BizAcuity

APRIL 1, 2023

Anti-Money Laundering (AML) is increasingly becoming a crucial branch of risk management and fraud prevention. In fact, online casinos as an industry carries the biggest risk of money laundering. With the exponential growth of large datasets, predictive analytics is being leveraged by enterprises across industries.

Machine Learning 52

Machine Learning Predictive Analytics Predictive Modeling Risk 52

BizAcuity

JANUARY 18, 2023

Anti-Money Laundering (AML) is increasingly becoming a crucial branch of risk management and fraud prevention. In fact, online casinos as an industry carries the biggest risk of money laundering. With the exponential growth of large datasets, predictive analytics is being leveraged by enterprises across industries.

Machine Learning 52

Machine Learning Predictive Analytics Predictive Modeling Risk 52

CIO Business Intelligence

JUNE 22, 2022

The financial services industry has had to dedicate more resources to personalisation, fighting fraud, and reducing cloud concentration risk. Real-time access to accurate data on customers that drives machine learning models are crucial to the accuracy of predictions or recommendations they make in real time.

IoT 92

IoT IT Manufacturing Internet of Things 92

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. they can train their own surrogate model.

Modeling 219

Modeling Machine Learning Predictive Modeling Consulting 219

IBM Big Data Hub

MAY 17, 2024

But as businesses around the globe rapidly adopt the technology to augment processes from merchandising to order management, there is some risk. By using machine learning algorithms and big data analytics, AI can uncover patterns, correlations and trends that might escape human analysts.

B2B 68

B2B Interactive Optimization Forecasting 68

Business Over Broadway

SEPTEMBER 27, 2018

In this paper, I show you how marketers can improve their customer retention efforts by 1) integrating disparate data silos and 2) employing machine learning predictive analytics. In our world of Big Data, marketers no longer need to simply rely on their gut instincts to make marketing decisions.

Machine Learning 76

Machine Learning Data Integration Marketing Predictive Modeling 76

datapine

NOVEMBER 27, 2019

This is one of the major trends chosen by Gartner in their 2020 Strategic Technology Trends report , combining AI with autonomous things and hyperautomation, and concentrating on the level of security in which AI risks of developing vulnerable points of attacks. Industries harness predictive analytics in different ways.

Business Intelligence 361

Business Intelligence Analytics Prescriptive Analytics Data Quality 361

IBM Big Data Hub

MAY 1, 2023

The early versions of AI were capable of predictive modelling (e.g., The four categories of predictive modelling, robotics, speech and image recognition are collectively known as algorithm-based AI or Discriminative AI. recommending similar Netflix shows based on your previous choices) or robotics (e.g.,

Insurance 79

Insurance Predictive Modeling Cost-Benefit Statistics 79

IBM Big Data Hub

JULY 6, 2023

While data science and machine learning are related, they are very different fields. In a nutshell, data science brings structure to big data while machine learning focuses on learning from the data itself. What is data science? This post will dive deeper into the nuances of each field.

Machine Learning 57

Machine Learning Data Science Statistics Deep Learning 57

IBM Big Data Hub

MAY 18, 2023

This report outlines the combination of traditional decision automation tools with machine learning models and other technologies. As Forrester notes in the report, many organizations are eager to harness the power of AI but also must be cautious of risks.

Reporting 91

Reporting Scorecard Predictive Modeling Machine Learning 91

CIO Business Intelligence

JUNE 7, 2022

Monte Carlo simulation: According to Investopedia , “Monte Carlo simulations are used to model the probability of different outcomes in a process that cannot easily be predicted due to the intervention of random variables.” It is frequently used for risk analysis. Data analytics and data science are closely related.

Data Analytics 133

Data Analytics Diagnostic Analytics Management Analytics 133

Domino Data Lab

JULY 28, 2021

Data science is a field that uses math and statistics as part of a scientific process to develop an algorithm that can extract insights from data. All models are not made equal. At this stage, data scientists begin writing code for computation and model-building.

Data Science 110

Data Science Statistics Recreation/Entertainment Predictive Analytics 110

Rocket-Powered Data Science

OCTOBER 6, 2023

In these applications, the data science involvement includes both the “learning” of the most significant patterns to alert on and the improvement of their models (logic) to minimize false positives and false negatives. Broken models are definitely disruptive to analytics applications and business operations.

