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.

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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.

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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.

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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.

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Risk Management Testing Risk Data Science 95

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.

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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.

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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.”

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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.

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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.

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Risk Interactive Insurance Strategy 132

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.

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CIO Business Intelligence

MARCH 18, 2024

We are proceeding cautiously because the rise of LLMs [large language models] presents a new level of data security risk,” he says. “We We have been developing our own internal AI capability over the last few years using open-source models. People don’t realize the AI models have to churn so many compute resources.

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Risk Cost-Benefit Data Processing Testing 133

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 86

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

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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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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.

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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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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.

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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.

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Unstructured Data Cost-Benefit Risk Risk Management 119

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.

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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.

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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 Risk 56

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.

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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.

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Manufacturing Business Objectives Optimization Risk Management 80

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.

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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?

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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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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).

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Machine Learning Risk Testing Insurance 52

CIO Business Intelligence

JUNE 8, 2023

Relevant skills include security architecture design, threat modeling, compliance and regulations, security operations, risk assessment and management, and security infrastructure implementation. Average salary: US$145,512 Increase since 2021: 7.7%

IT 140

IT Digital Transformation Software Risk 140

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?

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DataRobot

JULY 16, 2021

The first is trust in the performance of your AI/machine learning model. They all serve to answer the question, “How well can my model make predictions based on data?” How can identifying gaps or discrepancies in the training data help you build a more trustworthy model? Dimensions of Trust. How large is the data set?

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Machine Learning Uncertainty Modeling Measurement 98

Decision Management Solutions

APRIL 23, 2019

Predictive analytics streamlines the decision to maximize efficiency and accuracy, enabling carriers to be smarter about risk while automating more decisions. Predictive analytic models can also address complex interrelationships between data elements that contribute to risk and may be too subtle to be recognized by an underwriter.

Insurance 69

Insurance Risk Technology Management 69

CIO Business Intelligence

APRIL 14, 2023

But it doesn’t always work, so don’t forget to test ChatGPT’s output before pasting it somewhere that matters.” Focusing on generative AI may be a useful approach since it addresses large language models (LLMs), including ChatGPT, without having to boil the ocean across the AI universe.

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Risk Experimentation Marketing Risk Management 122

DataRobot

NOVEMBER 12, 2021

We’ve found that implementing a model requires four general personas to collaborate across the organization. AI Implementers: The IT organization that must inherit a model, whether ML Engineers, or more generally ML Ops personnel. AI Consumers: The internal and external parties that consume the model’s output.

Insurance 69

Insurance Risk Risk Management Manufacturing 69

IBM Big Data Hub

SEPTEMBER 20, 2022

But these organizations need to be able to trust their AI/ML models before they can be operationalized and used in crucial business processes. These days, if an AI model makes a biased, unfair decision involving the health, wealth or well-being of humans, an organization can hit the news for the wrong reasons. Is it scalable?

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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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BizAcuity

APRIL 1, 2023

Anti-Money Laundering (AML) is increasingly becoming a crucial branch of risk management and fraud prevention. The algorithms can detect anomalies in the transactional data and helps to identify high-risk customers and transactions that may be linked to money laundering activities.

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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.

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IT Software Risk Management 116