O'Reilly on Data

MAY 18, 2020

This article answers these questions, based on our combined experience as both a lawyer and a data scientist responding to cybersecurity incidents, crafting legal frameworks to manage the risks of AI, and building sophisticated interpretable models to mitigate risk. All predictive models are wrong at times?—just

Risk 359

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Smart Data Collective

MARCH 23, 2022

This AI could be utilized as a safety feature , like real-time risk assessment, for example, alerting the driver when a potential incident has been detected. Software that has bugs need to be properly tested at all stages of development for both functionalities as well as cybersecurity. The first is developing high-quality software.

IoT 111

IoT Internet of Things Testing Cost-Benefit 111

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

Smarten

MAY 31, 2022

The technology research firm, Gartner has predicted that, ‘predictive and prescriptive analytics will attract 40% of net new enterprise investment in the overall business intelligence and analytics market.’ It is meant to identify crucial relationships and opportunities and risks and help the organization to accurately predict: Growth.

Predictive Analytics 67

Predictive Analytics Analytics Predictive Modeling Prescriptive Analytics 67

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

Smart Data Collective

SEPTEMBER 17, 2020

Financial institutions such as banks have to adhere to such a practice, especially when laying the foundation for back-test trading strategies. Here are a few of the advantages of Big Data in the banking and financial industry: Improvement in risk management operations. The Role of Big Data. Engaging the Workforce.

Big Data 142

Big Data Finance Risk Management Risk 142

O'Reilly on Data

DECEMBER 12, 2019

Because all ML models make mistakes, everyone who cares about ML should also care about model debugging. [1] 1] This includes C-suite executives, front-line data scientists, and risk, legal, and compliance personnel. Model debugging is an emergent discipline focused on finding and fixing problems in ML systems.

Machine Learning 191

Machine Learning Modeling Testing Risk Management 191

DataRobot

OCTOBER 19, 2021

DataRobot models successfully identified areas with high per-capita infection rates across the board, not only the densely populated urban counties. This approach allows end-users to make better informed decisions when assessing risk and other criteria such as age or ethnic diversity. reporting backlogs).

Forecasting 98

Forecasting Predictive Modeling Optimization Testing 98

CIO Business Intelligence

AUGUST 23, 2023

To do so, they explored the optimization problem of “cardinality constraints” and developed a hybrid quantum-classical approach to financial index tracking portfolios that maximizes returns and minimizes risk. Because of quantum’s abilities the Ally team could create 50 separate scenarios and back-test the models.

IT 85

IT Machine Learning Risk Optimization 85

DataRobot

MAY 25, 2021

DataRobot is excited to be awarded the 2021 ACT-IAC Innovation Award for ContagionNET, our pioneering rapid antigen test for COVID-19 that is at the forefront of pandemic preparedness and response. As part of these efforts, we built accurate predictive models to determine the spread of the disease weeks and months in advance of a surge.

Testing 52

Testing Predictive Modeling Data-driven Reporting 52

CIO Business Intelligence

JUNE 7, 2022

Predictive analytics is often considered a type of “advanced analytics,” and frequently depends on machine learning and/or deep learning. Prescriptive analytics is a type of advanced analytics that involves the application of testing and other techniques to recommend specific solutions that will deliver desired outcomes.

Data Analytics 133

Data Analytics Diagnostic Analytics Management Analytics 133

Smarten

JUNE 25, 2019

Augmented analytics allows for data prep, Smart Data Visualization and Assisted Predictive Modeling with the help of machine learning and natural language processing (NLP), so users need not be trained as data scientists to get to the heart of the data and find those elusive nuggets of information that will help them create, change and improve.

Analytics 51

Analytics Predictive Modeling Visualization Machine Learning 51

CIO Business Intelligence

OCTOBER 5, 2023

As chief digital and technology officer at CBRE, Davé recognized early that the commercial real estate industry was ripe for AI and machine learning enhancements, and he and his team have tested countless use cases across the enterprise ever since. For AI, the high-value quadrant is where you’ll find most predictive modeling.

