IBM Big Data Hub

MAY 8, 2023

This blog continues the discussion, now investigating the risks of adopting AI and proposes measures for a safe and judicious response to adopting AI. Risk and limitations of AI The risk associated with the adoption of AI in insurance can be separated broadly into two categories—technological and usage.

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IBM Big Data Hub

APRIL 25, 2023

Environmental, Social, and Governance (ESG) risk management has emerged as a critical aspect of business strategy for companies worldwide. Focusing on ESG Risk Management can help your organization become more profitable, and your organization can start on this journey today. So how does ESG risk management help the bottom line?

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IBM Big Data Hub

JUNE 19, 2023

Companies can manufacture products closer to the consumer, reducing the risk of disruption caused by natural disasters and geopolitical instability. The European Union and China are investing trillions in their economies to rebuild local industries and create a less risk-prone supply chain.

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IBM Big Data Hub

SEPTEMBER 13, 2023

Incorporating an effective attack surface management tool into your security strategy can significantly help you mitigate the risks of data breaches. Because hackers don’t limit their reconnaissance efforts to what’s in your inventory, these unknown assets put you at risk.

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Cloudera

OCTOBER 23, 2022

Already underrepresented populations like workers without a four-year degree are four times more likely to work in highly automatable jobs than individuals with a bachelor’s degree, with 55% of tasks performed by low-wage workers also at risk for automation. In the U.S.

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DataRobot Blog

APRIL 29, 2022

This provides a great amount of benefit, but it also exposes institutions to greater risk and consequent exposure to operational losses. The stakes in managing model risk are at an all-time high, but luckily automated machine learning provides an effective way to reduce these risks.

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Smart Data Collective

JULY 8, 2021

Let’s talk about some benefits and risks of artificial intelligence. Risks of Artificial Intelligence: Unsustainability. Visit our blog to find more information. The post Understanding the Benefits And Risks Of Relying on AI appeared first on SmartData Collective. A future threat to humanity.

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Smart Data Collective

FEBRUARY 14, 2023

However, it is important to understand the benefits and risks associated with cloud computing before making the commitment. However, there are some risks associated with using cloud-based software for business purposes. Firstly, there is always the risk of data breaches due to cyber-attacks or human error.

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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. .

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Alation

JANUARY 9, 2023

Alation joined with Ortecha , a data management consultancy, to publish a white paper providing insights and guidance to stakeholders and decision-makers charged with implementing or modernising data risk management functions. The Increasing Focus On Data Risk Management. Download the complete white paper now.

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IBM Big Data Hub

OCTOBER 16, 2023

In addition, real-time monitoring helps to comply with SLAs, reduce risks and safeguard your operations. Explore what IBM Instana can do for your business The post Challenges and risks associated with lack of real-time monitoring in SAP appeared first on IBM Blog.

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DataRobot

OCTOBER 22, 2021

In part one of our “Five Ways AI Can Help States Solve Their Hardest Problems” blog series, we reviewed the ramifications of crisis response management—or lack thereof—and how AI can help. Their increased usage has also led to new challenges related to compliance, misuse, and fraud risk. WHITE PAPER. Download Now.

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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 encompasses risk management and regulatory compliance and guides how AI is managed within an organization.

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DataKitchen

MAY 10, 2024

This blog post introduces five critical use cases for data observability, each pivotal in maintaining the integrity and usability of data throughout its journey in any enterprise. Read The Entire Blog Series Each use case presents unique challenges and requires specific strategies to ensure data health.

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Dataiku

NOVEMBER 20, 2020

AI systems may cause unintended harms and their increasing adoption leads to additional risks and scrutiny. In this blog post, I present tools project teams can use to identify ethical concerns associated with their AI use cases.

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John Wheeler

APRIL 16, 2021

This week, we kicked-off a major research effort to explore current innovations in the rapidly expanding integrated risk management (IRM) market. This represents a great opportunity for organizations to utilize risk management technology (RiskTech) to bridge the gap between these seemingly disconnected transformation efforts.

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Cloudera

OCTOBER 20, 2021

As part of these efforts, disclosure requirements will mandate that firms provide “the impact of a company’s activities on the environment and society, as well as the business and financial risks faced by a company due to its sustainability exposures.” What are the key climate risk measurements and impacts? They need to understand;

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IBM Big Data Hub

AUGUST 1, 2023

Risk-based application patching helps IT pros discover vulnerable apps and automatically patch them based on the criticality of the situation. ” What is risk-based application patching? Modern unified endpoint management (UEM) solutions include laptop management.

