DataRobot Blog

JUNE 3, 2022

The influence of the GPT-3 language model has the potential to be both beneficial and misused. . Generative Pre-trained Transformer 3 (GPT-3) is a language model that utilizes deep-structured learning to predict human-like text. Similarly, no one is focused on smaller models. What is GPT-3? When is enough ever enough?

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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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CIO Business Intelligence

MAY 14, 2024

Enterprises need to ensure that private corporate data does not find itself inside a public AI model,” McCarthy says. You don’t want a mistake to happen and have it end up ingested or part of someone else’s model. Such private cloud solutions eliminate the risks of multitenancy data leakage, for example, a key CIO concern with AI.

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IT Data Processing Enterprise Modeling 118

IBM Big Data Hub

MARCH 7, 2023

The challenge was gleaning relevant insights from the vast amount of data collected to help identify those infants at risk. The specific approach we took required the use of both AI and edge computing to create a predictive model that could process years of anonymized data to help doctors make informed decisions.

Risk 52

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CIO Business Intelligence

DECEMBER 11, 2023

Traditionally, the work of the CFO and the finance team was focused on protecting the company’s assets and reputation and guarding against risk. They can even optimize capital allocation decisions, such as dividend distribution versus share buy-back, by rapidly modeling multiple scenarios and market conditions.

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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. With the exponential growth of large datasets, predictive analytics is being leveraged by enterprises across industries.

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BizAcuity

JANUARY 18, 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. With the exponential growth of large datasets, predictive analytics is being leveraged by enterprises across industries.

Machine Learning 52

Machine Learning Predictive Analytics Predictive Modeling Risk 52

FineReport

APRIL 9, 2024

From advanced analytics to predictive modeling, the evolving landscape of business intelligence is revolutionizing how data is processed and leveraged for actionable insights. Proactive Risk Management : BI tools enable organizations to proactively identify potential risks through predictive modeling and trend analysis.

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Smarten

MAY 31, 2022

The technology research firm, Gartner has predicted that, ‘predictive and prescriptive analytics will attract 40% of net new enterprise investment in the overall business intelligence and analytics market.’ It is meant to identify crucial relationships and opportunities and risks and help the organization to accurately predict: Growth.

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Smarten

SEPTEMBER 28, 2022

In order to understand how businesses might use assisted predictive modeling and predictive analytics, let’s look at some business use cases and how analytical techniques can help the enterprise derive concise, clear information to support decisions and strategies. Quality Control. Demand Planning. Plan and forecast accurately.

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Predictive Analytics Analytics Predictive Modeling Forecasting 52

IBM Big Data Hub

MAY 18, 2023

This report outlines the combination of traditional decision automation tools with machine learning models and other technologies. As Forrester notes in the report, many organizations are eager to harness the power of AI but also must be cautious of risks.

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Smart Data Collective

MARCH 3, 2021

When the risk of recurrence for a malpractice case is high, for example, they can convince the judge to be more generous in rewarding the client. The impact of predictive modelling on personal injury cases. Predictive modelling is a technology that evolved together with big data analytics.

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Analytics Big Data Predictive Modeling Insurance 129

Cloudera

JUNE 27, 2023

Adopting an MDA is more efficient and cost effective than legacy data architectures which are siloed, poorly governed, with vast redundant data sets and data processes that are expensive and expose the company to risk. Appliance-based models are usually significantly more expensive than disaggregated solutions. Modernize data flows.

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IBM Big Data Hub

MAY 1, 2023

The early versions of AI were capable of predictive modelling (e.g., The four categories of predictive modelling, robotics, speech and image recognition are collectively known as algorithm-based AI or Discriminative AI. recommending similar Netflix shows based on your previous choices) or robotics (e.g.,

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Insurance Predictive Modeling Cost-Benefit Statistics 82

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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Ontotext

MAY 31, 2023

To support drug discovery, Ontotext has recently developed a method for gene-disease link prediction, which can help focus research efforts where it would matter most and speed up the drug development process. The link prediction models are based on the so-called Knowledge Graph Embeddings, which relying on knowledge graph technology.

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DataRobot

OCTOBER 19, 2021

DataRobot helped combat this problem head on by applying AI to evaluate and predict resource allocation and identify the most impacted communities from a national to county level. On average, DataRobot forecasts had a 21 percent lower rate of error than all other published competing models over a six to eight week period. White Paper.

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DataKitchen

JUNE 19, 2023

Our customers start looking at the data in dashboards and models and then find many issues. This pillar emphasizes the importance of implementing thorough data validation tests to mitigate the risks of erroneous analysis or decision-making based on faulty data. In our experience, the locus of those problems changes over time.

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Smart Data Collective

SEPTEMBER 17, 2020

Here are a few of the advantages of Big Data in the banking and financial industry: Improvement in risk management operations. Big Data can efficiently enhance the ways firms utilize predictive models in the risk management discipline. Big Data provides financial and banking organizations with better risk coverage.

