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

DataRobot Blog

JULY 22, 2022

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. No predictive model — no matter how well-conceived and built — will work forever.

Modeling 105

Modeling Risk Management Testing Risk 105

CIO Business Intelligence

MARCH 19, 2025

Most data management conferences and forums focus on AI, governance and security, with little emphasis on ESG-related data strategies. If sustainability-related data projects fail to demonstrate a clear financial impact, they risk being deprioritized in favor of more immediate business concerns.

IT 59

IT Data Governance Data-driven Metrics 59

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. Beyond assessing risk from a qualitative perspective, companies in many industries (e.g.,

Risk Management 52

Risk Management Risk Insurance Predictive Modeling 52

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

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-. REFERENCES. [1]

Machine Learning 52

Machine Learning Predictive Analytics Predictive Modeling Risk 52