O'Reilly on Data

DECEMBER 12, 2019

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. Because all ML models make mistakes, everyone who cares about ML should also care about model debugging. [1]

Machine Learning 191

Machine Learning Modeling Testing Risk Management 191

Domino Data Lab

AUGUST 6, 2021

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. As such, model governance needs to be applied to each model for as long as it’s being used.

Machine Learning 145

Machine Learning Modeling Testing Risk 145

Cloudera

JULY 15, 2021

We recently hosted a roundtable focused on o ptimizing risk and exposure management with data insights. 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. .

Risk 97

Risk Optimization Management Insurance 97

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.

Risk 86

Risk Marketing Technology Management 86

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 130

Modeling Risk Software Enterprise 130

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 125

Enterprise Modeling Testing Data-driven 125

Cloudera

MARCH 28, 2019

The trouble is, mortgage lenders persist in relying on historical macro-economic assumptions in their models so they risk repeating the errors of a decade ago when banks – and their regulators – failed to recognize the warning signs from a far richer source: low-level micro-economic data. Risk management 3.0.

Risk 73

Risk Management Risk Management Machine Learning 73

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. CIOs should look for other operational and risk management practices to complement transformation programs.

Digital Transformation 139

Digital Transformation Unstructured Data Machine Learning Risk Management 139

DataRobot

OCTOBER 5, 2021

But the technology’s ability to unleash rapid impact with great scope and unique dimensions can also increase organizational risk. For companies implementing AI systems, that risk extends beyond revenue to the reputational damage of using an algorithm that is perceived to be discriminatory or harmful to vulnerable groups.

Risk Management 95

Risk Management Testing Risk Data Science 95

Decision Management Solutions

APRIL 23, 2019

, in which he states there are only three levers of value in insurance: Sell More, Manage Risk Better (aka underwriting and adjusting), and Cost Less to Operate. Let’s dive into greater detail on the second lever – Manage Risk Better. Insurers can also manage risk more effectively through continuous improvement.

Insurance 69

Insurance Risk Technology Management 69

CIO Business Intelligence

SEPTEMBER 7, 2023

Additionally, related issues during use are risk of hallucinations and prompt engineering. 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.

Risk Management 79

Risk Management Risk Testing Digital Transformation 79

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 109

Risk Interactive Insurance Strategy 109

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 359

Risk Modeling Data Processing Software 359

IBM Big Data Hub

APRIL 4, 2024

Lots of innovation is happening, with new technologies emerging in areas such as data and AI, payments, cybersecurity and risk management, to name a few. A third-party risk management review, where the ISV needs to describe how it is complying to required controls. Most of these new technologies are born-in-cloud.

Cost-Benefit 60

Cost-Benefit Risk Management Risk Digital Transformation 60

CIO Business Intelligence

FEBRUARY 20, 2024

Others are weighing the advantages of subscription-based business models where industrial equipment, automation, and processes are delivered as a service. Nall comments: “Copilot can help manufacturers de-risk their supply chains by helping predict potential disruption so they can put mitigations in place ahead of time.”

Manufacturing 83

Manufacturing Sales Risk Management Forecasting 83

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 111

Enterprise Uncertainty Risk Management Reporting 111

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.

Finance 118

Finance Machine Learning Risk Management Optimization 118

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 142

Big Data Finance Risk Management Risk 142

CIO Business Intelligence

DECEMBER 7, 2023

That also means distinguishing between where you can take some risks and where the risk is going to be more limited. Then I think you can temper the level of testing, learning, and scaling and embed some of those growth mindset principles within your team. Space to take some risk and try new things has to be an expectation.

Testing 98

Testing Risk Marketing IoT 98

CIO Business Intelligence

SEPTEMBER 7, 2023

That’s why, around the world, governments and the defense industry as a whole are now investing and exploring generative artificial intelligence (AI), or large language models (LLMs), to better understand what’s possible. The second challenge is managing new risks, which stem primarily from the threat of misinformation.

Unstructured Data 103

Unstructured Data Cost-Benefit Risk Data-driven 103

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 124

Prescriptive Analytics IT Machine Learning Risk 124

IBM Big Data Hub

AUGUST 18, 2023

The Digital Operational Resilience Act , or DORA, is a European Union (EU) regulation that created a binding, comprehensive information and communication technology (ICT) risk-management framework for the EU financial sector. It offers more control and flexibility for comprehensive testing and validation.