Experimentation 127

Experimentation Strategy Machine Learning Interactive 127

Business Over Broadway

SEPTEMBER 27, 2018

pharmacogenomics) and risk assessment of genetic disorders (e.g., genetic counseling, genetic testing). Analytics applied to these types of data help you generate better predictive models because your integrated data contain all the key variables that are useful in predicting customer churn. Summary.

Machine Learning 76

Machine Learning Data Integration Marketing Predictive Modeling 76

Smarten

APRIL 17, 2018

How Can Assistive Predictive Modeling Help My Business Users? Assisted Predictive Modeling can help your business achieve its goals.

Predictive Analytics 40

Predictive Analytics Predictive Modeling Analytics Forecasting 40

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

Domino Data Lab

JULY 28, 2021

There are many software packages that allow anyone to build a predictive model, but without expertise in math and statistics, a practitioner runs the risk of creating a faulty, unethical, and even possibly illegal data science application. All models are not made equal.

Data Science 110

Data Science Statistics Recreation/Entertainment Predictive Analytics 110

IBM Big Data Hub

JUNE 29, 2023

This information is then used to build predictive models of an asset’s performance over time and help spot potential problems before they arise. One of the ways maintenance managers refine and improve predictive analytics to increase asset reliability is through the creation of a digital twin.

Cost-Benefit 85

Cost-Benefit Measurement Management Predictive Analytics 85

Smarten

AUGUST 21, 2019

Assisted Predictive Modeling – These tools enable the average business user to leverage sophisticated predictive algorithms without requiring statistical or data science skills. Users can highlight trends and patterns, test hypotheses and theories to reduce business risk, and easily predict and forecast results.

Analytics 51

Analytics Predictive Modeling Visualization Statistics 51

DataRobot

JUNE 4, 2021

For instance, if you’re not completely sure how to join your datasets or lack the time to manually generate and test hundreds of derived features, DataRobot’s powerful automation can help. But the power of enriching data for improved predictive models extends to nearly all industries and use cases.

Forecasting 52

Forecasting Machine Learning Predictive Modeling Modeling 52

CIO Business Intelligence

OCTOBER 13, 2023

Responsibilities include building predictive modeling solutions that address both client and business needs, implementing analytical models alongside other relevant teams, and helping the organization make the transition from traditional software to AI infused software.

Deep Learning 143

Deep Learning Machine Learning Modeling Software 143

Birst BI

OCTOBER 30, 2018

A personal crystal ball that predicts your days ahead is what financial services firms everywhere want. Every day, these companies pose questions such as: Will this new client provide a good return on investment, relative to the potential risk? Is this existing client a termination risk? Will this next trade return a profit?

Predictive Analytics 73

Predictive Analytics Descriptive Analytics Analytics Metrics 73

Smarten

JANUARY 31, 2019

Put simply, business Intelligence uses historical data to reveal where the business has been, and managers can use this data to predict competitive response and discover what is changing in customer buying behavior and in sales.

Business Intelligence 40

Business Intelligence Analytics Predictive Modeling Forecasting 40

Smarten

JANUARY 8, 2019

Assisted predictive modeling suggests techniques to analyze data that will result in the right outcome for the goals of the analysis. Business users get support for day-to-day decisions and can quickly and easily test theories and hypotheses in a risk-free environment.

Analytics 40

Analytics Predictive Modeling Visualization Business Analysis 40

IBM Big Data Hub

JULY 6, 2023

Some examples of data science use cases include: An international bank uses ML-powered credit risk models to deliver faster loans over a mobile app. An AI-based medical assessment platform analyzes medical records to determine a patient’s risk of stroke and predict treatment plan success rates.

Machine Learning 58

Machine Learning Data Science Statistics Deep Learning 58

Smarten

JUNE 27, 2018

For example, if an outlier indicates a risk or a mistake, that outlier should be identified and the risk or mistake should be addressed.

