CIO Business Intelligence

APRIL 4, 2022

These losses impact everyone from financial institutions, to businesses and individuals — victims of fraud. Preventing Fraud Real-time. The first challenge in overcoming fraud is to identify that it’s taking place. Better yet would be to prevent fraud from happening in the first place. Real-time Fraud Detection.

Technology 52

Technology Deep Learning Cost-Benefit Data Quality 52

CIO Business Intelligence

JUNE 2, 2022

Among the hot technologies, artificial intelligence and machine learning — a subset of AI that that makes more accurate forecasts and analysis as it ingests data — continue to be of high interest as banks keep a strong focus on costs while trying to boost customer experience and revenue. AI enhances operational efficiency.

Predictive Analytics 145

Predictive Analytics Technology Analytics Machine Learning 145

Smart Data Collective

SEPTEMBER 22, 2021

Cyber fraud statistics and preventions that every internet business needs to know to prevent data breaches in 2021. In the following section, we take a deep dive into the key trends that you should be aware of and the prevention measures to keep them at bay. The key types of cyber security frauds in 2021. Let’s dive in.

Statistics 105

Statistics Risk Measurement Software 105

Rocket-Powered Data Science

MARCH 10, 2020

And this: perhaps the most powerful node in a graph model for real-world use cases might be “context”. Hence, the graph model can be applied productively and effectively in numerous network analysis use cases. Next, consider the anti-money laundering (AML) use case: person A and person C are under suspicion for illicit trafficking.

Metadata 250

Metadata Machine Learning ROI Prescriptive Analytics 250

CIO Business Intelligence

FEBRUARY 10, 2023

Predictive analytics definition Predictive analytics is a category of data analytics aimed at making predictions about future outcomes based on historical data and analytics techniques such as statistical modeling and machine learning. Airlines frequently use predictive analytics to set ticket prices reflecting past travel trends.

Predictive Analytics 144

Predictive Analytics Analytics Predictive Modeling Forecasting 144

O'Reilly on Data

MARCH 31, 2020

If you’re already a software product manager (PM), you have a head start on becoming a PM for artificial intelligence (AI) or machine learning (ML). A lot to learn, but worthwhile to access the unique and special value AI can create in the product space. We won’t go into the mathematics or engineering of modern machine learning here.

Management 332

Management Machine Learning Experimentation Metrics 332

CIO Business Intelligence

MARCH 30, 2022

For organizations looking to move beyond stale reports, decision intelligence holds promise, giving them the ability to process large amounts of data with a sophisticated mix of tools such as artificial intelligence and machine learning to transform data dashboards and business analytics into more comprehensive decision support platforms.

Machine Learning 144

Machine Learning Risk Testing Data-driven 144

bridgei2i

JUNE 23, 2021

The use of Artificial Intelligence is increasing exponentially across all major industries. It enables the insurance agents to track claims and appeals, underwrite, issue policies, detect and prevent fraud, and update policy information. Fraud Detection. 6 Ways AI Is Taking The Insurance Industry Into The Future.

Insurance 52

Insurance Risk Deep Learning Data-driven 52

CIO Business Intelligence

APRIL 6, 2022

AIOps is built on AI and machine learning (ML) to evaluate processes, identify inefficiencies, and automate remediation, all with little to no human intervention. For long-term efficiency, algorithms can be trained to detect and anticipate performance issues before they negatively impact operations. Just starting out with analytics?

Data mining 72

Data mining Deep Learning Insurance Digital Transformation 72

IBM Big Data Hub

OCTOBER 16, 2023

Machine learning (ML)—the artificial intelligence (AI) subfield in which machines learn from datasets and past experiences by recognizing patterns and generating predictions—is a $21 billion global industry projected to become a $209 billion industry by 2029. At Facebook Messenger, ML powers customer service chatbots.

Machine Learning 51

Machine Learning Marketing Forecasting Modeling 51

Cloudera

FEBRUARY 11, 2022

Advances in the development and application of Machine Learning (ML) and Deep Learning (DL) algorithms, require greater care to ensure that the ethics embedded in previous rule-based systems are not lost. What is Machine Learning. Instead, they are learned by training a model on data.

Machine Learning 93

Machine Learning Sales Data Science Modeling 93

Cloudera

AUGUST 20, 2020

Machine Learning (ML) and Artificial Intelligence (AI), while still emerging technologies inside of enterprise organisations, have given some companies the ability to dynamically change their fortunes and reshape the way they are doing business — that is if they are brave enough to experiment and explore the unknown.