Cost-Benefit 80

Cost-Benefit Testing Strategy Risk Management 80

CIO Business Intelligence

JULY 31, 2023

As vendors add generative AI to their enterprise software offerings, and as employees test out the tech, CIOs must advise their colleagues on the pros and cons of gen AI’s use as well as the potential consequences of banning or limiting it. How has, say, ChatGPT hit your business model?” How is your business impacted by generative AI?

Strategy 94

Strategy Risk Enterprise Risk Management 94

IBM Big Data Hub

DECEMBER 4, 2023

As part of our generative AI initiatives, we can demonstrate the ability to use a foundation model with prompt tuning to review the structured and unstructured data within the insurance documents (data associated with the customer query) and provide tailored recommendations concerning the product, contract or general insurance inquiry.

Insurance 56

Insurance Digital Transformation Risk Management Interactive 56

CIO Business Intelligence

APRIL 14, 2023

But just like other emerging technologies, it doesn’t come without significant risks and challenges. According to a recent Salesforce survey of senior IT leaders , 79% of respondents believe the technology has the potential to be a security risk, 73% are concerned it could be biased, and 59% believe its outputs are inaccurate.

Risk 117

Risk Experimentation Marketing Risk Management 117

Cloudera

FEBRUARY 24, 2021

Trend #1: The Crossroads of Risk Management and Emerging Technology. One shift the financial services industry will have to come to terms with is the fact that 2020 may have made risk management models of the past outdated or obsolete , particularly credit risk models. What’s Next?

Risk Management 59

Risk Management Risk Recreation/Entertainment Machine Learning 59

CIO Business Intelligence

JUNE 8, 2023

The demand for project managers has grown, with salaries for this role increasing by 15.6% Key skills for the role include resource allocation, risk and change management, quality assurance, communication, and leadership and team building. percent since 2021, according to Dice.

IT 135

IT Digital Transformation Software Risk 135

CIO Business Intelligence

AUGUST 23, 2023

That need for complex mathematical modeling at scale makes the finance industry a perfect candidate for the promise of quantum computing, which makes (extremely) quick work of computations, including complex ones, delivering results in minutes or hours instead of weeks and months.

IT 75

IT Machine Learning Risk Optimization 75

DataRobot

AUGUST 9, 2021

However, many organizations don’t implement any feedback loop to monitor and control their models after they’re out in production. General Data Protection Regulation, California Consumer Privacy Act, SR 11-7 Guidance on Model Risk Management, etc).

Machine Learning 52

Machine Learning Risk Testing Insurance 52

IBM Big Data Hub

JANUARY 30, 2024

Finance: Optimized for high-speed transactions and can assist in providing robust security, harnessing AI for fraud detection and real-time risk management. Retail: Manage e-commerce platforms, customer data analytics and supply chain logistics, where data analysis often must occur at the edge.

Manufacturing 79

Manufacturing Business Objectives Optimization Risk Management 79

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.

Data-driven 92

Data-driven Software Big Data Testing 92

CIO Business Intelligence

MARCH 13, 2024

We did side-by-side testing,” he says. AI models are able to present best practice code systems to help junior developers learn and hone their skills.” In testing, gen AI was also particularly good at generating test cases and creating dummy data for testing.

IT 124

IT Consulting Testing Cost-Benefit 124

CIO Business Intelligence

APRIL 13, 2023

Gartner projects that spending on information security and risk management products and services will grow 11.3% To better focus security spend, some chief information security officers (CISOs) are shifting their risk assessments from IT systems to the data, applications, and processes that keep the business going.

IT 111

IT Cost-Benefit Risk Consulting 111

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

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

CIO Business Intelligence

APRIL 27, 2023

They’re required to work closely with upper management, executives, and key stakeholders to identify business needs and requirements. Relevant skills for a systems architect include risk management, performance optimization, security, leadership, and a strong knowledge of complex computer systems.

IT 113

IT Software Risk Management 113

DataRobot

NOVEMBER 12, 2021

However, these benefits can come with a risk which must be addressed. We’ve learned over time, the processes, data fluency, ingenuity and risk tolerances vary greatly across and even within industries. We’ve found that implementing a model requires four general personas to collaborate across the organization.

Insurance 69

Insurance Risk Risk Management Manufacturing 69