Predictive Modeling, Risk Management and Testing

Why you should care about debugging machine learning models

O'Reilly on Data

DECEMBER 12, 2019

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. Interpretable ML models and explainable ML. Sensitivity analysis.

Machine Learning

Machine Learning Modeling Testing Risk Management

How Big Data Impacts The Finance And Banking Industries

Smart Data Collective

SEPTEMBER 17, 2020

Financial institutions such as banks have to adhere to such a practice, especially when laying the foundation for back-test trading strategies. Here are a few of the advantages of Big Data in the banking and financial industry: Improvement in risk management operations. The Role of Big Data. Engaging the Workforce.

Big Data

Big Data Finance Risk Management Risk

Minding Your Models

DataRobot Blog

JULY 22, 2022

That requires a good model governance framework. 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. Future Models.

Modeling

Modeling Risk Management Testing Machine Learning

Webinars

Data Talks, CFOs Listen: Why Analytics Are Key To Better Spend Management

Mastering Apache Airflow® 3.0: What’s New (and What’s Next) for Data Orchestration

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Transforming IT from cost center to catalyst

CIO Business Intelligence

SEPTEMBER 10, 2024

We envisioned harnessing this data through predictive models to gain valuable insights into various aspects of the industry. This included predicting political outcomes, such as potential votes on pipeline extensions, as well as operational issues like predicting the failure of downhole submersible pumps, which can be costly to repair.

IT

IT Risk Risk Management Predictive Modeling

What is Model Risk and Why Does it Matter?

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. Model Validation – Prior to the use of a model (i.e.,

Risk

Risk Modeling IT Risk Management

Generative AI copilots: What’s hype and where to drive results

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

Advertising Cost-Benefit Experimentation Reporting

CDOs: Your AI is smart, but your ESG is dumb. Here’s how to fix it

CIO Business Intelligence

MARCH 19, 2025

Highlight how ESG metrics can enhance risk management, regulatory compliance and brand reputation. Predictive modeling can help companies optimize energy consumption, while AI-driven insights can identify supply chain inefficiencies that lead to excessive waste.

IT

IT Data Governance Data-driven Metrics

11 most in-demand gen AI jobs companies are hiring for

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

Deep Learning Machine Learning Modeling Software

Financial IT leaders prep for a quantum-fueled future

CIO Business Intelligence

AUGUST 23, 2023

“You’re able to select a smaller number of stocks with predictive return and lower operational and transaction costs, which ultimately means that you can reduce the variability and more accurately predict returns,” Muthukrishnan says. It’s important for us to test the technology and be ready,” Muthukrishnan says.

IT

IT Machine Learning Risk Optimization

How to Leverage Machine Learning for AML Compliance

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

Machine Learning Predictive Analytics Predictive Modeling Risk

How to Leverage Machine Learning for AML Compliance

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

Machine Learning Predictive Analytics Predictive Modeling Risk

How Financial Services and Insurance Streamline AI Initiatives with a Hybrid Data Platform

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. Formalize ethics and bias testing. Practice real-time risk management. Automate wealth management.

Insurance

Insurance Risk Data-driven Finance

What to Do When AI Fails

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

Risk Modeling Data Processing Reporting

Predicting Movie Profitability and Risk at the Pre-production Phase

Insight

FEBRUARY 19, 2020

I held out 20% of this as a test set and used the remainder for training and validation. Feature Selection and Engineering Most of the inputs to my model were taken either as is from the data source, or with minimal processing. Scatterplot of the predicted ROI vs. the true ROI for the hold-out test set. A New Hope ).

Risk

Risk ROI Modeling Metrics

Data Leaders Brief

Why you should care about debugging machine learning models

How Big Data Impacts The Finance And Banking Industries

Webinars

Trending Sources

Minding Your Models

Webinars

Transforming IT from cost center to catalyst

What is Model Risk and Why Does it Matter?

Generative AI copilots: What’s hype and where to drive results

CDOs: Your AI is smart, but your ESG is dumb. Here’s how to fix it

11 most in-demand gen AI jobs companies are hiring for

Financial IT leaders prep for a quantum-fueled future

How to Leverage Machine Learning for AML Compliance

How to Leverage Machine Learning for AML Compliance

How Financial Services and Insurance Streamline AI Initiatives with a Hybrid Data Platform

What to Do When AI Fails

Predicting Movie Profitability and Risk at the Pre-production Phase

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