Enterprise 121

Enterprise Experimentation Uncertainty Deep Learning 121

IBM Big Data Hub

FEBRUARY 23, 2024

Enterprises can use NLU to offer personalized experiences for their users at scale and meet customer needs without human intervention. Machine learning (ML) and deep learning (DL) form the foundation of conversational AI development. DL models can improve over time through further training and exposure to more data.

Enterprise 106

Enterprise Interactive Data-driven Key Performance Indicator 106

IBM Big Data Hub

FEBRUARY 13, 2024

Generative AI represents a significant advancement in deep learning and AI development, with some suggesting it’s a move towards developing “ strong AI.” Generative adversarial networks (GANs) or variational autoencoders (VAEs) are used for images, videos, 3D models and music.

Enterprise 125

Enterprise Modeling Testing Data-driven 125

Smart Data Collective

DECEMBER 21, 2020

They interact with AI features on their phone or when using a service, so their expectations are ever-increasing. Insurers are already using AI to select rates for customers and measure the risk they may pose, but how will it directly be of use in claims processing? What will be the main changes to insurance claims?

Insurance 132

Insurance Cost-Benefit Big Data Unstructured Data 132

Cloudera

OCTOBER 4, 2023

For example: In manufacturing, fast-moving data provides the only way to detect — or even predict and prevent — defects in real time before they propagate across an entire production cycle. We can move to predictive fraud and breach prevention, greatly increasing the protection of customer data and financial assets.

Data Warehouse 85

Data Warehouse Experimentation Cost-Benefit Dashboards 85

IBM Big Data Hub

FEBRUARY 14, 2024

When thinking of artificial intelligence (AI) use cases, the question might be asked: What won’t AI be able to do? But right now, pure AI can be programmed for many tasks that require thought and intelligence , as long as that intelligence can be gathered digitally and used to train an AI system.

Cost-Benefit 88

Cost-Benefit Insurance Unstructured Data Machine Learning 88

Domino Data Lab

MARCH 11, 2021

Machine learning (ML) frameworks are interfaces that allow data scientists and developers to build and deploy machine learning models faster and easier. Machine learning is used in almost every industry, notably finance , insurance , healthcare , and marketing. How to choose the right ML Framework. Evaluating Your Needs.

Machine Learning 143

Machine Learning Deep Learning Insurance Modeling 143

IBM Big Data Hub

JULY 11, 2023

Artificial intelligence is used in healthcare for everything from answering patient questions to assisting with surgeries and developing new pharmaceuticals, benefitting both patients and healthcare systems. How does artificial intelligence benefit healthcare?

Deep Learning 60

Deep Learning Cost-Benefit Insurance Machine Learning 60

Ontotext

MARCH 8, 2023

Gartner predicts that graph technologies will be used in 80% of data and analytics innovations by 2025, up from 10% in 2021. Use Case #1: Customer 360 / Enterprise 360 Customer data is typically spread across multiple applications, departments, and regions. Several factors are driving the adoption of knowledge graphs. million users.

Enterprise 52

Enterprise Knowledge Discovery Risk Data-driven 52

CIO Business Intelligence

APRIL 13, 2023

For instance, if you start with a Monet painting and use an extended generative AI model trained on all Monet artwork and complementary artists, you can create an input dataset that includes images by the artist. Deep learning models, for example, can have thousands or even millions of parameters.

Modeling 72

Modeling Deep Learning Data-driven Data Processing 72

O'Reilly on Data

OCTOBER 22, 2020

A recent discussion led us down the path of our favorite questions: what they are, why they’re useful, and when they don’t work so well. We hope you’re able to borrow questions you haven’t used before, and even cook up new questions that are more closely related to your personal and professional interests. .”

Consulting 296

Consulting Risk Cost-Benefit Modeling 296

Domino Data Lab

JULY 28, 2021

In this article, take a deep dive into data science and how Domino’s Enterprise MLOps platform allows you to scale data science in your business. What is Data Science and How is it Used? Improvements in storage have made it possible to capture, manage and use this data in as fast as real time. What are Data Scientists?

Data Science 45

Data Science Machine Learning Statistics Deep Learning 45

bridgei2i

NOVEMBER 20, 2020

These solutions help drive both functional and pan-enterprise transformation by making AI real.One of the crucial asks in enterprises today is the need for agile business surveillance, executives want real-time business performance monitoring with the ability to get contextual alerts and deep dive analysis. Ashwini: Yeah, absolutely.

Risk Management 92

Risk Management Risk Management Metrics 92

Smart Data Collective

NOVEMBER 1, 2020

Machines, artificial intelligence (AI), and unsupervised learning are reshaping the way businesses vie for a place under the sun. With that being said, let’s have a closer look at how unsupervised machine learning is omnipresent in all industries. What Is Unsupervised Machine Learning? Well, machine learning is almost the same.