IT 40

IT Business Intelligence Predictive Modeling Testing 40

IBM Big Data Hub

FEBRUARY 14, 2024

Automotive With applications of AI, automotive manufacturers are able to more effectively predict and adjust production to respond to changes in supply and demand. They can streamline workflows to increase efficiency and reduce time-consuming tasks and the risk of error in production, support, procurement and other areas.

Cost-Benefit 87

Cost-Benefit Insurance Unstructured Data Machine Learning 87

Smarten

JUNE 28, 2018

Business Problem: A bank wants to group loan applicants into high/medium/low risk based on attributes such as loan amount, monthly installments, employment tenure, the number of times the applicant has been delinquent in other payments, annual income, debt to income ratio etc. Use Case – 1. Use Case – 2.

IT 40

IT Business Intelligence Predictive Modeling Risk 40

DataRobot Blog

JUNE 23, 2022

Your artificial intelligence (AI) system has given you this “predicted feature” in addition to what you already know about the applicant. Business rules set by the credit committee to control business risk and the loan portfolio also constrain accepting this application and advancing the loan. Automate and Scale Your Decisions.

Predictive Modeling 59

Predictive Modeling Modeling Data Science Measurement 59

The Unofficial Google Data Science Blog

DECEMBER 4, 2019

Done right, strategic forecasts can provide insights to decision makers on trends, incorporate forward-looking knowledge of product plans and technology roadmaps when relevant, expose the risks and biases of relying on any one forecasting methodology, and invite input from stakeholders on the uncertainty ranges.

Forecasting 113

Forecasting Data Science Statistics Uncertainty 113

IBM Big Data Hub

OCTOBER 16, 2023

Banks and other financial institutions train ML models to recognize suspicious online transactions and other atypical transactions that require further investigation. Banks and other lenders use ML classification algorithms and predictive models to determine who they will offer loans to. Many stock market transactions use ML.

Machine Learning 51

Machine Learning Marketing Forecasting Modeling 51

Domino Data Lab

AUGUST 1, 2021

This dataset classifies customers based on a set of attributes into two credit risk groups – good or bad. This is because in this post we are not after a state-of-the-art classifier, but want to rather focus on the interpretation of the black-box model fitted against the sample dataset. random_state=seed) y_train.value_counts().

Modeling 139

Modeling Deep Learning Machine Learning Knowledge Discovery 139

Smarten

JUNE 25, 2018

Loan applicants in a bank might be grouped as low, medium, and high risk applicants based on applicant age, annual income, employment tenure, loan amount, the number of times a payment is delinquent etc. About Smarten.

Enterprise 40

Enterprise IT Business Intelligence Predictive Modeling 40

Smarten

JUNE 25, 2018

Business Problem: A stock broker wants to analyze the price volatility of a stock as a measure of risk. Business Benefit: By analyzing the standard deviation or variance, one can measure the risk associated with a particular stock in terms of price fluctuations. 5) Standard Deviation/Variance. 6) Skewness/Histogram.

Statistics 40

Statistics Enterprise Measurement Business Intelligence 40

Smarten

JUNE 29, 2018

Based on these labels, the bank can easily make a decision on whether to give a loan to an applicant and the credit limit and interest rate each applicant is eligible for based on the amount of risk involved. Use Case – 2.

IT 40

IT Business Intelligence Predictive Modeling Statistics 40

Sisense

SEPTEMBER 25, 2020

Linear regression is a form of supervised learning (or predictive modeling). In supervised learning, the dependent variable is predicted from the combination of independent variables. When a single independent variable is used to predict the value of a dependent variable, it’s called simple linear regression. Clustering.

Statistics 98

Statistics Predictive Modeling Modeling Machine Learning 98

Smarten

JULY 10, 2018

Independent Samples T Test: What is the Independent Samples T Test Method of Analysis and How Can it Benefit an Organization? Use Case(s): Predict if loan default based on attributes of applicant; predict likelihood of successful treatment of new patient based on patient attributes and more.

Analytics 40

Analytics Forecasting Statistics Sales 40