John Wheeler

OCTOBER 20, 2020

banking, insurance and securities) measure risk on a quantitative basis. Other quantitative analysis methods are used to develop more precise predictive models to determine the potential for digital risk events, such as product/service liability, fraud or theft.

Risk Management 52

Risk Management Risk Predictive Modeling Insurance 52

Smarten

MAY 16, 2019

Advanced analytics solutions are perfect for credit unions, banks, insurance businesses, auto and real estate loan processes. When applied to the loan approval process, predictive analytics can improve productivity and optimize resources and it can decrease loan defaults and help the business to capitalize on available funds.

Predictive Analytics 40

Predictive Analytics Analytics Predictive Modeling Insurance 40

Cloudera

SEPTEMBER 7, 2023

The Danger of Black-Box AI Solutions We believe the best, most pragmatic solution for AI in financial services and insurance is what we call–“Trusted AI.” But before more is said about what this is, let’s walk through some of the issues that a financial institution needs to take into account when it considers a commercial AI service.

Insurance 78

Insurance Risk Data-driven Modeling 78

Sisense

MARCH 27, 2019

Finally, real-time BI helps better understand trends and create more accurate predictive models for organizations. By combining with historic trends, they can also create predictive models for ordering that automate time-consuming tasks. Who Uses Real-Time BI? What are the Real-Time BI Best Practices?

Business Intelligence 74

Business Intelligence Data Warehouse Predictive Modeling Visualization 74

Smarten

JULY 30, 2018

Assisted Predictive Modeling can support business users, IT staff and other users by providing recommendations in gathering, formatting and presenting data to improve the effectiveness and clarity of resulting data and reporting.

Predictive Analytics 40

Predictive Analytics Analytics Predictive Modeling Forecasting 40

DataRobot Blog

APRIL 29, 2022

With the big data revolution of recent years, predictive models are being rapidly integrated into more and more business processes. When business decisions are made based on bad models, the consequences can be severe. In 2017, additional regulation targeted much smaller financial institutions in the U.S.

Risk 111

Risk Modeling IT Risk Management 111

CIO Business Intelligence

JULY 26, 2023

We’ve been on a journey for the last six years or so to build out our platforms,” says Cox, noting that Keller Williams uses MLS, demographic, product, insurance, and geospatial data globally to fill its data lake. “We

Data Lake 97

Data Lake Digital Transformation Machine Learning Data Architecture 97

Juice Analytics

MARCH 11, 2021

Predictive models to take descriptive data and attempt to tell the future. The right product manager We’ve helped launch data products in many industries including healthcare, education, insurance, advertising and market research. She enhances data through predictive modeling and other advanced data analytics techniques.

Predictive Modeling 128

Predictive Modeling Visualization Reporting Interactive 128

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

O'Reilly on Data

MAY 18, 2020

All predictive models are wrong at times?—just As the renowned statistician George Box once quipped , “All models are wrong, but some are useful.” In the context of AI incidents, this complexity is problematic because it can make audits, debugging, and simply even understanding what went wrong nearly impossible.

Risk 359

Risk Modeling Data Processing Software 359

Birst BI

OCTOBER 30, 2018

The output of these algorithms, when used in financial services, can be anything from a customer behavior score to a prediction of future trading trends, to flagging a fraudulent insurance claim. The credit scores generated by the predictive model are then used to approve or deny credit cards or loans to customers.

Predictive Analytics 73

Predictive Analytics Descriptive Analytics Analytics Metrics 73

Smarten

JUNE 26, 2018

Finance – An organization might use this technique to Identify if demographic factors influence banking channel/product/service preference or selection of a type of term plan of an insurance etc. How Can the Chi Square Test of Association Be Used for Business Analysis?

Testing 40

Testing Statistics IT Business Intelligence 40

FineReport

APRIL 9, 2024

The integration of clinical data analysis tools empowers healthcare providers to leverage predictive analytics for proactive decision-making. Through the utilization of predictive models, clinicians can forecast patient outcomes and resource needs, enabling early intervention and personalized care delivery.