Machine Learning 131

Machine Learning Data mining Visualization Statistics 131

bridgei2i

APRIL 23, 2020

In this fluid situation, banks anticipate their risks to grow manifold under various categories: Banks will monitor portfolios at a much granular level and use agile methodology. They will need to evolve new scorecards considering the pandemic factor for early default detection. Challenge of Rising Fraud. Learn MORE. “We

Risk 52

Risk Management Scorecard Forecasting 52

IBM Big Data Hub

JANUARY 16, 2024

Learn more about hybrid cloud for financial services Trend: Cybersecurity risk management AI brings about more challenges, especially when it comes to cyber risks. The need for increased fraud detection will also be important to watch in the coming year as more and more financial institutions try generative AI.

Risk Management 51

Risk Management Insurance Cost-Benefit Risk 51

Cloudera

SEPTEMBER 12, 2018

It’s a big week for us, as many Clouderans descend on New York for the Strata Data Conference. The week is typically filled with exciting announcements from Cloudera and many partners and others in the data management, machine learning and analytics industry. Congratulations and high fives to every organization that submitted entries.

Machine Learning 45

Machine Learning Big Data Data Science Data Warehouse 45

Insight

AUGUST 20, 2019

Data Engineering is a discipline notorious for being framework-driven and it is often hard for newcomers to find the right ones to learn. As part of their fellowship training and transition into data engineering, Fellows spend three weeks working on data engineering projects where they dive deep into these frameworks.

Data Warehouse 71

Data Warehouse Big Data Data-driven Data Processing 71

Cloudera

NOVEMBER 19, 2020

November 15-21 marks International Fraud Awareness Week – but for many in government, that’s every week. From bogus benefits claims to fraudulent network activity, fraud in all its forms represents a significant threat to government at all levels. A solid foundation for fraud detection. Some experts estimate the U.S.

Machine Learning 114

Machine Learning Data-driven Modeling Deep Learning 114

IBM Big Data Hub

APRIL 15, 2024

Data virtualization empowers businesses to unlock the hidden potential of their data, delivering real-time AI insights for cutting-edge applications like predictive maintenance, fraud detection and demand forecasting. With the rise of generative AI chatbots, foundation models now use this rich data set.

Analytics 72

Analytics Optimization Data-driven Metrics 72

O'Reilly on Data

OCTOBER 13, 2020

In contrast, many production AI systems rely on feedback loops that require the same technical skills used during initial development. This distinction assumes a slightly different definition of debugging than is often used in software development.

Management 243

Management Metrics Machine Learning Modeling 243

Jen Stirrup

FEBRUARY 24, 2024

In this post, we will review Artificial Intelligence with some example use cases to see how Artificial Intelligence is for small businesses and more prominent, established businesses. In simple terms, Artificial Intelligence allows machines to emulate human cognitive functions like learning and problem-solving.

Cost-Benefit 70

Cost-Benefit Data-driven Testing Risk 70

Domino Data Lab

APRIL 21, 2021

In this article, we’ll discuss the challenge organizations face around fraud detection, how machine learning can be used to identify and spot anomalies that the human eye might not catch. deep learning) there is no guaranteed explainability. It can be implemented as either unsupervised (e.g.

Statistics 69

Statistics Machine Learning Modeling Metrics 69

O'Reilly on Data

MARCH 20, 2019

Apply fair and private models, white-hat and forensic model debugging, and common sense to protect machine learning models from malicious actors. Like many others, I’ve known for some time that machine learning models themselves could pose security risks. Data poisoning attacks.

Modeling 221

Modeling Machine Learning Predictive Modeling Consulting 221

Domino Data Lab

APRIL 29, 2021

Deep learning enthusiasts are increasingly putting NVIDIA’s GTC at the top of their gotta-be-there conference list. Three of them were particularly compelling and inspired a new point of view on transfer learning that I feel is important for analytical practitioners and leaders to understand. Deep Learning Trends from GTC21.

Data Science 134

Data Science Deep Learning Modeling Forecasting 134

Cloudera

NOVEMBER 25, 2021

We are bringing this to life through the deep product integration of our Cloudera Data Platform (CDP) on AWS, with field collaboration and AWS Marketplace availability, with credits for evaluation and migration. Common Use Cases for Cloud and Data Solutions . Customer use cases can be grouped into three categories. .

Machine Learning 88

Machine Learning IoT Risk Digital Transformation 88