High-volume administrative tasks are a feature of every business. We helped a global bank develop a machine learning algorithm to help with the task of reviewing over 100,000 ‘sanctions alerts’ every day.
Read this story to find out:
• what other benefits AI can deliver beyond cost savings
• how AI tools achieve greater accuracy than human reviewers
• what it takes to apply AI and Machine Learning successfully in a regulated sector.
Connected Intelligence in Insurance
Insurance as we know it is transforming dramatically, thanks to capabilities brought about by new technologies such as machine learning and artificial intelligence (AI).
Download this IDC Analyst Infobrief to learn about how the new breed of insurers are becoming more personalized, more predictive, and more real-time than ever.
What you will learn:
The insurance industry's global digital trends, supported by data and analysis
What capabilities will make the insurers of the future become disruptors in their industry
Notable leaders based on IDC Financial Insights research and their respective use cases
Essential guidance from IDC
Over the past decade there has been a major transformation in the manufacturing industry. Data has enabled a paradigm shift, with real-time IoT sensor data and machine learning algorithms delivering new insights for process and product optimization.
Smart Manufacturing, also known as Industry 4.0, has laid the groundwork for the next industrial revolution. Using a smart factory system, all relevant data is aggregated, analyzed, and acted upon.
We call this Manufacturing Intelligence, which gives decision-makers a competitive edge to:
Digitize the business
Survive digital disruption
Watch this webinar to understand use cases and their underlying technology that helped our customers become smart manufacturers.
The Insurance industry continues to undergo significant transformation, with
new technologies, business models, and competitors entering the market at an
increasing rate. To be successful in attracting and retaining the most valuable
customers, insurance companies must innovate and increase the speed at which
they respond to customer demands. Traditionally, the insurance software market
was dominated by a handful of specialist vendors with products that were initially
expensive, difficult to deploy, costly to maintain, and did not provide the speed
needed for today’s market.
Now there has been a shift away from these “black box” applications to platforms
that allow insurers to make their algorithmic IP available to business users, allowing
much faster response to business demands. The algorithmic platform approach also
comes at a fraction of the cost of black box solutions, while delivering advanced
analytical techniques like Machine Learning and Artificial Intelligence (AI).
The idea of load balancing is well defined in the IT world: A network device accepts traffic on behalf ofa group of servers, and distributes that traffic according to load balancing algorithms and the availabilityof the services that the servers provide. From network administrators to server administrators to applicationdevelopers, this is a generally well understood concept.
While interest in Machine Learning/Artificial Intelligence/ (ML/AI) has never been higher, the number of companies deploying it is only a subset, and successful implementations a smaller proportion still. The problem isn’t the technology; that part is working great. But the mere presence and provision of tools, algorithms, and frameworks aren’t enough. What’s missing is the attitude, appreciation, and approach necessary to drive adoption and working solutions.
To learn more, join us for this free 1-hour webinar from GigaOm Research. The webinar features GigaOm analyst Andrew Brust and panelists Jen Stirrup, Lillian Pierson, and special guest from Cloudera Fast Forward Labs, Alice Albrecht. Our panel members are seasoned veterans in the database and analytics consulting world, each with a track record of successful implementations. They’ll explain how to go beyond the fascination phase of new technology towards the battened down methodologies necessary to build bulletproof solutions th
Published By: BetterUp
Published Date: Mar 06, 2019
As HR investments become more data-driven, coaching must deliver on its ROI evidence gap. At BetterUp, we’ve created a model that can effectively measure the impact of coaching on the bottom line. Learn how coaching ROI has been measured in the past, and why these methods are flawed. Discover which inputs are factored into our ROI algorithm and you can map the ROI of coaching to tangible business results.
Artificial Intelligence (AI) has already begun to improve targeting, segmentation, media buying and planning in the advertising industry. AI algorithms can extract complex patterns from vast numbers of data points, and in so doing, are able to self-correct and learn patterns. The revenue potential that improved personalization, segmentation and targeting that AI provides to marketers is huge.