Business Analytics 157

Business Analytics Prescriptive Analytics Analytics Diagnostic Analytics 157

Cloudera

NOVEMBER 18, 2020

It hosts over 150 big data analytics sandboxes across the region with over 200 users utilizing the sandbox for data discovery. With this functionality, business units can now leverage big data analytics to develop better and faster insights to help achieve better revenues, higher productivity, and decrease risk. .

Internet Publishing and Broadcasting 121

Internet Publishing and Broadcasting Data-driven Broadcasting Digital Transformation 121

BizAcuity

MARCH 18, 2019

The consumer lending business is centered on the notion of managing the risk of borrower default. Credit scoring systems and predictive analytics model attempt to quantify uncertainty and provide guidance for identifying, measuring and monitoring risk. Benefits of Predictive Analytics in Unsecured Consumer Loan Industry.

Predictive Analytics 83

Predictive Analytics Analytics Cost-Benefit Data mining 83

CIO Business Intelligence

OCTOBER 13, 2023

Data scientist As companies embrace gen AI, they need data scientists to help drive better insights from customer and business data using analytics and AI. For most companies, AI systems rely on large datasets, which require the expertise of data scientists to navigate.

Deep Learning 143

Deep Learning Machine Learning Modeling Software 143

IBM Big Data Hub

OCTOBER 5, 2022

You also build planning models to capture relationships and constraints so that you can change your driver assumptions and immediately see the impact on resources and capacity over time. Time matters too: your models must be quick to run, so analysis can be done before the assumptions are out-of-date.

Analytics 52

Analytics Forecasting Predictive Modeling Manufacturing 52

IBM Big Data Hub

FEBRUARY 14, 2024

By infusing AI into IT operations , companies can harness the considerable power of NLP, big data, and ML models to automate and streamline operational workflows, and monitor event correlation and causality determination. ML algorithms can predict patterns, improve accuracy, lower costs and reduce the risk of human error.

Cost-Benefit 86

Cost-Benefit Insurance Unstructured Data Machine Learning 86

Smart Data Collective

MAY 23, 2021

Rodrigo Liang is CEO of SambaNova, which provides both hardware and software to businesses for the purpose of analyzing data. While this can be classed as data science, one difference is that data science tends to use a predictive model to make its analysis, while AI can be capable of analyzing based on learned knowledge and facts.

Data Science 99

Data Science Cost-Benefit Statistics Predictive Modeling 99

IBM Big Data Hub

AUGUST 28, 2023

Text representation In this stage, you’ll assign the data numerical values so it can be processed by machine learning (ML) algorithms, which will create a predictive model from the training inputs.

Data mining 56

Data mining Machine Learning Deep Learning Marketing 56

O'Reilly on Data

MARCH 24, 2020

The assumed value of data is a myth leading to inflated valuations of start-ups capturing said data. John Myles White , data scientist and engineering manager at Facebook, wrote: “The biggest risk I see with data science projects is that analyzing data per se is generally a bad thing.

Machine Learning 343

Machine Learning Statistics Data Quality Modeling 343

IBM Big Data Hub

JUNE 29, 2023

Sensors collect data in real-time that is then fed into an enterprise asset management (EAM) or computerized maintenance management system (CMMS), where AI-enhanced data analysis tools and processes like machine learning (ML) spot issues and help resolve them.

Cost-Benefit 84

Cost-Benefit Measurement Management Predictive Analytics 84

Sisense

MARCH 27, 2019

This includes the ability to perform ad-hoc analysis on existing data or creating visualizations specific to new streams. Finally, real-time BI helps better understand trends and create more accurate predictive models for organizations. Who Uses Real-Time BI? What are the Real-Time BI Best Practices?

Business Intelligence 74

Business Intelligence Data Warehouse Predictive Modeling Visualization 74

IBM Big Data Hub

OCTOBER 16, 2023

ML also helps businesses forecast and decrease customer churn (the rate at which a company loses customers), a widespread use of big data. Banks and other financial institutions train ML models to recognize suspicious online transactions and other atypical transactions that require further investigation.

Machine Learning 49

Machine Learning Marketing Forecasting Modeling 49

Cloudera

SEPTEMBER 5, 2018

Having clean, validated, conformed historical data in a single repository is critical for an effective AML program. It provides more relevant matches for KYC screening, enables better predictive algorithms for risk scoring, shortens time spent on investigation, and cuts down on costs to support periodic regulatory reviews.