Digital Transformation 52

Digital Transformation Business Intelligence Data-driven Visualization 52

O'Reilly on Data

MARCH 20, 2019

The objective here is to brainstorm on potential security vulnerabilities and defenses in the context of popular, traditional predictive modeling systems, such as linear and tree-based models trained on static data sets. If an attacker can receive many predictions from your model API or other endpoint (website, app, etc.),

Modeling 222

Modeling Machine Learning Predictive Modeling Consulting 222

AWS Big Data

MARCH 7, 2023

These are just some of the KPIs and metrics that are key for predictive modeling of events as the game acquires new players while keeping existing users involved, engaged, and playing. User to user interactions – Invitations, gifting, chats (private and group), challenges, and so on during an event.

Analytics 68

Analytics Data Warehouse Data Lake Metadata 68

Rocket-Powered Data Science

FEBRUARY 12, 2021

The AAI report covers these industries: energy/utilities, financial/insurance, government, healthcare, industrial/manufacturing, life sciences, retail/consumer, services/consulting, technology, telecom, and transportation/airlines. AAI’s recently published “Now and Next State of RPA” report presents detailed results of that survey.

ROI 233

ROI Digital Transformation Reporting Machine Learning 233

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.” Bayesian data analysis and Monte Carlo simulations are common in finance and insurance. And it was good.

Machine Learning 247

Machine Learning Testing Modeling Cost-Benefit 247

DataRobot Blog

MAY 25, 2022

Using either the code-centric DataRobot Core or no-code Graphical User Interface (GUI), both data scientists and non-data scientists such as risk analysts, government experts, or first responders can build, compare, explain, and deploy their own models.

Risk 52

Risk Predictive Modeling Insurance Predictive Analytics 52

DataRobot

NOVEMBER 15, 2021

This will be further expanded to energy, life sciences, insurance, government, telecommunications and media organizations in 2022. With DataRobot AI Cloud for Industries, we’re excited to announce expanded partner-delivered and partner-enabled offerings for these key industries with our growing ecosystem of strategic partners.

Manufacturing 52

Manufacturing Forecasting Machine Learning Data-driven 52

CIO Business Intelligence

AUGUST 15, 2023

The Behavioral Health Acuity Risk (BHAR) model leverages a machine learning technique called random forests, which can be natively hosted in the electronic health record and updated in near-real time, with results immediately available to clinical staff.

IT 96

IT Manufacturing IoT Cost-Benefit 96

Smarten

JULY 10, 2018

Use Case(s): Determine if a product sells better in certain locations, verify if gender has an influence on purchasing decisions, Identify if demographic factors influence banking channel/product/service preference or selection of a type of term insurance plan and more.

Analytics 40

Analytics Forecasting Statistics Sales 40

Smart Data Collective

DECEMBER 12, 2020

These analytics are much more accurate and include more data that allows better predictive models to be created. These things can end up resulting in much more precision in predictions which can help to minimize the risk associated with making financial trading decisions. Better Cybersecurity.

Big Data 136

Big Data Machine Learning Finance Predictive Modeling 136

Domino Data Lab

AUGUST 20, 2019

Another example is the use of body mass index (BMI) by medical providers and insurance companies. Some of the benefits of rescaling become more prominent when we move beyond predictive modeling and start making statistical or causal claims. Numerically, these values have very different weight. Discretization.

Testing 68

Testing Modeling Interactive Measurement 68

Domino Data Lab

AUGUST 1, 2021

plot.barh(figsize=(10,12)); We see that the top 5 features with most significant influence on the prediction are: checking_account_A14: absence of a checking account status_A93: personal status and sex – single male property_A123: owns property different to real estate, savings agreement, or life insurance (e.g.

Modeling 139

Modeling Deep Learning Machine Learning Knowledge Discovery 139

Smarten

JUNE 21, 2018

Business Problem : Insurance claim manager wants to forecast policy sales for next month based on past 12 months data. Tools such as Smarten Plug n’ Play predictive analysis provide assisted predictive modeling capabilities. 2) Double Exponential Smoothing Use Case.

Forecasting 40

Forecasting Enterprise IT Predictive Modeling 40