CIO Business Intelligence

MARCH 18, 2024

One such company has built a tool that predicts customer intent and behavior based on previous interactions and other market data. Laying the foundation To develop POC implementations, Menon and her team are establishing a lab that is expected to debut in March 2024 for testing AI tools before rollout.

Risk 133

Risk Cost-Benefit Data Processing Testing 133

Cloudera

JULY 15, 2021

For financial institutions and insurers, risk and exposure management has always been a fundamental tenet of the business. Now, risk management has become exponentially complicated in multiple dimensions. . Attendees included senior risk managers and analytics experts from financial institutions and insurance companies.

Risk 98

Risk Optimization Management Insurance 98

DataRobot

JULY 16, 2021

They all serve to answer the question, “How well can my model make predictions based on data?” In performance, the trust dimensions are the following: Data quality — the performance of any machine learning model is intimately tied to the data it was trained on and validated against. Operations.

Machine Learning 98

Machine Learning Uncertainty Modeling Measurement 98

DataRobot Blog

MAY 26, 2022

To start with, SR 11-7 lays out the criticality of model validation in an effective model risk management practice: Model validation is the set of processes and activities intended to verify that models are performing as expected, in line with their design objectives and business uses.

Risk 52

Risk Modeling Metrics Business Objectives 52

O'Reilly on Data

JULY 15, 2019

In recent posts, we described requisite foundational technologies needed to sustain machine learning practices within organizations, and specialized tools for model development, model governance, and model operations/testing/monitoring. Note that the emphasis of SR 11-7 is on risk management.). Sources of model risk.

Machine Learning 38

Machine Learning Management Enterprise Risk Management 38

DataRobot Blog

JULY 21, 2022

Inquire whether there is sufficient data to support machine learning. Document assumptions and risks to develop a risk management strategy. Discuss how the stakeholders want to interact with the machine learning model after it is built. Perform data quality checks and develop procedures for handling issues.

Machine Learning 98

Machine Learning Metrics Modeling Testing 98

DataRobot Blog

DECEMBER 20, 2022

In this article, we’ll first take a closer look at the concept of Real Estate Data Intelligence and the potential of AI to become a game changer in this niche. We’ll then empirically test this assumption based on an example of real estate asset assessment. You can understand the data and model’s behavior at any time.

Management 59

Management Machine Learning Optimization Modeling 59