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Smart Data Collective

MARCH 23, 2022

This AI could be utilized as a safety feature , like real-time risk assessment, for example, alerting the driver when a potential incident has been detected. Utilizing advanced heuristics and AI modeling OEMs can simulate a multitude of conditions, fast-tracking these models using automation.

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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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John Wheeler

OCTOBER 20, 2020

We continue our “20 for 20” theme this year by highlighting the integrated risk management (IRM) critical capabilities and top 20 software functions / features. These five capabilities support both integrated view of strategic, operational and technology risk as well as the related business outcomes, processes and assets.

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Risk Management Risk Predictive Modeling Insurance 52

Smarten

MARCH 12, 2020

A solution that provides a balance between data agility and access and data governance and security can provide solid, dependable information and the ability for users to leverage Self-Serve Data Preparation , Assisted Predictive Modeling and Smart Data Visualization while protecting the organization from risk and mitigating security issues.

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Business Over Broadway

SEPTEMBER 27, 2018

pharmacogenomics) and risk assessment of genetic disorders (e.g., In this scenario, marketing analytics can only be conducted within one data silo at a time, decreasing your model’s predictive power / increasing your model’s error. Machine Learning and Predictive Modeling of Customer Churn.

Machine Learning 76

Machine Learning Data Integration Marketing Predictive Modeling 76

DataRobot

MARCH 2, 2021

With DataRobot, you can build dozens of predictive models with the push of a button and easily deploy them. Monitoring deployed models is easy because we provide features to check on service health, data drift, and accuracy. We can help with data preparation and AI development, deployment, and monitoring.

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CIO Business Intelligence

OCTOBER 5, 2023

“We’ve seen so many initiatives fail when it’s technology for technology’s sake,” says Davé, who suggests two means of avoiding this mistake: prioritization models aligned to your business strategy and strategic partnerships. Let’s start with the models. For AI, the high-value quadrant is where you’ll find most predictive modeling.

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Smarten

JUNE 8, 2022

Citizen Data Scientists can create new models, share models and collaborate, thereby improving business results and data literacy.’. The business wants to use predictive analytics to identify those customers who were most likely to leave and develop processes and strategies to improve customer retention and reduce customer churn.

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IBM Big Data Hub

OCTOBER 5, 2022

Build planning models to improve supply chain management. You also build planning models to capture relationships and constraints so that you can change your driver assumptions and immediately see the impact on resources and capacity over time. The challenge faced by every company is matching supply with demand.

Analytics 52

Analytics Forecasting Predictive Modeling Manufacturing 52

CIO Business Intelligence

OCTOBER 12, 2023

I will say this, and this is a part of your personality profile, risk aversion right now is not a great thing. If we were not willing to take some risks, we couldn’t have done it. But if you were risk averse when ChatGPT came out, you probably didn’t see the possibilities that are now evident. That’s a healthy tension.

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CIO Business Intelligence

DECEMBER 13, 2022

Our digital transformation strategy is centered around establishing a consumer-oriented model that helps us customize chronic care management based on the ever-changing conditions of each patient.” Everyone looks to leadership to be role models and set examples. Second, I think flexibility is crucial. For us, that’s a balance.

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Domino Data Lab

JULY 28, 2021

There are many software packages that allow anyone to build a predictive model, but without expertise in math and statistics, a practitioner runs the risk of creating a faulty, unethical, and even possibly illegal data science application. All models are not made equal. A simple example is the case of missing values.

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Smarten

APRIL 17, 2018

How Can Assistive Predictive Modeling Help My Business Users? Assisted Predictive Modeling can help your business achieve its goals.

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Smarten

MAY 17, 2019

These techniques can be beneficial for infrastructure planning, construction, highway planning and management, government, agriculture, weather, travel and city planning, and can help the business to plan for resources, locations, supply chain, marketing, inventory, pricing, risk management, maintenance and other planning activities.

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Smarten

MAY 17, 2019

Whether you need to anticipate and plan for equipment maintenance, target online customers, control customer churn, or identify ways to cross-sell and upsell customers on existing and new products and services, these predictive analytics tools can help you to optimize your marketing budget and your resources and mitigate risk and market missteps.

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CIO Business Intelligence

JUNE 7, 2022

Data analytics draws from a range of disciplines — including computer programming, mathematics, and statistics — to perform analysis on data in an effort to describe, predict, and improve performance. Predictive analytics is often considered a type of “advanced analytics,” and frequently depends on machine learning and/or deep learning.

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O'Reilly on Data

MARCH 20, 2019

Apply fair and private models, white-hat and forensic model debugging, and common sense to protect machine learning models from malicious actors. Like many others, I’ve known for some time that machine learning models themselves could pose security risks.

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DataRobot Blog

AUGUST 10, 2022

NHS organisations are used to using workforce KPIs to manage staffing levels, but the real opportunity is being able to identify staff that are at risk of leaving post and to implement strategies to retain their much needed talent. Resetting urgent care performance and delivery. Improving patient outcomes through a data-first approach.

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