O'Reilly on Data

DECEMBER 12, 2019

For all the excitement about machine learning (ML), there are serious impediments to its widespread adoption. Not least is the broadening realization that ML models can fail. And that’s why model debugging, the art and science of understanding and fixing problems in ML models, is so critical to the future of ML.

Machine Learning 187

Machine Learning Modeling Testing Risk Management 187

Domino Data Lab

AUGUST 6, 2021

In the world of machine learning (ML) and artificial intelligence (AI), governance is a lifelong pursuit. All models require testing and auditing throughout their deployment and, because models are continually learning, there is always an element of risk that they will drift from their original standards.

Machine Learning 145

Machine Learning Modeling Testing Risk 145

Analytics Vidhya

DECEMBER 4, 2022

When too much risk is restricted to very few players, it is considered as a notable failure of the risk management framework. […]. The post XAI: Accuracy vs Interpretability for Credit-Related Models appeared first on Analytics Vidhya.

Modeling 358

Modeling Risk Management Risk Management 358

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. What is a model?

Risk 111

Risk Modeling IT Risk Management 111

DataRobot Blog

MAY 10, 2022

Addressing the Key Mandates of a Modern Model Risk Management Framework (MRM) When Leveraging Machine Learning . The regulatory guidance presented in these documents laid the foundation for evaluating and managing model risk for financial institutions across the United States.

Risk 64

Risk Modeling Machine Learning Data Quality 64

CIO Business Intelligence

SEPTEMBER 8, 2022

Machine learning (ML) is a commonly used term across nearly every sector of IT today. This article will share reasons why ML has risen to such importance in cybersecurity, share some of the challenges of this particular application of the technology and describe the future that machine learning enables.

Machine Learning 142

Machine Learning Internet of Things IoT Modeling 142

Analytics Vidhya

MARCH 4, 2024

Introduction In a significant development, the Indian government has mandated tech companies to obtain prior approval before deploying AI models in the country.

Modeling 230

Modeling Risk Technology Analytics 230

O'Reilly on Data

MAY 21, 2019

Companies successfully adopt machine learning either by building on existing data products and services, or by modernizing existing models and algorithms. I will highlight the results of a recent survey on machine learning adoption, and along the way describe recent trends in data and machine learning (ML) within companies.

Machine Learning 234

Machine Learning Technology Deep Learning Data Science 234

Domino Data Lab

MARCH 11, 2021

Machine learning (ML) frameworks are interfaces that allow data scientists and developers to build and deploy machine learning models faster and easier. Machine learning is used in almost every industry, notably finance , insurance , healthcare , and marketing. How to choose the right ML Framework.

Machine Learning 143

Machine Learning Deep Learning Insurance Modeling 143

Smart Data Collective

DECEMBER 9, 2020

When most people consider the merits of machine learning, they typically think about its applications from a capitalist standpoint. There are countless ways that business owners are using machine learning advances to pad their bottom lines. They learn to identify numerous risk factors and alert the driver.

Machine Learning 141

Machine Learning Risk Insurance Predictive Analytics 141

DataRobot Blog

JUNE 27, 2022

Monitoring Modern Machine Learning (ML) Methods In Production. In our previous two posts, we discussed extensively how modelers are able to both develop and validate machine learning models while following the guidelines outlined by the Federal Reserve Board (FRB) in SR 11-7. Monitoring Model Metrics.

Risk 59

Risk Modeling Metrics Measurement 59

Cloudera

MAY 19, 2021

In the previous blog post in this series, we walked through the steps for leveraging Deep Learning in your Cloudera Machine Learning (CML) projects. RAPIDS brings the power of GPU compute to standard Data Science operations, be it exploratory data analysis, feature engineering or model building. Introduction.

Machine Learning 137

Machine Learning Data Science Data Lake Modeling 137

IBM Big Data Hub

OCTOBER 16, 2023

As more businesses use AI systems and the technology continues to mature and change, improper use could expose a company to significant financial, operational, regulatory and reputational risks. It includes processes that trace and document the origin of data, models and associated metadata and pipelines for audits.