At HERE Technologies, we are placing AI and machine learning at the center of our products and services. We see the opportunity in automated machine learning to enrich the targeting and effectiveness of mobile advertising campaigns in real time. But the outcome of implementing such technology depends on the quality of data being fed into it from the outset. AI wouldn’t be as helpful if it’s being used alongside questionable location data or audience data.
HERE’s location data provides a strong thread that can be woven throughout every stage of the media buying process, offering more context and
The NSA’s Information Assurance Directorate left many people scratching their heads in the winter
of 2015. The directive instructed those that follow its guidelines to postpone moving from RSA
cryptography to elliptic curve cryptography (ECC) if they hadn’t already done so.
“For those partners and vendors that have not yet made the transition to Suite B elliptic curve
algorithms, we recommend not making a significant expenditure to do so at this point but instead to
prepare for the upcoming quantum-resistant algorithm transition.”
The timing of the announcement was curious. Many in the crypto community wondered if there had been
a quantum computing breakthrough significant enough to warrant the NSA’s concern. A likely candidate
for such a breakthrough came from the University of New South Wales, Australia, where researchers
announced that they’d achieved quantum effects in silicon, which would be a massive jump forward for
Komprimierungsalgorithmen sorgen dafür, dass weniger Bit benötigt werden, um einen bestimmten Datensatz zu repräsentieren. Je höher das Komprimierungsverhältnis, desto mehr Speicherplatz wird durch dieses spezielle Datenreduzierungsverfahren eingespart. Während unseres OLTP-Tests erreichte das Unity-Array bei den Datenbank-Volumes ein Komprimierungsverhältnis von 3,2:1, während das 3PAR-Array im Schnitt nur ein Verhältnis von 1,3:1 erreichte. In unserem Data Mart-Ladetest erzielte das 3PAR bei den Datenbank-Volumes ein Verhältnis von 1,4:1, das Unity-Array nur 1,3:1.
The transition to autonomous is all around. Its capability for problem-solving has never been seen before. Its potential for creating business value from algorithms and data makes it the next big frontier for business leaders. Two industry experts have discussed Oracle Autonomous Data Warehouse Cloudand what it can help organisations achieve. Talking about innovation,
security and efficiency, they put the casefor an autonomous future.
Published By: Dell EMC
Published Date: Feb 14, 2019
Isilon scale-out NAS delivers the analytics performance and extreme concurrency at scale to feed the most data hungry analytic algorithms. Access this overview from Dell and Intel® to learn more.
Intel Inside®. Powerful Productivity Outside.
Published By: FusionOps
Published Date: Jun 15, 2016
The supply chain generates huge volumes of data captured in ERP, CRM, demand planning and other systems. Download this whitepaper to learn how FusionOps Machine Learning can provide companies with a more accurate, granular understanding of their business by harmonizing these disparate data sources in the cloud, and applying machine learning algorithms.
In some cases, adopting cloud IoT platform may make more sense where required processes, communication costs and cloud costs meet sufficient total cost of ownership against deploying MDC. Additionally, in situations that an end-user organization already has a secure room or a modular data center solution where infrastructure can be housed and/or the amount of infrastructure involved may be too small to benefit from power/cooling advantages of being housed in an MDC, the organization may not see a need for an MDC. An MDC is nothing more than a smaller form of a modular data center, and a number of providers have entered the modular data center solutions space in the past. These modular data center solution providers came into the market with high expectations for growth and ROI only to find that high sales were not forthcoming due to limited use cases, so many exited the space.
Compression algorithms reduce the number of bits needed to represent a set of data—the higher the compression ratio, the more space this particular data reduction technique saves. During our OLTP test, the Unity array achieved a compression ratio of 3.2-to-1 on the database volumes, whereas the 3PAR array averaged a 1.3-to-1 ratio. In our data mart loading test, the 3PAR achieved a ratio of 1.4-to-1 on the database volumes, whereas the Unity array got 1.3 to 1.