Machine Learning 40

Machine Learning Risk Cost-Benefit Big Data 40

Smart Data Collective

SEPTEMBER 30, 2021

to help with data processing , but it still proves challenging. One particular company, Watershed, hopes to be able to build a tool in which raw data can yield insight and concrete actions in which carbon emissions are reduced. Drought Risk Assessment and Prediction. Many companies use platforms like Spark 3.0

Machine Learning 120

Machine Learning Cost-Benefit Optimization Forecasting 120

Smart Data Collective

MAY 17, 2021

Big data is at the heart of the digital revolution. Basing fleet management operations on data is not new, and in some ways, it’s always been a part of the industry. Basing fleet management operations on data is not new, and in some ways, it’s always been a part of the industry. Improved Fleet Management Controls.

Big Data 100

Big Data Cost-Benefit IoT Machine Learning 100

IBM Big Data Hub

AUGUST 14, 2023

Under-deployed tools and solutions that do the minimal that’s “good enough” or that face other barriers like the risk aversion to fully automating processes that could have unintended consequences. Budget constraints for cybersecurity and perception that their organization is sufficiently protected.

Cost-Benefit 58

Cost-Benefit Machine Learning Consulting Data mining 58

Domino Data Lab

JANUARY 21, 2021

Producing insights from raw data is a time-consuming process. Predictive modeling efforts rely on dataset profiles , whether consisting of summary statistics or descriptive charts. Results become the basis for understanding the solution space (or, ‘the realm of the possible’) for a given modeling task.

Statistics 106

Statistics Unstructured Data Data Science Visualization 106

IBM Big Data Hub

MAY 6, 2024

Optimize protocols using historical and synthetic data Complex and stringent protocols are notorious for delaying clinical trials and eroding patient engagement. Diving deep into historical trial data with a protocol complexity rating also reveals golden insights, especially around patient-centric elements.

Advertising 84

Advertising Data-driven Optimization Strategy 84

datapine

DECEMBER 4, 2019

Predictive analytics is the practice of extracting information from existing data sets in order to forecast future probabilities. Applied to business, it is used to analyze current and historical data in order to better understand customers, products, and partners and to identify potential risks and opportunities for a company.

Business Intelligence 212

Business Intelligence Prescriptive Analytics Analytics Predictive Analytics 212

Jet Global

MAY 9, 2024

Machine Learning Pipelines : These pipelines support the entire lifecycle of a machine learning model, including data ingestion , data preprocessing, model training, evaluation, and deployment. API Data Pipelines : These pipelines retrieve data from various APIs and load it into a database or application for further use.

Data Lake 52

Data Lake Data Warehouse Business Intelligence Machine Learning 52

O'Reilly on Data

SEPTEMBER 19, 2023

Each time, the underlying implementation changed a bit while still staying true to the larger phenomenon of “Analyzing Data for Fun and Profit.” ” They weren’t quite sure what this “data” substance was, but they’d convinced themselves that they had tons of it that they could monetize.

Machine Learning 243

Machine Learning Testing Modeling Cost-Benefit 243

IBM Big Data Hub

APRIL 5, 2024

The maintenance, repair and overhaul (MRO) system provides necessary spare parts to carry out work and maintains an optimum stock level with a balance of stock out risk and part holding costs. Asset criticality defines whether a preventive or predictive task is justified in terms of cost and risk.

Management 68

Management Risk Optimization Predictive Modeling 68

O'Reilly on Data

SEPTEMBER 11, 2019

Machine learning, artificial intelligence, data engineering, and architecture are driving the data space. The Strata Data Conferences helped chronicle the birth of big data, as well as the emergence of data science, streaming, and machine learning (ML) as disruptive phenomena. 105, -17) or even “Python” (No.

IoT 20

IoT Big Data Data Warehouse Uncertainty 20

IBM Big Data Hub

DECEMBER 20, 2023

For instance, if data scientists were building a model for tornado forecasting, the input variables might include date, location, temperature, wind flow patterns and more, and the output would be the actual tornado activity recorded for those days. temperature, salary).

Machine Learning 78

Machine Learning Modeling Deep Learning Predictive Modeling 78

IBM Big Data Hub

JULY 5, 2023

Reduced risk of security breaches: Through real-time asset tracking and improved asset security capabilities, market-leading ALM systems can now help businesses track and monitor their assets in a way that helps prevent theft and data breaches.

Strategy 55

Strategy Management IoT Cost-Benefit 55