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. This has the added benefit of often uncovering hidden patterns.

Data Analytics 135

Data Analytics Diagnostic Analytics Management Analytics 135

Smarten

MAY 16, 2019

Perhaps the greatest risk to a lending organization is that presented by loan applicants who are unprepared to fulfill the long-term obligation of paying off a loan. Advanced analytics solutions are perfect for credit unions, banks, insurance businesses, auto and real estate loan processes. Learn More: Loan Approval.

Predictive Analytics 40

Predictive Analytics Analytics Predictive Modeling Insurance 40

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

When looking at the associated care (medication, exercise, follow-up appointments, age, risk factors, etc), hospitals and doctors can gain insight into methods, techniques and treatment plans and which will result in the best outcome and reduce the number of people readmitted for this type of medical issue.

Predictive Analytics 40

Predictive Analytics Analytics Predictive Modeling Forecasting 40

Cloudera

SEPTEMBER 7, 2023

With AI, financial institutions and insurance companies now have the ability to automate or augment complex decision-making processes, deliver highly personalized client experiences, create individualized customer education materials, and match the appropriate financial and investment products to each customer’s needs.

Insurance 83

Insurance Risk Data-driven Finance 83

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

FineReport

APRIL 9, 2024

The implementation of robust healthcare data management strategies is imperative to mitigate the risks associated with data breaches and non-compliance. The integration of clinical data analysis tools empowers healthcare providers to leverage predictive analytics for proactive decision-making.

Digital Transformation 52

Digital Transformation Business Intelligence Data-driven Visualization 52

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

DataRobot Blog

MAY 25, 2022

The DataRobot AI Cloud Platform can also help identify infrastructure and buildings at risk of damage from natural disasters. The post AI for Climate Change and Weather Risk appeared first on DataRobot AI Cloud. In 2017, Hurricane Harvey struck the U.S. Gulf Coast and caused approximately $125 billion in damage. Learn more.

Risk 52

Risk Predictive Modeling Insurance Predictive Analytics 52

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 222

Modeling Machine Learning Predictive Modeling Consulting 222

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.” “Here’s our risk model. And it was good. For a few years, even. But then we hit another hurdle.

Machine Learning 247

Machine Learning Testing Modeling Cost-Benefit 247

CIO Business Intelligence

AUGUST 15, 2023

A risk-limiting audit (RLA) is one audit type used for election verification. 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 98

IT Manufacturing IoT Cost-Benefit 98

DataRobot

NOVEMBER 15, 2021

This will be further expanded to energy, life sciences, insurance, government, telecommunications and media organizations in 2022. Healthcare providers are enabled with the predictive power to reduce healthcare costs, readmissions and improve patient outcomes.

Manufacturing 52

Manufacturing Forecasting Machine Learning Data-driven 52

Smarten

JULY 10, 2018

Use Case(s): Group loan applicants into high/medium/low risk based on attributes such as loan amount, installments, or employment tenure, organize customers into groups/segments based on similar traits, product preferences and expectations 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. Risk Assessment. Better Cybersecurity.

Big Data 137

Big Data Machine Learning Finance Predictive Modeling 137

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 to be expected, as there is no reason for a perfect 50:50 separation of the good vs. bad credit risk. PDPs for the bicycle count prediction model (Molnar, 2009). 1 570 0 570 Name: credit, dtype: int64.

Modeling 139

Modeling Deep Learning Machine Learning Knowledge Discovery 139