DataRobot Blog

JULY 22, 2022

Using AI-based models increases your organization’s revenue, improves operational efficiency, and enhances client relationships. You need to know where your deployed models are, what they do, the data they use, the results they produce, and who relies upon their results. That requires a good model governance framework.

Modeling 105

Modeling Risk Management Testing Risk 105

O'Reilly on Data

MAY 18, 2020

This article answers these questions, based on our combined experience as both a lawyer and a data scientist responding to cybersecurity incidents, crafting legal frameworks to manage the risks of AI, and building sophisticated interpretable models to mitigate risk. All predictive models are wrong at times?—just

Risk 364

Risk Modeling Data Processing Reporting 364

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

Cloudera

MARCH 2, 2022

Risk management is a highly dynamic discipline these days. Stress testing is a particular area that has become even more important throughout the pandemic. Similarly, the European Central Bank is issuing stress testing requirements related to climate risk given the potential economic shifts related to addressing climate change.

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Testing Management Risk Management Forecasting 95

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

CIO Business Intelligence

NOVEMBER 7, 2024

It covers essential topics like artificial intelligence, our use of data models, our approach to technical debt, and the modernization of legacy systems. This team addresses potential risks, manages AI across the company, provides guidance, implements necessary training, and keeps abreast of emerging regulatory changes.

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Insurance Experimentation Testing Technology 120

bridgei2i

NOVEMBER 20, 2020

Episode 2: AI enabled Risk Management for FS powered by BRIDGEi2i Watchtower. AI enabled Risk Management for FS powered by BRIDGEi2i Watchtower. Today the Chief Risk Officers(CROs) struggle with the critical task of monitoring and assessing key risks in real time and firefight to mitigate any critical issues that arise.

Risk Management 92

Risk Management Risk Management Metrics 92

CIO Business Intelligence

JULY 11, 2023

In recent years, we have witnessed a tidal wave of progress and excitement around large language models (LLMs) such as ChatGPT and GPT-4. By deploying the LLM within their own VPC, the company can benefit from the AI’s insights without risking the exposure of their valuable data.

Modeling 95

Modeling Testing Experimentation Risk Management 95

CIO Business Intelligence

MARCH 4, 2025

In fact, successful recovery from cyberattacks and other disasters hinges on an approach that integrates business impact assessments (BIA), business continuity planning (BCP), and disaster recovery planning (DRP) including rigorous testing. See also: How resilient CIOs future-proof to mitigate risks.)

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Testing Risk Consulting Risk Management 64

Cloudera

OCTOBER 20, 2021

Understanding a firm’s exposure to climate risk begins with creating scenarios and gaining better visibility to the impact of a variety of variables on the book of business. Stress testing was heavily scrutinized in the post 2008 financial crisis. Transition : the changes in asset values, business models, etc. (ex.

Risk 80

Risk Modeling Risk Management Measurement 80

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.

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Big Data Finance Risk Management Risk 145

CIO Business Intelligence

JANUARY 6, 2025

These regulations mandate strong risk management and incident response frameworks to safeguard financial operations against escalating technological threats. DORA mandates explicit compliance measures, including resilience testing, incident reporting, and third-party risk management, with non-compliance resulting in severe penalties.

Risk Management 52

Risk Management Risk Insurance Reporting 52

DataRobot

OCTOBER 5, 2021

Throughout history, introducing innovations in fields like aviation and nuclear power to society required robust risk management frameworks. AI is no different, and by its nature, it demands a comprehensive approach to governance utilizing risk management. Step 1: Classify the AI Decision Type.

Risk Management 95

Risk Management Testing Risk Data Science 95

Smart Data Collective

APRIL 22, 2020

Big data software development models vary depending on the ultimate purpose, scale, and other constraints. Using a specified data-driven model/ methodology that considers these factors is key to ensuring a successful deployment of the software. In the end, it is compiled and kept ready for testing as a whole.

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Data-driven Software Big Data Testing 114

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 85

Data Architecture Risk Management Risk Management 85

CIO Business Intelligence

JULY 1, 2024

Notable examples of AI safety incidents include: Trading algorithms causing market “flash crashes” ; Facial recognition systems leading to wrongful arrests ; Autonomous vehicle accidents ; AI models providing harmful or misleading information through social media channels.

Reporting 129

Reporting Risk Management Experimentation Risk 129

CIO Business Intelligence

DECEMBER 19, 2023

Many technology investments are merely transitionary, taking something done today and upgrading it to a better capability without necessarily transforming the business or operating model. In the SANS 2023 DevSecOps Survey , less than 22% of respondents patched and resolved critical security risks and vulnerabilities in under two days.

Digital Transformation 143

Digital Transformation Unstructured Data Machine Learning Risk Management 143

Domino Data Lab

AUGUST 10, 2021

Models are the central output of data science, and they have tremendous power to transform companies, industries, and society. At the center of every machine learning or artificial intelligence application is the ML/AI model that is built with data, algorithms and code. The process of creating models is called modeling.

Modeling 64

Modeling IT Deep Learning Risk Management 64

Cloudera

FEBRUARY 4, 2021

Financial services institutions need the ability to analyze and act on massive volumes of data from diverse sources in order to monitor, model, and manage risk across the enterprise. They need a comprehensive data and analytics platform to model risk exposures on-demand. Cloudera is that platform.

