predictive models

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By: Dun & Bradstreet     Published Date: Mar 03, 2017
Creating predictive analytics from alternative data has become the current focus of the biggest quant trading firms in the industry The democratization of financial services data and technology, together with more intense competition, makes the needs of today’s market participants vastly different from those of previous generations. Firms must locate untapped sources of data for both public and non-public companies. This alternative data, such as payment data and other non-public information, from sources beyond the common channels, can be a predictive indicator of market performance; a difference maker in assisting firms as they develop models to evaluate their investments. By combining our unique data sets with advanced analytics, traders, analysts and managers can seek predictive signals and actionable information utilizing their own models. View our research report to learn how alternative data, our 'Information Alpha,' can help you earn differentiated investment returns.
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     Dun & Bradstreet
By: Akamai     Published Date: Jun 04, 2010
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.
Tags : akamai, predictive, online advertising, tracking pixels, online shopping, in-market, site visitors, performance marketing
     Akamai
By: IBM     Published Date: Jul 19, 2016
This video demonstrates how IBM’s Behavior Based Customer Insight for Banking leverages predictive analytics to help you personalize customer engagement and deliver customized actions. The solution leverages advanced predictive models to analyze customer transactions and spending behavior to more deeply understand customer needs and propensities, anticipate life events, and help provide a unique customer experience.
Tags : ibm, banking, finance, consumer insights, business intelligence, business anlytics, enterprise applications
     IBM
By: TIBCO Software     Published Date: Aug 02, 2019
A perfect storm of legislation, market dynamics, and increasingly sophisticated fraud strategies requires you to be proactive in detecting fraud quicker and more effectively. TIBCO’s Fraud Management Platform allows you to meet ever-increasing requirements faster than traditional in-house development, easier than off-the-shelf systems, and with more control because you’re in charge of priorities, not a vendor. All this is achieved using a single engine that can combine traditional rules with newer predictive analytics models. In this webinar you will learn: Why a fraud management platform is necessary How to gain an understanding of the components of a fraud management platform The benefits of implementing a fraud management platform How the TIBCO platform has helped other companies Unable to attend live? We got you. Register anyway and receive the recording after the event.
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     TIBCO Software
By: Domino Data Lab     Published Date: Feb 08, 2019
As data science becomes a critical capability for companies, IT leaders are finding themselves responsible for enabling data science teams with infrastructure and tooling. But data science is much more like an experimental research organization than the engineering and business teams that IT organizations support today. Compounding the challenge, data science teams are growing fast, often by 100% a year. This guide will quickly help you understand what data science teams do to build their predictive models and how to best support them. Learn how to modernize IT’s approach to ensure your company’s data science teams perform their best, and maximize impact to the business. Some highlights include: Why data science should not be treated like engineering. How to go beyond simple infrastructure allocation and give data science teams capabilities to manage their workflows and model lifecycle. Why agility and special hardware to support burst computing are so important to data science break
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     Domino Data Lab
By: Domino Data Lab     Published Date: Feb 08, 2019
A data science platform is where all data science work takes place and acts as the system of record for predictive models. While a few leading model-driven businesses have made the data science platform an integral part of their enterprise architecture, most companies are still trying to understand what a data science platform is and how it fits into their architecture. Data science is unlike other technical disciplines, and models are not like software or data. Therefore, a data science platform requires a different type of technology platform. This document provides IT leaders with the top 10 questions to ask of data science platforms to ensure the platform handles the uniqueness of data science work.