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erwin

FEBRUARY 12, 2021

Managing an organization’s governance, risk and compliance (GRC) via its enterprise and business architectures means managing them against business processes (BP). Governance, risk and compliance are treated as isolated bubbles. Data-related risks are not connected with the data architects/data scientists.

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Analytics Vidhya

MARCH 21, 2023

Introduction With the rapid advancements in Artificial Intelligence (AI), it has become increasingly important to discuss the ethical implications and potential risks associated with the development of these technologies.

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John Wheeler

JUNE 4, 2020

Risk management is trending through the roof here at Gartner as we strive to help our clients emerge from the COVID-19 crisis. What’s also trending is the readership of my RiskTech blog posts. What’s So Cool About Risk Management? What’s So Cool About Risk Management? 4 Reasons Why GRC Is a Useless Term.

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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.

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Aryng

JANUARY 18, 2022

Customer research helps in identifying the customers’ […] The post Leave risk behind by embracing customers’ needs appeared first on Aryng's Blog. Everything falls into place once their experience is satisfying. But how do you know what the customers want?

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IBM Big Data Hub

AUGUST 17, 2023

Overprovisioning is the go-to strategy for avoiding performance risks. It’s a low-risk way to get comfortable with automation. In other words, put those performance-risk nightmares to bed. Trying to find the balance between performance and efficiency is anything but a walk in the park. Have your app owner take it.

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IBM Big Data Hub

AUGUST 16, 2023

Nevertheless, I also understand the criticality of evaluating the inherent risks before embarking on its direct adoption within our organizations. In this discussion, I explore the risks and challenges associated with ChatGPT in an enterprise context, necessitating a careful approach to its implementation.

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bridgei2i

JANUARY 17, 2021

Case study: Emerging Risk Identification for Personal Lines Insurance. The client, a global insurance leader, wanted to identify emerging risks, associated drivers & trends by analyzing various external data sources including medical journals, legal opinion blogs, newsfeed & social media. Business Context.

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DataRobot Blog

JUNE 27, 2022

If the assumptions are being breached due to fundamental changes in the process being modeled, the deployed system is not likely to serve its intended purpose, thereby creating further model risk that the institution must manage. Ongoing model monitoring is an essential component of a sound model risk management practice. Conclusion.

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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. End-to-end Data Lifecycle.

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DataKitchen

MAY 10, 2024

This blog post delves into the third critical use case for Data Observation and Data Quality Validation: development and Deployment. It highlights how DataKitchen’s Data Observation solutions equip organizations to enhance their development practices, reduce deployment risks, and increase overall productivity.

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Sisense

JANUARY 7, 2022

As businesses adapt to the pandemic and shift to new norms, risk mitigation strategies have become as normal and ubiquitous as having a fire escape in the office. Smarter, AI-driven learning and development initiatives will help mitigate risk in our rapidly evolving world. Minimising risk by ‘infusing’ AI. ” Anna adds.

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DataRobot Blog

MAY 26, 2022

To start with, SR 11-7 lays out the criticality of model validation in an effective model risk management practice: Model validation is the set of processes and activities intended to verify that models are performing as expected, in line with their design objectives and business uses. Conclusion.

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Aryng

APRIL 22, 2022

Most managers are good at formulating innovative […] The post How to differentiate the thin line separating innovation and risk in experimentation appeared first on Aryng's Blog. We have seen this as a general trend in start-ups, and we know that it’s an awful feeling!

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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.

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Laminar Security

JUNE 29, 2023

Using a risk-based approach to deal with policy violations and continuous compliance monitoring will help avoid data exposures and fines. Organizations risk significantly increasing public cloud data risks if they don’t take necessary steps to improve its security. Public cloud spending and adoption is growing fast.

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CDW Research Hub

MAY 1, 2019

Links to last month’s most popular posts: Managing Third-Party Risk: Lessons from the Field. The post April 2019 Blog Post Round-Up appeared first on Sirius Computer Solutions. 6 Stages to the Analytics-Driven Security Journey. 4 Tips to Maximize Your IAM Strategy in the Cloud.

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Decision Management Solutions

APRIL 23, 2019

In February, we published a blog post on “Using Technology to Add Value in Insurance”. 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.

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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. What Is Model Risk?

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