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Risk Marketing Technology Management 88

CIO Business Intelligence

OCTOBER 15, 2024

But continuous deployment isn’t always appropriate for your business , stakeholders don’t always understand the costs of implementing robust continuous testing , and end-users don’t always tolerate frequent app deployments during peak usage. CrowdStrike recently made the news about a failed deployment impacting 8.5

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Enterprise Testing Business Objectives Experimentation 143

CIO Business Intelligence

MAY 15, 2024

It seems anyone can make an AI model these days. Even if you don’t have the training data or programming chops, you can take your favorite open source model, tweak it, and release it under a new name. According to Stanford’s AI Index Report, released in April, 149 foundation models were released in 2023, two-thirds of them open source.

Modeling 137

Modeling Risk Software Enterprise 137

CIO Business Intelligence

SEPTEMBER 10, 2024

We envisioned harnessing this data through predictive models to gain valuable insights into various aspects of the industry. Additionally, we explored how predictive models could be used to identify the ideal profile for haul truck drivers, with the goal of reducing accidents and fatalities.

IT 122

IT Risk Risk Management Predictive Modeling 122

CIO Business Intelligence

SEPTEMBER 7, 2023

Additionally, it’s paramount within the financial services sector to ensure responsible AI and adherence to regulatory guidance for model risk. Keeping our AI approach interpretable and managing bias becomes crucial. The trust we build with our customers is our most important asset—and we don’t take that for granted.

Risk Management 98

Risk Management Risk Testing Digital Transformation 98

IBM Big Data Hub

FEBRUARY 13, 2024

This data is fed into generational models, and there are a few to choose from, each developed to excel at a specific task. Generative adversarial networks (GANs) or variational autoencoders (VAEs) are used for images, videos, 3D models and music. Autoregressive models or large language models (LLMs) are used for text and language.

Enterprise 118

Enterprise Testing Modeling Data-driven 118

CIO Business Intelligence

MAY 20, 2024

This has CIOs moving from experimenting and testing intelligence in pockets to scaling up deployments and rolling out intelligence throughout their organizations. IT projects also include deployment of AI-powered security solutions and other technologies that support a zero-trust security model. Risk management came in at No.

IT 142

IT Risk Management Machine Learning Risk 142

CIO Business Intelligence

JANUARY 18, 2024

Optimism aside, the true test is in how well organizations will master the changes to the nature of work that AI enables. Nearly a third (29%) of CEOs are dissatisfied with their organization’s speed of innovation, capabilities in risk management, and talent acquisition and retention rates.

Machine Learning 126

Machine Learning Finance Risk Management Optimization 126

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.

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Risk Optimization Management Insurance 100

DataRobot Blog

MAY 26, 2022

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. In summary, to ensure that they have built a robust model, modelers must make certain that they have designed the model in a way that is backed by research and industry-adopted practices.

Risk 52

Risk Modeling Metrics Business Objectives 52

CIO Business Intelligence

JULY 10, 2024

The only significant increase in risk mitigation was in accuracy, where 38% of respondents said they were working on reducing risk of hallucinations, up from 32% last year. However, organizations that followed risk management best practices saw the highest returns from their investments.

Enterprise 136

Enterprise Risk Modeling Risk Management 136

CIO Business Intelligence

MARCH 19, 2025

Developers, data architects and data engineers can initiate change at the grassroots level from integrating sustainability metrics into data models to ensuring ESG data integrity and fostering collaboration with sustainability teams. Highlight how ESG metrics can enhance risk management, regulatory compliance and brand reputation.

IT 59

IT Data Governance Data-driven Metrics 59

CIO Business Intelligence

JANUARY 11, 2024

CIOs are under increasing pressure to deliver AI across their enterprises – a new reality that, despite the hype, requires pragmatic approaches to testing, deploying, and managing the technologies responsibly to help their organizations work faster and smarter. The top brass is paying close attention.

Enterprise 128

Enterprise Uncertainty Reporting Risk Management 128

CIO Business Intelligence

APRIL 5, 2024

Senate Bill 1047 , introduced in the California State Legislature in February, would require safety testing of AI products before they’re released, and would require AI developers to prevent others from creating derivative models of their products that are used to cause critical harms.

Risk 132

Risk Insurance Interactive Strategy 132

CIO Business Intelligence

SEPTEMBER 17, 2024

All this while CIOs are under increased pressure to deliver more competitive capabilities, reduce security risks, connect AI with enterprise data, and automate more workflows — all areas where architecture disciplines have a direct role in influencing outcomes.

Enterprise 145

Enterprise Digital Transformation Cost-Benefit Risk 145

CIO Business Intelligence

AUGUST 30, 2024

Qualifications: High school diploma or equivalent Cost: $300 plus a $100 application fee PHR The Professional in Human Resources (PHR) demonstrates mastery of the technical and operational aspects of HR management, including US laws and regulations.

Cost-Benefit 118

Cost-Benefit Risk Management Strategy Testing 118

CIO Business Intelligence

OCTOBER 4, 2023

There’s also strong demand for non-certified security skills, with DevSecOps, security architecture and models, security testing, and threat detection/modelling/management attracting the highest pay premiums.

Prescriptive Analytics 135

Prescriptive Analytics IT Machine Learning Deep Learning 135

DataRobot Blog

JULY 21, 2022

Document assumptions and risks to develop a risk management strategy. Machine learning models created in silos are rarely implemented. Models could also be deployed into multiple environments at once. Discuss how the stakeholders want to interact with the machine learning model after it is built.

Machine Learning 98

Machine Learning Metrics Modeling Testing 98

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 143

Deep Learning Machine Learning Modeling Software 143