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 141

Big Data Finance Risk Management Risk 141

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

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 97

IT Machine Learning Risk Optimization 97

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

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 135

Data Analytics Diagnostic Analytics Management Analytics 135

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 130

Experimentation Strategy Machine Learning Interactive 130

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

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

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 88

Cost-Benefit Measurement Management Predictive Analytics 88

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 60

Machine Learning Data Science Statistics Deep Learning 60

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 Use?

Testing 176

Testing Data Quality Predictive Modeling Metrics 176

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 89

Cost-Benefit Insurance Unstructured Data Machine Learning 89

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

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

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 50

Machine Learning Marketing Forecasting Modeling 50

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 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

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

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 for each applicant, based on the amount of risk. Use Case – 2.

Enterprise 40

Enterprise Business Intelligence Predictive Modeling Statistics 40

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

DataRobot Blog

JULY 22, 2022

That requires a good model governance framework. 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. Future Models.

Modeling 105

Modeling Risk Management Testing Risk 105

O'Reilly on Data

JANUARY 17, 2023

Building Models. A common task for a data scientist is to build a predictive model. You’ll try this with a few other algorithms, and their respective tuning parameters–maybe even break out TensorFlow to build a custom neural net along the way–and the winning model will be the one that heads to production.

Machine Learning 325

Machine Learning Predictive Modeling Software Modeling 325

O'Reilly on Data

SEPTEMBER 19, 2023

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.” You can see a simulation as a temporary, synthetic environment in which to test an idea. And it was good.

Machine Learning 247

Machine Learning Testing Modeling Cost-Benefit 247

CIO Business Intelligence

MARCH 5, 2024

CIOs must also partner with CISOs, legal, human resources, and business leaders to build awareness of policies and develop a generative AI risk management strategy. CIOs and IT leaders are at the center and must decide what copilots to test, who should receive access, and whether experiments are delivering business value.

Cost-Benefit 124

Cost-Benefit Advertising Experimentation Software 124

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 72

Management Risk Optimization Predictive Modeling 72