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     Domino Data Lab
By: Domino Data Lab     Published Date: May 23, 2019
As data science becomes a critical capability for companies, IT leaders are finding themselves responsible for enabling data science teams with infrastructure and tooling. But data science is much more like an experimental research organization than the engineering and business teams that IT organizations support today. Compounding the challenge, data science teams are growing fast, often by 100% a year. This guide will quickly help you understand what data science teams do to build their predictive models and how to best support them. Learn how to modernize IT’s approach to ensure your company’s data science teams perform their best, and maximize impact to the business. Some highlights include: Why data science should not be treated like engineering. How to go beyond simple infrastructure allocation and give data science teams capabilities to manage their workflows and model lifecycle. Why agility and special hardware to support burst computing are so important to data science break
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     Domino Data Lab
By: Datarobot     Published Date: May 14, 2018
The DataRobot automated machine learning platform captures the knowledge, experience, and best practices of the world’s leading data scientists to deliver unmatched levels of automation and ease-of-use for machine learning initiatives. DataRobot enables users of all skill levels, from business people to analysts to data scientists, to build and deploy highly-accurate predictive models in a fraction of the time of traditional modeling methods
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     Datarobot
By: FICO     Published Date: Feb 06, 2017
FICO is helping the bank construct the most predictive and effective expected loss models possible and is also helping it assess risk impact across portfolios
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     FICO
By: TIBCO Software     Published Date: Mar 04, 2019
A perfect storm of legislation, market dynamics, and increasingly sophisticated fraud strategies requires you to be proactive in detecting fraud quicker and more effectively. TIBCO’s Fraud Management Platform allows you to meet ever-increasing requirements faster than traditional in-house development, easier than off-the-shelf systems, and with more control because you’re in charge of priorities, not a vendor. All this is achieved using a single engine that can combine traditional rules with newer predictive analytics models. In this webinar you will learn: Why a fraud management platform is necessary How to gain an understanding of the components of a fraud management platform The benefits of implementing a fraud management platform How the TIBCO platform has helped other companies Unable to attend live? We got you. Register anyway and receive the recording after the event.
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     TIBCO Software
By: TruSignal     Published Date: Jun 03, 2013
This white paper aims to provide B2C digital marketers with a better understanding of why you may need an audience expansion technique and what questions to ask yourself before you get started. We hope to not only build an imperative for audience expansion techniques, but also to offer a guide that will help you choose the right data and right techniques for reaching more of your desired prospects online. Specifically, this white paper will discuss and differentiate two specific expansion approaches: lookalike and act-alike audiences including how they are built, the problems they solve and how to use them effectively throughout the marketing funnel.
Tags : audience expansion, lookalike, act-alike, audience targeting, predictive analytics, big data, profile data, behavioral data
     TruSignal
By: FICO     Published Date: Nov 03, 2015
Using predictive models.
Tags : model, risk governance, management, analytics, optimization, compliance
     FICO
By: iKnowtion     Published Date: Nov 17, 2011
Learn how predictive models can improve call volume forecasting. The models provide information about how various independent factors, such as advertising, affect call volume in the short and medium term.
Tags : customer intelligence, predictive modeling, forecasting, call volume, marketing analytics, iknowtion
     iKnowtion
By: IBM     Published Date: Aug 07, 2012
View this demo to find out how IBM SPSS® solutions for predictive customer analytics can deliver deep customer insights that help you tune your marketing efforts-effectively and efficiently attracting new customers, nurturing customer relationships and retaining ideal customers. Watch how IBM SPSS software uses existing customer information to help you do the following: Identify your best customers for targeted marketing programs with customer segmentation, cluster and profiling techniques; confidently predict which customers will respond to your offers with powerful predictive models; get more out of every customer interaction by delivering real-time, predictive intelligence to front-line decision makers; and enrich and deepen your customer insight with social media analytics.
Tags : predictive customer analytics, ibm, decision management, consumer insight, customer, crm, customer relationship management, retain customer
     IBM
By: IBM     Published Date: Aug 08, 2012
With tight budgets, it isn't easy to create the operational dexterity needed to thrive in a competitive marketplace. View this demo to find out how IBM® SPSS® solutions for predictive operational analytics help manage physical and virtual assets, maintain infrastructure and capital equipment, and improve the efficiency of people and processes. By using your existing business information, IBM SPSS software can help you: predict and prevent equipment failures that can lead to disruptive, costly downtime; quickly identify and resolve product quality issues to mitigate risks and reduce warranty costs; optimize product assortment planning to increase revenue, reduce working capital requirements and improve the return on inventory investments; and act to retain your best employees by developing predictive attrition models to identify the workers at greatest risk of leaving the organization.
