to have a wide range of connectors so that it can support every type of data, making it easily ingested into the platform so that it’s included in a comprehensive analysis. Building connectors before starting projects will delay value extraction and make projects more expensive. From databases and enterprise applications including CRM and ERP systems such as SAP, to big data Hadoop environments, cloud applications like Office 365, GoogleApps and Salesforce, and cloud storage such as Box and Microsoft OneDrive, having a connector for every vital application in the business will ensure that the resulting insights deliver a complete view into the business.
  3. Support for the structured and unstructured. When analyzing business data, it’s critical to include unstructured data, such as email and document files, as well as structured content, like the data included in databases. Only when both are included in an analysis can true insights be revealed. Since so much valuable data is embedded in unstructured files, evaluating these contents can produce truly insightful information into the business that can’t otherwise be recognized by evaluating structured forms of content.
  4. Extensive security and access control. Today’s data is not only a critical asset, it’s also private and stringently regulated. Any solution that touches regulated data must follow strict security and compliance guidelines, ensuring that policy controls are in place. Be sure to select a search and analytics platform that supports stringent access controls, including user authentication, cross-domain security and secure communications, to assure that compliance practices are followed.
  5. Agility to support hybrid infrastructure. The cloud is quickly changing everything. When large data sets are in play, it’s very likely that much of that data is being retained in cloud-based environments. Whether retained in public cloud solutions, such as Amazon Web Services (AWS), or private cloud architectures, data still must be accessed and integrated into a comprehensive enterprise search and analytics solution to be part of a successful solution for true business insights. Here it’s critical to select a solution that will not only support any combination of a private and public cloud infrastructure as well as on-premises architectures with a hybrid approach to data analysis that will also support hundreds of millions of documents and billions of database records. This will ensure that regardless of how large the environment becomes, and wherever data may reside, it can become part of a comprehensive analysis for true and accurate results.

Big data presents a wealth of opportunity for you and your customers. By taking a holistic approach to cognitive search and analytics so that every silo of data is included in an enterprise search activity, the insights can be exceptionally revealing. These results can not only increase customer opportunities, grow sales and improve overall organizational productivity, they’ll also help you build the customer loyalty that will pay off for years to come.

 

 

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5 Ways Finance & Insurance Organizations Take Advantage of Cognitive Search and Analytics

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Leading banks, financial institutions, and insurance companies are known to be data-intensive organizations and depend more than ever on data to make well founded decisions. They also rely on data to extract insights on customers that can result in increasing revenue streams. In order to address these challenges they need to be agile, innovative and responsive to evolving customer needs. Find here five ways Financial organizations leverage their big data using cognitive search and powerful analytics:

  1. Get Actionable insight from the most diverse data sources: the objective is to analyze, structure and categorize all available data to get intuitive and unified information access across all internal and external data sources, including customer contracts, insurance claims, payment history, email communications, CRM data, company policies and processes and more. Employees must be able to access relevant information without having to know where data is stored, in which format or how to access it.
  2. Obtain instant 360° views of customers, portfolios, investment targets, contracts, financial performance, and any other subject linked to the business of an organization. People can do so across all business units – from banking to insurance, leasing, property management, asset management, and beyond. Only an efficient “insight engine” – as some leading analysts call cognitive search and analytics platforms – can provide rapid 360° views to users without the need to change existing applications.
  3. Detect fraudulent activities & prevent money laundering: banks and insurance companies face the daunting task to accurately and rapidly identify fraud by analyzing Big Data volumes. To face this challenge, a cognitive insight platform enables the detection of “unusual” data patterns by predictive machine learning algorithms and the mapping of relationships between people, bank accounts, credit card numbers, financial transactions, and many other data types. To uncover patterns in behavior, analysts use a combination of interactive charts, timeline analyses, tables and relationship maps.
  4. Reduce customer churn: the combination of cognitive search and powerful analytics help organizations improve customer retention. Here, Natural Language Processing with text mining agents plays a major role in detecting relevant information in customers’ data and behavior, for example by analyzing information requests and navigation patterns on the company’s website. Predictive Analysis also plays a role in reducing churn rates. For example, machine learning algorithms help detecting patterns and trends in customers’ transactions which can identify them as “high-risk” potential defectors.  Companies can propose tempting offers to potential churners that prove usually quite effective in retaining them. This also reflects in staggering yearly ROI figures, up to tens of millions of dollars.
  5. Recommend up-sell and cross-sell offers: Once customer data is collected and analyzed across all available channels, additional functionalities can be added with marginal effort. Machine learning algorithms, such as “collective filtering and recommendation”, can then be used to optimize marketing campaigns, improve up-selling and cross-selling. Indeed, on top of the 360° view of customers, we can use machine learning algorithms to recommend products and/or services that are relevant to customers, based on deep analytics of contents and customers’ behavior data.

In the fast-evolving world of Finance & Insurance, it becomes increasingly important for these organizations to capture, process and analyze massive amounts of structured and unstructured to make better business decisions while better serving their customers. A Cognitive Search & Analytics platform that delivers superior agility, flexibility and scalability and turns data into business insight can bring significant value.

Interested to learn more about this platform for your organization? We’d love to help.

