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. How do they make vehicles safer?

IoT 110

IoT Internet of Things Testing Cost-Benefit 110

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. For predictive analytics to deliver high accuracy, a lot depends on the combination of domain knowledge and technical expertise.

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. In this modern age, each business entity is driven by data.

Big Data 142

Big Data Finance Risk Management Risk 142

O'Reilly on Data

DECEMBER 12, 2019

Not least is the broadening realization that ML models can fail. And that’s why model debugging, the art and science of understanding and fixing problems in ML models, is so critical to the future of ML. Because all ML models make mistakes, everyone who cares about ML should also care about model debugging. [1]

Machine Learning 191

Machine Learning Modeling Testing Risk Management 191

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. Predictive Analytics. Predictive Analytics can help businesses in reducing risk (eg.

Machine Learning 52

Machine Learning Predictive Analytics Predictive Modeling Risk 52

DataRobot

OCTOBER 19, 2021

By leveraging prediction and augmented decision-making, governments can address the needs of taxpayers proactively while quickly adapting to evolving circumstances. DataRobot helped combat this problem head on by applying AI to evaluate and predict resource allocation and identify the most impacted communities from a national to county level.

Forecasting 98

Forecasting Predictive Modeling Optimization Testing 98

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 127

Data Analytics Diagnostic Analytics Management Analytics 127

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. Let’s start with the models. And those experiments have paid off.

Experimentation 119

Experimentation Strategy Machine Learning Interactive 119

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

IT 40

IT Business Intelligence Predictive Modeling Testing 40

IBM Big Data Hub

JUNE 29, 2023

When a critical asset like an expensive piece of machinery or infrastructure breaks unexpectedly, it affects customers and can cost companies millions. To be successful, businesses need to have clear, real-time visibility into the condition of their assets and a plan to keep them running smoothly and make repairs quickly when things break.

Cost-Benefit 86

Cost-Benefit Measurement Management Predictive Analytics 86

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

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

Smarten

APRIL 17, 2018

How Can Assistive Predictive Modeling Help My Business Users? In order to achieve goals and objectives and perform well in their respective roles, business users need to analyze data, draw conclusions, predict results and help the organization achieve its goals. If you want to find out more, Contact Us.

Predictive Analytics 40

Predictive Analytics Predictive Modeling Analytics Forecasting 40

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. What is Hierarchical Clustering? Use Case – 1.

IT 40

IT Business Intelligence Predictive Modeling Risk 40

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.

Machine Learning 76

Machine Learning Data Integration Marketing Predictive Modeling 76

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. Careers, Data Scientist, Generative AI, Hiring, IT Jobs

Deep Learning 143

Deep Learning Machine Learning Modeling Software 143

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. But there should be much difference between an object in group 1 and group 2. Use Case – 2. About Smarten.

Enterprise 40

Enterprise IT Business Intelligence Predictive Modeling 40

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

Smarten

JUNE 29, 2018

Binary Logistic Regression Classification makes use of one or more predictor variables that may be either continuous or categorical to predict the target variable classes. In this article, we will discuss the Binary Logistic Regression Classification method of analysis, and how it can be used in business. But the p value for Age is >0.05

IT 40

IT Business Intelligence Predictive Modeling Statistics 40

DataRobot

JUNE 4, 2021

is good, we want to improve the overall accuracy of our machine learning model. 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. Although the RMSE of 11.6

Forecasting 52

Forecasting Machine Learning Predictive Modeling Modeling 52

IBM Big Data Hub

JULY 6, 2023

Areas making up the data science field include mining, statistics, data analytics, data modeling, machine learning modeling and programming. Machine learning can then “learn” from the data to create insights that improve performance or inform predictions. This post will dive deeper into the nuances of each field.

Machine Learning 59

Machine Learning Data Science Statistics Deep Learning 59

Smarten

JUNE 27, 2018

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

Business Analytics 40

Business Analytics Analytics IT Business Intelligence 40

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

IBM Big Data Hub

FEBRUARY 14, 2024

Other uses include Netflix offering viewing recommendations powered by models that process data sets collected from viewing history; LinkedIn uses ML to filter items in a newsfeed, making employment recommendations and suggestions on who to connect with; and Spotify uses ML models to generate its song recommendations.

Cost-Benefit 88

Cost-Benefit Insurance Unstructured Data Machine Learning 88

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 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

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. Well, that might depend on the rules.

Predictive Modeling 59

Predictive Modeling Modeling Data Science Measurement 59

Smarten

JUNE 27, 2018

In this way, the riskier customers are identified easily and bank can avert the risk of delinquencies. Business Problem: Based on customer attributes and past online shopping behavioral data, an online retail giant wants to predict the future purchases of customers. Use Case – 2.

IT 40

IT Business Intelligence Predictive Modeling Statistics 40

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

DataKitchen

SEPTEMBER 21, 2023

In the context of Data in Place, validating data quality automatically with Business Domain Tests is imperative for ensuring the trustworthiness of your data assets. Running these automated tests as part of your DataOps and Data Observability strategy allows for early detection of discrepancies or errors. What is Data in Place?

Testing 176

Testing Data Quality Predictive Modeling Metrics 176

Smarten

JUNE 29, 2018

Credit/Loan Approval Analysis – Given a list of client transactional attributes, the business can predict whether a client will default on a bank loan. Weather Prediction – Based on temperature, humidity, pressure etc., an organization can predict if it will be rainy/sunny/cold. Use Case – 2.

Enterprise 40

Enterprise Business Intelligence Predictive Modeling Statistics 40

CIO Business Intelligence

AUGUST 15, 2023

Shamim Mohammad, CIO, CarMax CarMax That volume created a Sisyphean task for the company’s content writers, as they struggled to provide up-to-date information by make, model, and year for each vehicle in the company’s constantly changing inventory. A risk-limiting audit (RLA) is one audit type used for election verification.

IT 91

IT Manufacturing IoT Cost-Benefit 91

O'Reilly on Data

JANUARY 17, 2023

A common task for a data scientist is to build a predictive model. If it does, you suspect that the variable you’re trying to predict has mixed in with the variables used to predict it. You might say that the outcome of this exercise is a performant predictive model. That’s sort of true.

Machine Learning 325

Machine Learning Predictive Modeling Software Modeling 325

O'Reilly on Data

SEPTEMBER 19, 2023

Stage 2: Machine learning models Hadoop could kind of do ML, thanks to third-party tools. While data scientists were no longer handling Hadoop-sized workloads, they were trying to build predictive models on a different kind of “large” dataset: so-called “unstructured data.” Cloud computing? BI is useful.

Machine Learning 247

Machine Learning Testing Modeling Cost-Benefit 247

DataRobot Blog

JULY 22, 2022

At many organizations, the current framework focuses on the validation and testing of new models, but risk managers and regulators are coming to realize that what happens after model deployment is at least as important. Legacy Models. No predictive model — no matter how well-conceived and built — will work forever.

Modeling 105

Modeling Risk Management Testing Risk 105