Risk 73

Risk Modeling Management Metadata 73

Smart Data Collective

JUNE 24, 2023

Fortunately, new advances in machine learning technology can help mitigate many of these risks. Therefore, you will want to make sure that your cryptocurrency wallet or service is protected by machine learning technology. What are Crypto Wallets and Can Machine Learning Actually Help Keep Them Safe?

Machine Learning 77

Machine Learning Broadcasting Risk Data mining 77

DataRobot Blog

MAY 26, 2022

Validating Modern Machine Learning (ML) Methods Prior to Productionization. Last time , we discussed the steps that a modeler must pay attention to when building out ML models to be utilized within the financial institution. Validating Machine Learning Models.

Risk 52

Risk Modeling Metrics Business Objectives 52

O'Reilly on Data

JUNE 19, 2019

A look at the landscape of tools for building and deploying robust, production-ready machine learning models. Our surveys over the past couple of years have shown growing interest in machine learning (ML) among organizations from diverse industries. Model development. Model governance.

Modeling 193

Modeling Machine Learning Testing Metrics 193

IBM Big Data Hub

MAY 4, 2020

Many financial firms are increasing their use of AI models because they can represent the real world more accurately, and they can deliver better projections than traditional, rule-based models. But some AI models can add complexity and risk.

Risk Management 83

Risk Management Risk Modeling Management 83

Smart Data Collective

OCTOBER 13, 2021

It’s similar to prices – price optimization through machine learning is a great tool to grow your revenue. What can you learn from real-market examples? Figuring out the best pricing model can be tricky. That’s where machine learning algorithms come into place. How exactly? What product generates more money?

Machine Learning 125

Machine Learning Optimization Software Modeling 125

CIO Business Intelligence

JUNE 9, 2023

Elisabeth Stjernstoft, CIO at Swedish energy giant Ellevio, agrees that there’s a risk of using biased data that’s not representative of the customer group or population being looked at. “It We have to look at the data on which the model is trained, but also at how the algorithms are designed and the selection of functions.

Risk 103

Risk Machine Learning Modeling Technology 103

Smart Data Collective

OCTOBER 8, 2021

Learn how genetic algorithms and machine learning can help hedge fund organizations manage a business. This article looks at how genetic algorithms (GA) and machine learning (ML) can help hedge fund organizations. Modern machine learning and back-testing; how quant hedge funds use it. Pre-train tests.

Machine Learning 113

Machine Learning Testing Strategy Modeling 113

CIO Business Intelligence

SEPTEMBER 29, 2022

New York-based insurance provider Travelers, with 30,000 employees and 2021 revenues of about $35 billion, is in the business of risk. On the role of the Chief Data Officer: Due to the nature of our business, Travelers has always used data analytics to assess and price risk. s SVP and chief data & analytics officer, has a crowâ??s

Machine Learning 129

Machine Learning Insurance Technology Software 129

KDnuggets

MARCH 23, 2022

How sound risk management acts as a catalyst to building successful AI/ML models.

Risk Management 108

Risk Management Risk Modeling Management 108

Smart Data Collective

JULY 8, 2021

It is important to be informed about the potential benefits of machine learning as a consumer. You might have access to a number of websites that use AI technology to help save money, get new financing opportunities and avoid serious financial risks. There are a number of online machine learning tools that can help you.

Machine Learning 143

Machine Learning Management Finance Cost-Benefit 143

DataRobot Blog

JANUARY 17, 2023

Recently, a prospective customer asked me how I reconcile the fact that DataRobot has multiple very successful investment banks using DataRobot to enhance the P&L of their trading businesses with my comments that machine learning models aren’t always great at predicting financial asset prices. For price discovery (e.g.,

Machine Learning 87

Machine Learning Marketing Experimentation Modeling 87

IBM Big Data Hub

JULY 6, 2023

While data science and machine learning are related, they are very different fields. In a nutshell, data science brings structure to big data while machine learning focuses on learning from the data itself. What is machine learning? This post will dive deeper into the nuances of each field.

Machine Learning 56

Machine Learning Data Science Statistics Deep Learning 56

Smart Data Collective

NOVEMBER 25, 2020

Machine learning is transforming the financial sector more than anybody could have ever predicted. One of the most significant changes brought by advances in machine learning is with the loan underwriting process. Towards Data Science analyzed several dozen papers on the use of machine learning in loan scoring.

