O'Reilly on Data

DECEMBER 12, 2019

That’s where model debugging comes in. Model debugging is an emergent discipline focused on finding and fixing problems in ML systems. In addition to newer innovations, the practice borrows from model risk management, traditional model diagnostics, and software testing. Sensitivity analysis.

Machine Learning 282

Machine Learning Modeling Testing Risk Management 282

CIO Business Intelligence

MAY 9, 2024

And they should have a proficiency in data science and analytics to effectively leverage data-driven insights and develop AI models. This includes skills in statistical analysis, data visualization, and predictive modeling. That helps them ensure that AI initiatives adhere to legal and ethical standards.

Risk Management 138

Risk Management Cost-Benefit Risk Technology 138

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. This provides a great amount of benefit, but it also exposes institutions to greater risk and consequent exposure to operational losses.

Risk 111

Risk Modeling IT Risk Management 111

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

BizAcuity

APRIL 1, 2023

Anti-Money Laundering (AML) is increasingly becoming a crucial branch of risk management and fraud prevention. Widely used to discover trends, patterns, check assumptions and spot anomalies or outliers, EDA involves a variety of techniques including statistical analysis, and machine learning to gain a better understanding of data.

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. Widely used to discover trends, patterns, check assumptions and spot anomalies or outliers, EDA involves a variety of techniques including statistical analysis, and machine learning to gain a better understanding of data.

Machine Learning 52

Machine Learning Predictive Analytics Predictive Modeling Risk 52

O'Reilly on Data

MAY 18, 2020

And last is the probabilistic nature of statistics and machine learning (ML). Most AI models decay overtime: This phenomenon, known more widely as model decay , refers to the declining quality of AI system results over time, as patterns in new data drift away from patterns learned in training data. How Material Is the Threat?

Risk 364

Risk Modeling Data Processing Reporting 364