O'Reilly on Data

DECEMBER 12, 2019

In addition to newer innovations, the practice borrows from model risk management, traditional model diagnostics, and software testing. The study of security in ML is a growing field—and a growing problem, as we documented in a recent Future of Privacy Forum report. [8]. Sensitivity analysis. Residual analysis.

Machine Learning 191

Machine Learning Modeling Testing Risk Management 191

CIO Business Intelligence

MARCH 24, 2023

They must be accompanied by documentation to support compliance-based and operational auditing requirements. Key features include a collaborative business glossary, the ability to visualize data lineage, and generate data quality measurements based on business definitions.

Data Governance 142

Data Governance Management Metadata Data Quality 142

DataRobot

JULY 16, 2021

Accuracy — this refers to a subset of model performance indicators that measure a model’s aggregated errors in different ways. Testing your model to assess its reproducibility, stability, and robustness forms an essential part of its overall evaluation. Operations.

Machine Learning 98

Machine Learning Uncertainty Modeling Measurement 98

DataRobot

AUGUST 9, 2021

General Data Protection Regulation, California Consumer Privacy Act, SR 11-7 Guidance on Model Risk Management, etc). MLOps governance is a comprehensive AI audit solution for machine learning testing and governance. It empowers enterprises to measure, monitor, and manage AI-introduced risks at scale.

Machine Learning 52

Machine Learning Risk Testing Insurance 52

Cloudera

SEPTEMBER 7, 2023

But these measures alone may not be sufficient to protect proprietary information. Even when backed by robust security measures, an external AI service is a tempting, outsized target for potential security breaches: each integration point, data transfer, or externally exposed API becomes a target for malicious actors.

Insurance 79

Insurance Risk Data-driven Finance 79

CIO Business Intelligence

APRIL 22, 2022

The exam covers topics including Scrum, Kanban, Lean, extreme programming (XP), and test-driven development (TDD). Candidates can also take a self-test to benchmark their knowledge. The exam covers project basics, project constraints, communication and change management, and project tools and documentation.

Management 111

Management Cost-Benefit Testing Risk 111

BizAcuity

APRIL 1, 2023

Anti-Money Laundering (AML) is increasingly becoming a crucial branch of risk management and fraud prevention. In this case, once a customer’s documents are scanned and uploaded, the necessary data is extracted from the key documents and then converted to machine-readable form.

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. In this case, once a customer’s documents are scanned and uploaded, the necessary data is extracted from the key documents and then converted to machine-readable form. Predictive Analytics. REFERENCES. [1]

Machine Learning 52

Machine Learning Predictive Analytics Predictive Modeling Risk 52

erwin

MARCH 12, 2020

It also highlights select enterprise architecture management suite (EAMS) vendors based on size and functionality, including erwin. The report notes six primary EA competencies in which we excel in the large vendor category: modeling, strategy translation, risk management, financial management, insights and change management.

Enterprise 85

Enterprise Reporting Management ROI 85

CIO Business Intelligence

APRIL 12, 2022

Even though Nvidia’s $40 billion bid to shake up enterprise computing by acquiring chip designer ARM has fallen apart, the merger and acquisition (M&A) boom of 2021 looks set to continue in 2022, perhaps matching the peaks of 2015, according to a report from risk management advisor Willis Towers Watson. Precisely buys PlaceIQ.

Technology 143

Technology Enterprise Consulting Software 143

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

AWS Big Data

AUGUST 14, 2023

How do we define “risk” and “value” in the context of data products, and how can we measure this? To answer questions such as these and plan accordingly, organizations must implement data product portfolio management (DPPM). Strategies for measuring value and prioritizing data products are explored later in this post.

Management 82

Management Risk Measurement Optimization 82

Grooper

OCTOBER 23, 2019

By measuring performance, they drive improvements through necessary process and workflow changes. And how willing are key people to move away from current data management practices and tools?”. Your dream of effectively managing an enterprise-wide master data model can be a reality. You must be able to measure your performance.

Metrics 59

Metrics Measurement Strategy Optimization 59

IBM Big Data Hub

JULY 7, 2023

It means physically securing servers and user devices, managing and controlling access, application security and patching, maintaining thoroughly tested, usable data backups and educating employees. Here are some data security measures that every organization should strongly consider implementing. Define sensitive data.

Risk 40

Risk Data Governance Data Lake Data-driven 40

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. They may not have been documented, tested, or actively monitored and maintained. Legacy Models.

Modeling 105

Modeling Risk Management Testing Risk 105

IBM Big Data Hub

FEBRUARY 22, 2024

But it’s equally important that they have a deep understanding of the risks and limitations of AI and how to implement the appropriate security measures and ethics guardrails. Note: These measures of responsibility must be interpretable by AI non-experts (without “mathsplaining”). This is misguided.

Metadata 101

Metadata Measurement Risk Modeling 101

DataRobot Blog

JULY 21, 2022

Outline clear metrics to measure success. Document assumptions and risks to develop a risk management strategy. Define the exact calculation for the target variable or create a couple options to test. dashes and parentheses in telephone numbers) Inconsistent units of measure (e.g.,

Machine Learning 98

Machine Learning Metrics Modeling Testing 98

DataRobot

SEPTEMBER 16, 2021

Pricing actuaries can test various advanced algorithms with minimal setup, including XGBoost, GBM, ENET, and GAM. Exposure is a measure of the unit of risk for an insurance policy. For example, a car-year is a reasonable measure for personal auto insurance, while a home-year could be an exposure for homeowners insurance.

Modeling 52

Modeling Insurance Cost-Benefit Machine Learning 52

IBM Big Data Hub

JULY 28, 2023

In response, enterprises have made vulnerability management a key component of their cyber risk management strategies. The vulnerability management lifecycle offers a formal model for effective vulnerability management programs in an ever-changing cyberthreat landscape.

Management 68

Management Metrics Risk Measurement 68

Domino Data Lab

APRIL 3, 2019

Also, while surveying the literature two key drivers stood out: Risk management is the thin-edge-of-the-wedge ?for Edge caches become crucial for managing data on its way from web servers to mobile devices. They sold off most of the company later, retaining some of its IP, and are known to have kept copies of internal documents.

Data Governance 84

Data Governance Machine Learning Metadata Big Data 84

O'Reilly on Data

OCTOBER 18, 2022

As AI technologies are adopted more broadly in security and other high-risk applications, we’ll all need to know more about AI audit and risk management. applies external authoritative standards from laws, regulations, and AI risk management frameworks. The answer is simple—bad things and legal liabilities.

Risk Management 291

Risk Management Risk Testing Measurement 291

datapine

AUGUST 29, 2022

are using big data to their advantage, including: Progressive Insurance’s use of GPS trackers/accelerometers which determine customer safety ratings Google’s ability to predict local flu outbreaks by measuring spikes in flu-related local searches The government of Boston fixing potholes using data that residents enter into their smartphones.

Big Data 263

Big Data Data Analytics Analytics Data mining 263

Jet Global

APRIL 25, 2024

During the audit, the SOX compliance auditor compares past financial statements with current-year statements, analyzing financial information and SOX internal controls to ensure compliance measures are satisfactorily met. When complete, the SOX compliance report must be made available to all relevant parties.

Reporting 52

Reporting Risk Management Risk Software 52