CIO Business Intelligence

FEBRUARY 10, 2023

Predictive analytics definition Predictive analytics is a category of data analytics aimed at making predictions about future outcomes based on historical data and analytics techniques such as statistical modeling and machine learning. Financial services: Develop credit risk models. from 2022 to 2028.

Predictive Analytics 145

Predictive Analytics Analytics Predictive Modeling Forecasting 145

O'Reilly on Data

JANUARY 17, 2023

Building Models. A common task for a data scientist is to build a predictive model. You’ll try this with a few other algorithms, and their respective tuning parameters–maybe even break out TensorFlow to build a custom neural net along the way–and the winning model will be the one that heads to production.

Machine Learning 333

Machine Learning Predictive Modeling Software Modeling 333

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?

Risk 52

Risk Predictive Modeling Modeling Finance 52

IBM Big Data Hub

JANUARY 25, 2024

Yet many AI creators are currently facing backlash for the biases, inaccuracies and problematic data practices being exposed in their models. The math demonstrates a powerful truth All predictive models, including AI, are more accurate when they incorporate diverse human intelligence and experience.

Predictive Modeling 83

Predictive Modeling Risk Modeling Statistics 83

DataKitchen

SEPTEMBER 21, 2023

This proactive approach to data quality guarantees that downstream analytics and business decisions are based on reliable, high-quality data, thereby mitigating the risks associated with poor data quality. The fourth pillar focuses on testing the results of data models, visualizations, and other applications to validate data in use.

Testing 173

Testing Data Quality Predictive Modeling Metrics 173

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

Risk Technology Informatics Predictive Modeling 52

Insight

FEBRUARY 19, 2020

Using variability in machine learning predictions as a proxy for risk can help studio executives and producers decide whether or not to green light a film project Photo by Kyle Smith on Unsplash Originally posted on Toward Data Science. Hollywood is a $10 billion-a-year industry, and movies range from huge hits to box office bombs.

Risk 67

Risk ROI Modeling Metrics 67

CIO Business Intelligence

MAY 9, 2024

Companies want candidates who can drive innovation, deliver meaningful business results, and work closely with other leaders to manage risks. And they must develop and upskill talent to ensure the workforce is well-versed in the innovation and risk associated with AI use. The same can be said for AI talent in general, Daly stresses.

Risk Management 138

Risk Management Cost-Benefit Risk Technology 138

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.

Finance 105

Finance Forecasting IoT Predictive Modeling 105

David Menninger's Analyst Perspectives

NOVEMBER 7, 2024

The difference is in using advanced modeling and data management to make faster scenario planning possible, driven by actionable key performance measures that enable faster, well-informed decision cycles. A major practical benefit of using AI is putting predictive analytics within easy reach of any organization.

Forecasting 130

Forecasting Machine Learning Finance Predictive Analytics 130

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.

Machine Learning 52

Machine Learning Predictive Analytics Predictive Modeling Risk 52

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

IBM Big Data Hub

APRIL 5, 2024

The maintenance, repair and overhaul (MRO) system provides necessary spare parts to carry out work and maintains an optimum stock level with a balance of stock out risk and part holding costs. Asset criticality defines whether a preventive or predictive task is justified in terms of cost and risk.

Management 64

Management Risk Optimization Predictive Modeling 64

O'Reilly on Data

SEPTEMBER 19, 2023

Stage 2: Machine learning models Hadoop could kind of do ML, thanks to third-party tools. While data scientists were no longer handling Hadoop-sized workloads, they were trying to build predictive models on a different kind of “large” dataset: so-called “unstructured data.” And it was good.

Machine Learning 253

Machine Learning Testing Modeling Cost-Benefit 253

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.

Predictive Analytics 69

Predictive Analytics Analytics Predictive Modeling Prescriptive Analytics 69

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.

IT 143

IT Data Processing Enterprise Modeling 143

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.

Predictive Analytics 52

Predictive Analytics Analytics Predictive Modeling Forecasting 52

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.

Analytics 130

Analytics Big Data Predictive Modeling Insurance 130

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.

Business Intelligence 52

Business Intelligence Software Data-driven Predictive Modeling 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.

Reporting 87

Reporting Scorecard Predictive Modeling Machine Learning 87

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 97

IT Machine Learning Risk Optimization 97

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.

Data Architecture 62

Data Architecture Predictive Modeling Data Governance Data-driven 62

O'Reilly on Data

SEPTEMBER 15, 2020

In my book, I introduce the Technical Maturity Model: I define technical maturity as a combination of three factors at a given point of time. Technical competence results in reduced risk and uncertainty. AI initiatives may also require significant considerations for governance, compliance, ethics, cost, and risk.

Advertising 300

Advertising Cost-Benefit ROI Machine Learning 300

CIO Business Intelligence

JUNE 22, 2022

Predictive maintenance applications enable large-scale manufacturers to collect telemetry data and integrate all IoT functions, and these are powered by models driven by real-time data. . The financial services industry has had to dedicate more resources to personalisation, fighting fraud, and reducing cloud concentration risk.

IoT 98

IoT IT Manufacturing Internet of Things 98

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.

Big Data 142

Big Data Finance Risk Management Risk 142

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.,

Insurance 75

Insurance Predictive Modeling Cost-Benefit Statistics 75

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.

Forecasting 98

Forecasting Predictive Modeling Testing Optimization 98

Cloudera

SEPTEMBER 7, 2023

With the emergence of new creative AI algorithms like large language models (LLM) fromOpenAI’s ChatGPT, Google’s Bard, Meta’s LLaMa, and Bloomberg’s BloombergGPT—awareness, interest and adoption of AI use cases across industries is at an all time high. But it’s also fraught with risk.

Insurance 82

Insurance Risk Data-driven Finance 82

Cloudera

APRIL 27, 2022

Predictive maintenance applications enable large-scale manufacturers to collect telemetry data and integrate all IoT functions, and these are powered by models driven by real-time data. . The financial services industry has had to dedicate more resources to personalisation, fighting fraud, and reducing cloud concentration risk.

IoT 99

IoT IT Manufacturing Internet of Things 99

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.

IoT 114

IoT Internet of Things Cost-Benefit Testing 114

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.

Experimentation 52

Experimentation Cost-Benefit Predictive Modeling Risk 52

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.

Testing 130

Testing Cost-Benefit Metrics Data Quality 130

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.

Data Governance 69

Data Governance Predictive Modeling Data-driven Visualization 69

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.

Risk Management 52

Risk Management Risk Insurance Predictive Modeling 52

CIO Business Intelligence

MARCH 22, 2023

Enter the new class ML data scientists require large quantities of data to train machine learning models. Then the trained models become consumers of vast amounts of data to gain insights to inform business decisions. In the training phase, the primary objective is to use existing examples to train a model.

Machine Learning 105

Machine Learning Cost-Benefit Modeling Risk 105

BizAcuity

NOVEMBER 13, 2024

Making decisions based on data, rather than intuition alone, brings benefits such as increased accuracy, reduced risks, and deeper customer insights. Advanced Analytics and Predictive Insights The real value of data lies in its ability to forecast trends and identify opportunities.

Consulting 52

Consulting Data-driven Cost-Benefit Business Intelligence 52

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