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

Sinequa-Cognitive-Search-Analytics-Finance-Insurance

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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Digital Workplace: Digitized Chaos or Information at your Fingertips?

Digital Workplace

You have a digital Workplace, of course. Does it fulfil all the expectations you had when you went “all digital”? Or is getting at the right information still too complex, too cumbersome and time-consuming? Companies often need specialists to extract information for each specific work context. That is not agileand it’s in total contradiction with the modern digital workplace principles promising “information self-services”.

In decent Digital Workplaces, you find information, not data! And this information must be comprehensive and relevant, and delivered instantly, since in the era of digital business models, there is no time to sift through tons of data when you need information. At best, information is delivered proactively, in order to gain time, increase productivity and improve decision making.

Now, many of you may be wondering: “how to create value from data in increasingly digitalized businesses?”; “how to extract relevant information from big and diverse data and then, deliver precise and relevant information to each and every person at the right time?” This might seem like an elusive goal as we create more data than ever in digitalized workplaces, potentially increasing chaos every day.

To overcome these challenges, we need to simplify the digital workplace for users. This requires high performance systems of data retrieval, analytics and information delivery.

In the past, organizations have installed data warehouses and search engines to help people find relevant data. Many of these never delivered on the expectations – and the needs – of users and organizations. They were lacking in analytical power and in performance when faced with large and growing amounts of heterogeneous data and with the need to combine analysis of structured and unstructured data, including most prominently natural language processing (NLP) for a while range of languages.

The new generation of enterprise search platforms have evolved into whatGartner calls “Insight Engines”.

According to this leading analyst firm, 25% of large organizations will have an explicit strategy to make their corporate computing environment similar to a consumer computing experience by 2018; 46% have a digital workplace initiative underway and 4% have appointed a Digital Workplace leader.

As usual, the bright new digital future cannot be “bought” with a new piece of technology. It requires a change of mind-set and a change in corporate culture.  Nevertheless, be aware that the digital workplace technology you select can either facilitate or impede adoption and change of culture.

Gartner specifies these Digital Workplace Principles : Contribution/ Enthusiasm; Digital Dexterity; Autonomy

#1 Contribution/ Enthusiasm: By promoting employee engagement, digital workplaces create a workforce that makes discretionary contributions to business effectiveness

#2 Digital Dexterity: Creating a “consumer-like computing experience” to enable teams to be more effective

#3 Autonomy:  Exploiting emerging smart technologies and people-centric design to support dynamic non-routine work

To step into the era of the reimagined Digital workplace you need the “Insight Engine” to increase your employees’ effectiveness and productivity, to help them better serve their customers while enjoying their work environment.

Sinequa has been mentioned next to Apple, IBM and the likes in the latest Gartner’s Hype Cycle Content Management/Digital Workplace 2015 Reports – for proactive search capabilities that are mandatory for a transition to Digital Workplaces.

Take a look at our presentation in the Gartner Digital Workplace Summit last September in London:

 “The Re-Imagined Digital Workplace: Where is the Beef?

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4 Ways Big Data Analytics Transform Intelligence Data into Actionable Insights

Intelligence and law enforcement agencies experience an enormous pressure to identify threats across multiple data sources. These Defense and Security organizations require real-time information at their fingertips for quick analysis and decision making.

Big Data Search and Analytics for Defense and Security

Big Data Search and Analytics for Defense and Security

Here are 4 ways Big Data Analytics can transform intelligence data into actionable insights:

  • Monitoring of Social Media interactions

Intelligence agencies must anticipate any kind of cybercrime and attacks. Social media monitoring enables them to collect and analyze relevant and targeted information relating to counter-terrorism and criminal networks. Reacting at the right time is a major challenge for these organizations that use OSINT (Open-Source Intelligence) to find, select and acquire information from various sources online (social networks, forums, blogs, websites, videos etc.) in order to get real-time insight on potential threats, generate reports and prevent any kind of attacks. In response to this challenge, intelligence agencies must invest in a cutting-edge technology that brings together data search and collection across multiple online sources and a deep content analytics of unstructured textual data that are flooding the web.

  • Detection of money laundering, fraud & terrorist financing

Money laundering is a key component of most organized crime. Terrorist networks continue to be funded through money laundering schemes that need to be identified. A powerful Big Data Search and Analytics platform enables agents to pinpoint suspect money transfers, accounts and networks of individuals involved in sophisticated money laundering schemes through a highly dynamic approach to relationship mapping.

  • Identify and correlate threats & cyberattacks

Investigators face the daunting task to accurately identify fraud and cyberattacks across big data volumes within shrinking windows of time. To prevent threats and cyberattacks before they happen, intelligence agencies must be able to deliver dynamic relationship mapping to connect people, bank accounts, credit card numbers, financial transactions, and many other data types. They need a scalable platform based on advanced Search and Natural Language Processing capabilities. Analysts uncover patterns in behavior using a combination of interactive charts, timeline analyses, tables and relationship maps.

  • Solve crime cases with powerful search capabilities

Law enforcement professionals need effective crime analysis tools to easily reveal networks of criminal activity. The sophistication of criminal behavior has increased across virtually all areas, including cybercrime, identity theft, gang activity, fraud and narcotics. These tools must provide the ability to search and analyze a wide range of sources of both structured and unstructured data to gain meaningful insights using connections between people, phone calls, license plates, addresses, properties or other forms of data.

To learn more – please download the brochure “Sinequa for Defense and Security”.

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