Smarten

MAY 17, 2019

These techniques can be beneficial for infrastructure planning, construction, highway planning and management, government, agriculture, weather, travel and city planning, and can help the business to plan for resources, locations, supply chain, marketing, inventory, pricing, risk management, maintenance and other planning activities.

Predictive Analytics 60

Predictive Analytics Analytics Predictive Modeling Risk Management 60

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.

Advertising 124

Advertising Cost-Benefit Experimentation Reporting 124

John Wheeler

OCTOBER 20, 2020

We continue our “20 for 20” theme this year by highlighting the integrated risk management (IRM) critical capabilities and top 20 software functions / features. Some quantitative analysis supports cyber/IT risk requirements driven by the use of cyberinsurance. Incident Management.

Risk Management 52

Risk Management Risk Insurance Predictive 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

IBM Big Data Hub

OCTOBER 16, 2023

It includes processes that trace and document the origin of data, models and associated metadata and pipelines for audits. It encompasses risk management and regulatory compliance and guides how AI is managed within an organization. Foundation models can use language, vision and more to affect the real world.

Risk 70

Risk Modeling Management Metadata 70

CIO Business Intelligence

AUGUST 23, 2023

In markets, time-series predictions help understand how markets will behave and evaluate correlation between different types of assets. And in risk management, quantum can be deployed “for Monte Carlo simulations or understanding anti-money laundering or compliance issues that might be happening within your bank,” Shete says.

IT 97

IT Machine Learning Risk Optimization 97

BizAcuity

APRIL 1, 2023

Anti-Money Laundering (AML) is increasingly becoming a crucial branch of risk management and fraud prevention. The algorithms can detect anomalies in the transactional data and helps to identify high-risk customers and transactions that may be linked to money laundering activities.

Machine Learning 52

Machine Learning Predictive Analytics Predictive Modeling Risk 52

BizAcuity

JANUARY 18, 2023

Anti-Money Laundering (AML) is increasingly becoming a crucial branch of risk management and fraud prevention. The algorithms can detect anomalies in the transactional data and helps to identify high-risk customers and transactions that may be linked to money laundering activities. These include-.

Machine Learning 52

Machine Learning Predictive Analytics Predictive Modeling Risk 52

FineReport

APRIL 9, 2024

From advanced analytics to predictive modeling, the evolving landscape of business intelligence is revolutionizing how data is processed and leveraged for actionable insights. Proactive Risk Management : BI tools enable organizations to proactively identify potential risks through predictive modeling and trend analysis.

Business Intelligence 52

Business Intelligence Software Data-driven Predictive Modeling 52

Cloudera

SEPTEMBER 7, 2023

AI-ify risk management. Leverage ML/AI to refine risk models, incorporating data from diverse sources, and predicting outcomes based on market sentiment, climate data, etc. Practice real-time risk management. Automate wealth management. Simplify regulatory compliance.

Insurance 83

Insurance Risk Data-driven Finance 83

IBM Big Data Hub

MAY 17, 2024

Risk management and fraud detection Traditional AI and machine learning excel in processing vast volumes of B2C and B2B payments, enabling businesses to identify and respond to suspicious trends swiftly. Generative AI further enhances these capabilities by developing predictive models that anticipate changes in payment regulations.

B2B 64

B2B Interactive Optimization Data-driven 64

IBM Big Data Hub

AUGUST 28, 2023

Text representation In this stage, you’ll assign the data numerical values so it can be processed by machine learning (ML) algorithms, which will create a predictive model from the training inputs. Crisis management and risk management: Text mining serves as an invaluable tool for identifying potential crises and managing risks.

Data mining 53

Data mining Machine Learning Deep Learning Marketing 53

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.” Broadly speaking, materiality is the product of the impact of a model error times the probability of that error occuring. just hopefully less so than humans.

Risk 364

Risk Modeling Data Processing Reporting 364

Insight

FEBRUARY 19, 2020

I wanted to note that my technique to predict ROI and ROI uncertainty is designed to supplement but not supplant the creative decision-making process. This method can also be applied to risk management in other domains as well.

Risk 67

Risk ROI Modeling Metrics 67

Jet Global

AUGUST 13, 2024

Demand Forecasting: Machine learning analyzes sales data to predict future demand, leading to better inventory management and resource allocation. Risk Management: AI-powered anomaly detection and predictive modeling identify potential supply chain disruptions, allowing for proactive risk management.

Manufacturing 52

Manufacturing Reporting Optimization Cost-Benefit 52

Jet Global

MAY 9, 2024

Machine Learning Pipelines : These pipelines support the entire lifecycle of a machine learning model, including data ingestion , data preprocessing, model training, evaluation, and deployment. By integrating predictive models into data pipelines, organizations can benefit from actionable insights that drive strategic planning.

Data Lake 52

Data Lake Data Warehouse Business Intelligence Machine Learning 52