Machine Learning 144

Machine Learning Risk Predictive Analytics Forecasting 144

Cloudera

JUNE 29, 2023

To ensure the stability of the US financial system, the implementation of advanced liquidity risk models and stress testing using (MI/AI) could potentially serve as a protective measure. To improve the way they model and manage risk, institutions must modernize their data management and data governance practices.

Data Architecture 84

Data Architecture Risk Management Risk Management 84

Smart Data Collective

JUNE 3, 2021

Many different industries are becoming more reliant on machine learning. The insurance industry is among those that has found new opportunities to take advantage of machine learning technology. Many of the applications of big data for insurance companies will be realized with machine learning technology.

Insurance 98

Insurance Machine Learning Cost-Benefit Risk 98

O'Reilly on Data

MARCH 20, 2019

Apply fair and private models, white-hat and forensic model debugging, and common sense to protect machine learning models from malicious actors. Like many others, I’ve known for some time that machine learning models themselves could pose security risks.

Modeling 219

Modeling Machine Learning Predictive Modeling Consulting 219

BizAcuity

APRIL 1, 2023

Anti-Money Laundering (AML) is increasingly becoming a crucial branch of risk management and fraud prevention. In fact, online casinos as an industry carries the biggest risk of money laundering. Predictive Analytics can help businesses in reducing risk (eg.

Machine Learning 52

Machine Learning Predictive Analytics Predictive Modeling Risk 52

IBM Big Data Hub

OCTOBER 16, 2023

Machine learning (ML)—the artificial intelligence (AI) subfield in which machines learn from datasets and past experiences by recognizing patterns and generating predictions—is a $21 billion global industry projected to become a $209 billion industry by 2029. Many stock market transactions use ML.

Machine Learning 49

Machine Learning Marketing Forecasting Modeling 49

BizAcuity

JANUARY 18, 2023

Anti-Money Laundering (AML) is increasingly becoming a crucial branch of risk management and fraud prevention. In fact, online casinos as an industry carries the biggest risk of money laundering. How Machine Learning Helps Detect and Prevent AML. Predictive Analytics can help businesses in reducing risk (eg.

Machine Learning 52

Machine Learning Predictive Analytics Predictive Modeling Risk 52

IBM Big Data Hub

MAY 1, 2023

This wave of AI is attributable to what are known as foundation models. What are foundation models? As the name suggests, foundation models can be the foundation for many kinds of AI systems. Using machine learning techniques, these models apply information learned about one situation to another situation.

Modeling 97

Modeling Risk Machine Learning Technology 97

Domino Data Lab

AUGUST 6, 2021

Model Risk Management is about reducing bad consequences of decisions caused by trusting incorrect or misused model outputs. Systematically enabling model development and production deployment at scale entails use of an Enterprise MLOps platform, which addresses the full lifecycle including Model Risk Management.

Risk Management 64

Risk Management Risk Modeling Enterprise 64

CIO Business Intelligence

SEPTEMBER 26, 2023

While scoping and modeling the project, IWB relied on support from SAP’s Global Center of Excellence and Customer Advisory, providing both business and application expertise to organizations engaged in SAP implementations and optimizing existing ones. The problem was that the smart meters were only feeding their data once a day.

Machine Learning 101

Machine Learning Forecasting Measurement Data Warehouse 101

CIO Business Intelligence

JULY 19, 2022

Phishing/pharming, card testing, identity theft, and first-party misuse remain the most prevalent fraud attacks, each affecting more than three in 10 merchants globally, according to 2022 Global Fraud Report , by the Merchants Risk Council, Cybersource, and Verifi. 4] Cybersource, Remove the guesswork with Cybersource’s machine learning. [5]

Risk 93

Risk Machine Learning Reporting Modeling 93

BizAcuity

MAY 5, 2023

One model that is used across industries to ensure that users do not create multiple accounts is player deduplication’. Similarly, an AML model can identify patterns and anomalies based on known patterns of suspicious behavior from historical data (such as money dumping) to detect potential money laundering in real-time.

Machine Learning 52

Machine Learning Risk Modeling Data-driven 52