Les algorithmes de compression réduisent le nombre de bits nécessaires pour représenter un ensemble de données. Plus le taux de compression est élevé, plus cette technique de réduction des données permet d’économiser de l’espace. Lors de notre test OLTP, la baie Unity a atteint un taux de compression de 3,2 pour 1 sur les volumes de base de données. De son côté, la baie 3PAR affichait en moyenne un taux de 1,3 pour 1. Sur le test de chargement DataMart, la baie 3PAR a atteint un taux de 1,4 pour 1 sur les volumes de bases de données, tandis que la baie Unity enregistrait un taux de 1,3 pour 1.
Predictive analytics have been used by different industries for years to solve difficult problems that range from detecting credit card fraud to determining patient risk levels for medical conditions. It combines data mining and machine-learning technologies to create statistical models based on historical data. It then uses these models to predict future events. Extracting the power from the data requires powerful algorithms behind predictive analytics.
Published By: Clustrix
Published Date: Sep 04, 2013
Find out how AdScience has been able to increase their revenue potential by five times using Clustrix to optimize bidding for their online ad broker agency. AdScience runs complicated algorithms to process bids for ad space based on click history. It's critical for AdScience to have instant access to smart data.
The misuse or takeover of privileged accounts constitutes the most common source of breaches today. CA Threat Analytics for PAM provides a continuous, intelligent monitoring capability that helps enterprises detect and stop hackers and malicious insiders before they cause damage.
The software integrates a powerful set of user behavior analytics and machine learning algorithms with the trusted controls provided by CA Privileged Access Manager (CA PAM). The result is a solution that continuously analyzes the activity of individual users, accurately detects malicious and high-risk activities and automatically triggers mitigating controls to limit damage to the enterprise.
There’s never been a more urgent need for comprehensive security and surveillance solutions. GeoVision Inc. has built its business on helping meet this need, providing digital and networked video surveillance solutions to customers in 110 countries. To succeed in its highly competitive and fast-changing industry, GeoVision must always be on the lookout for ways to give its customers leading-edge performance. Find out how GeoVision is working closely with Intel to maximize the performance of the hardware using the tools in Intel® System Studio, a comprehensive development tool suite to optimize the computer vision and deep learning workloads.
Interest in machine learning has exploded over the past decade. You see machine learning in computer science programs, industry conferences, and the Wall Street Journal almost daily. For all the talk about machine learning, many conflate what it can do with what they wish it could do. Fundamentally, machine learning is using algorithms to extract information from raw data and represent it in some type of model. We use this model to infer things about other data we have not yet modeled. Neural networks are one type of model for machine learning; they have been around
Published By: Monetate
Published Date: Oct 11, 2018
Monetate Intelligent Recommendations is the only solution that gives merchandisers & digital marketers the power to show contextually relevant product recommendations without burdening IT resources.
Using manually curated or algorithmically-driven recommendations, marketers can easily support even the most complex product catalogs. Our solution filters recommendations based on customer attributes (e.g. shirt size), longitudinal behaviours (e.g. browsing behaviour), and situational context (e.g. product inventory at local stores). Best of all, an orchestration layer intelligently selects which algorithms and which filters to apply in any given situation, for any particular individual.
Published By: Monetate
Published Date: Oct 22, 2018
Monetate Intelligent Recommendations automates recommendations at scale without sacrificing any of the control you require. Our proprietary algorithms know what to serve each individual shopper to maximise brand value, while still allowing the control of an unlimited number of business guardrails defined by you.
On-demand companies rely on fast, accurate and robust mapping and location technologies to provide their users with a superior experience. Find out how real-time, predictive and historical traffic data can be applied to traffic-enabled routing algorithms to influence route calculations and automatically plot multiple routes with waypoints sequencing.
Discover how HERE can help you communicate updated ETAs and provide an optimized experience to your drivers and customers.
Wikibon conducted in-depth interviews with organizations that had achieved Big Data success and high rates of returns. These interviews determined an important generality: that Big Data winners focused on operationalizing and automating their Big Data projects. They used Inline Analytics to drive algorithms that directly connected to and facilitated automatic change in the operational systems-of-record. These algorithms were usually developed and supported by data tables derived using Deep Data Analytics from Big Data Hadoop systems and/or data warehouses. Instead of focusing on enlightening the few with pretty historical graphs, successful players focused on changing the operational systems for everybody and managed the feedback and improvement process from the company as a whole.