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Join Sinequa at Bio-IT World Conference & Expo 2016 (Booth #421)

Sinequa will present and exhibit at Bio IT World Conference & Expo that will take place on April 5-7 at the Seaport World Trade Center in Boston, USA.

Sinequa For Life Sciences

We invite you to stop by the Sinequa booth #421 to discuss innovative use cases of our solution for the Pharma industry – Sinequa For Life Sciences - and see how our customers raised their competitiveness by implementing our Big Data Search and Analytics solution across the most diverse data silos.

  

Also, make sure to book your agenda and attend our presentation in the Bioinformatics Track #5:

Wednesday, April 6, at 2:55-3:10 PM

“Increasing the Competitiveness of Pharma Companies:
Real Time Search and Analytics Across Structured & Unstructured Data”

Speaker: Xavier Pornain, Vice President of WW Sales & Alliances

Book your agenda

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Finding the Information ‘Needle in a Haystack’

Below is a contributed article from our VP Marketing, Laurent Fanichet (@fanichet). The original version is available on Biosciencetechnology.com.

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Sinequa - Finding the Information ‘Needle in a Haystack’

Digging through volumes of pharmaceutical data in any form, be that of lab reports, experimental results, clinical trial reports, scientific publications, patent filings, to even emails is a gargantuan task.  The data may deal with diseases, genes, drugs, active agents and mechanisms of action and can be textual, structured data like molecule structures, formulae, SAS data sets from clinical trials, curves, diagrams, and more.  When put together, all of this information can be retained in hundreds of millions of documents and billions of database records.

Compounding this volume of information are the billions of database records from internal and external trade sources that may be related to a life sciences project.  World-renowned pharmaceutical and chemical companies, such as AstraZeneca and Biogen, rely on search and analytic technology to solve real world problems by providing a single point of access to information extracted from all these data sources.  Search and Analytics solutions specialize in finding that data ‘needle in a haystack.’

To find that ‘needle,’ advanced organizations turn to the power of Search Based Applications (SBA).  Imagine that your company has an idea, but the data required to obtain meaningful results is spread across multiple business units or even enterprises in different formats.  What if you could quickly develop an application that could bring all the data together and allow you to create search queries to find the data that you require? And more importantly, what if the application could be built in a month’s time using highly advanced natural language processing that allows you to make sense of the complex information in scientific publications or clinical trial reports using artificial intelligence and machine learning from Spark; statistical analysis of structured data; and above all, combined statistical and linguistic/semantic analysis?

Advanced search and analytics platforms index all the structured and unstructured data sources and create a semantically enriched index, optimized for performance in dealing with user search queries.  In fact, some search and analytics solutions even offer as many as 140 smart connectors, ‘out of the box,’ that can seamlessly connect multiple sources of data.These companies integrate your company’s and industry specific dictionaries and ontologies allowing the information to be integrated and indexed, putting your specific knowledge ‘under the hood’ of one platform – making it an intelligent partner for anyone searching for relevant information for his/her subject.

The Pharma industry is starting to efficiently leverage SBAs in multiple ways. A major benefit of SBAs is that it allows companies to find subject experts. A company can quickly get a dynamically calculated list of people with their respective domains of expertise related to your question/subject. The results correspond to an ‘Expert Graph’ calculated from the ‘footprint’ experts leave in texts and data.

To make the most of your volumes of data, look for search and analytics solutions that will also allow you to build on your network of experts outside of your own internal resources.  For example, you can extend your search for experts on a particular subject by ploughing through massive amounts of data, in particular scientific publications, publicly available trial reports, patent filings, and reports from previous collaboration projects, in order to identify the best available experts – “Key Opinion Leaders” – and the organizations they belong to.

It’s also valuable to use a search and analytics solution to access the latest scientific information in your field with automatic alerts.  This is extremely valuable because it allows you to discover research trends in your field and potentially monitor the competition.  Such SBAs may easily cover as many as 110 million documents: all accessible external data sources including publications, Embase, Medline, Scopus, clinical trial reports and your company’s internal data sources via SharePoint, Documentum, etc.

Clinical trials going over many years generate millions of SAS datasets and billions of rows per drugs and studies.  Over time, Biostatisticians face tremendous challenges performing their analysis with the right datasets.  It is difficult to get a comprehensive list of patients having certain diseases within trials on a drug; ensuring completeness of results is nearly impossible with traditional tools and processes.

With powerful search and analytics indexing technology, scientists are able to search complex content with very precise criteria.  They can retrieve subjects that have shown certain diseases by specifying exact or fuzzy values on the AELLT variable for instance.  The scientists can then filter based on additional criteria like age and can combine about 900 CDISC variables and add any specific variables you may have.  They can search datasets based on the structure and metadata. Possibly even more important, the scientists can even search across many drugs and studies, merging current data silos.  In total, pharmaceutical company scientists are transforming their growing clinical trials data to a valuable asset that can be searched in real time.

Taken all together, an advanced search and analytics platform is able to leverage data indexing and analytics technology and enable organizations to create their own Search Based Applications.  In doing so, companies are able to:

  1. Accelerate research and time-to-market of drugs
  2. Quickly find experts on a particular subject
  3. Find key opinion leaders and R&D cooperation partners
  4. Push latest news on a subject to partners
  5. Monitor publications on particular subjects

The result? That ‘Needle in the Haystack’ just became much easier to find!

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