Tags : ibm, technology, predictive operational analytics, spss
     IBM
By: AWS     Published Date: Jul 24, 2019
Trupanion, a Seattle-based medical insurance provider for cats and dogs, needed to find data insights quickly. With only 1% of pet owners insured, the process of evaluating a claim to approve or deny payment was manual and time-consuming. Building accurate predictive models for decision-making required manpower, time, and technology that the small company simply did not have. DataRobot Cloud, built on AWS, helped Trupanion create an automated method for building data models using machine learning that reduced the time required to process claims from minutes to seconds. Join our webinar to hear how Trupanion transformed itself into an AI-driven organization, with robust data analysis and data science project prototyping that empowered the company to make better decisions and optimize business processes in less time and at a reduced cost. Join our webinar to learn: Why you don’t need to be an expert in data science to create accurate predictive models. How you can build and deploy pr
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     AWS
By: IBM     Published Date: Nov 14, 2014
IBM SPSS Modeler is a powerful, versatile data and text analytics workbench. Learn how you can build accurate predictive models quickly and intuitively, without programming. So you can use data to understand the current state of your organization and get a view into the future.
Tags : ibm, webinar, business intelligence, entity analytics, modeler, automated modeling, predictive intelligence, building models
     IBM
By: IBM     Published Date: Feb 05, 2015
Using IBM SPSS Modeler to develop predictive models for crime prevention, the City of Lancaster saw a crime rate reduction of over 35 percent.
Tags : roi, predictive models, crime rate reduction, spss modeler, predictive analytics
     IBM
By: IBM     Published Date: Feb 05, 2015
Discovery Health ... Predictive analytics used to craft preventive programs that keep members healthier and costs lower.
Tags : predictive analytics, preventive programs, predictive risk management, clinical risk models
     IBM
By: IBM     Published Date: Jul 12, 2016
Join us for a complimentary webinar with Mark Simmonds, IBM big data IT Architect who will talk with leading analyst Mike Ferguson of Intelligent Business Strategies about the current fraud landscape. They will discuss the business impact of fraud, how to develop a fraud-protection strategy and how IBM z Systems analytics solutions and predictive models can dramatically reduce your risk exposure and loss from fraud.
Tags : ibm, z systems, fraud loss reduction, fraud management, fraud prevention, fraud analytics, roi, security
     IBM
By: SAS     Published Date: Dec 20, 2018
Think of the self-service things you use in a day. Gas pumps. ATMs. Online apps for shopping. They’re convenient and easy to use. People choose what they want, when they want – without involving others in their minute-to-minute decisions. What if your organization could treat data discovery and analytics the same way? SAS has combined two of its visual solutions to do just that. SAS Visual Analytics and SAS Visual Statistics share the same web-based interface to provide self-service data exploration and easy-to-use interactive predictive analytics in a collaborative environment. This white paper takes a look at this convergence and outlines how these products can be used together so that everyone, even nontechnical users, can investigate data on their own, create analytical models and uncover new insights that drive competitive differentiation. Your analytics journey just got a lot easier.
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     SAS
By: Turn     Published Date: Mar 13, 2013
3 Keys to Look-Alike Modeling
Tags : look-alike modeling, targeting, predictive modeling, predictive models, advertising strategy
     Turn
By: IBM     Published Date: May 19, 2015
In this report we assess the performance of major providers of advanced analytics solutions and provide insight into how their customers are benefiting from these solutions.
Tags : advanced analytics, analytic solutions, complex predictive models, analytics platform, metrics, business metrics
     IBM
By: IBM     Published Date: May 22, 2015
In this webinar, we will discuss the broad range of IBM deployment approaches that can help organizations solve their business challenges and achieve a higher ROI from analytics.
Tags : analytics, roi, management, ibm, strategy
     IBM
By: IBM     Published Date: Jul 12, 2016
As most companies now realize, analytics is increasingly more of an integral part of their day-to-day business operations. In a recent survey by a global research firm, 80% of CIOs stated that transition from backward-looking, passive analysis must shift to forward-looking predictive analytics. The challenge is that many analytic solutions are aligned to a specific platform, tied to inflexible programming models and requiring vast data movement. In this webcast, Forrester and experts from IBM will highlight how technology like Apache Spark on z/OS allows businesses to extract deep customer insight without the cost, latency and security risks of data movement throughout the enterprise.
Tags : ibm, forrester, apache spark, spark technology, z systems, security, enterprise applications
     IBM
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