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 112

IT Software Risk Management 112

IBM Big Data Hub

DECEMBER 4, 2023

Risk management To make underwriting decisions related to property, insurance companies gather a significant amount of external data, including the property data provided in insurance application forms, historical records of floods, hurricanes, fire incidents and crime statistics for the specific location of the property.

Insurance 53

Insurance Digital Transformation Risk Management Interactive 53

FineReport

MARCH 24, 2024

Data analysts contribute value to organizations by uncovering trends, patterns, and insights through data gathering, cleaning, and statistical analysis. They identify and interpret trends in complex datasets, optimize statistical results, and maintain databases while devising new data collection processes.

Statistics 52

Statistics Data mining Visualization Reporting 52

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

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. Predictive Analytics can help businesses in reducing risk (eg. Predictive Analytics.

Machine Learning 52

Machine Learning Predictive Analytics Predictive Modeling Risk 52

CIO Business Intelligence

OCTOBER 13, 2023

It’s a role that requires experience with natural language processing , coding languages, statistical models, and large language and generative AI models. The role of algorithm engineer requires knowledge of programming languages, testing and debugging, documentation, and of course algorithm design.

Deep Learning 142

Deep Learning Machine Learning Modeling Software 142

Smart Data Collective

SEPTEMBER 21, 2022

As far as Data Analysis is concerned, potential employees should have an extensive knowledge of quantitative research, quantitative reporting, compiling statistics, statistical analysis, data mining, and big data. The old adage that you can build a better mousetrap and the world will beat a path to your door doesn’t hold up.

Statistics 85

Statistics Software Data mining Marketing 85

O'Reilly on Data

JULY 15, 2019

In recent posts, we described requisite foundational technologies needed to sustain machine learning practices within organizations, and specialized tools for model development, model governance, and model operations/testing/monitoring. Note that the emphasis of SR 11-7 is on risk management.). Sources of model risk.

Machine Learning 38

Machine Learning Management Enterprise Risk Management 38

Domino Data Lab

AUGUST 10, 2021

Models can predict things before they happen more accurately than humans, such as catastrophic weather events or who is at risk of imminent death in a hospital. This is useful for grouping unstructured data based on statistical properties. Managing Model Risk. This comes down to model risk management.

Modeling 64

Modeling IT Risk Management Deep Learning 64

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. I held out 20% of this as a test set and used the remainder for training and validation.

Risk 67

Risk ROI Modeling Metrics 67

CIO Business Intelligence

DECEMBER 6, 2023

Organizations need to have an honest look at their hygiene, priorities, and data sensitivities to ensure they’re plugging GenAI tools into the areas where they get maximum reward and minimized risk.” Assume AI is always right Impressive as they are, generative AI tools are inherently probabilistic.

IT 127

IT Risk Management Interactive Modeling 127

O'Reilly on Data

OCTOBER 18, 2022

When we use AI in security applications, the risks become even more direct. As AI technologies are adopted more broadly in security and other high-risk applications, we’ll all need to know more about AI audit and risk management. Data can be wrong. Predictions can be wrong. System designs can be wrong.

Risk Management 297

Risk Management Risk Testing Measurement 297

datapine

AUGUST 29, 2022

The book has three main ideas: The biggest risk your company faces is investing a lot of time and resources into building something that the market doesn’t want. Additionally, numerous case studies on risk management, fraud detection, customer relationship management, and web analytics are included and described in detail.

Big Data 263

Big Data Data Analytics Analytics Data mining 263

CIO Business Intelligence

JULY 6, 2023

Another benefit is greater risk management. Using automation technologies helps meet client expectations and ensures consistency, while lowering risks that can be attributed to human error.” Another good practice is to test and learn from solutions early and often. Secure sponsorship. “By Pilot to accelerate results.

Cost-Benefit 114

Cost-Benefit Technology Unstructured Data Testing 114

Andrew White

JANUARY 11, 2021

Some data is more a risk than valuable. Value Management or monetization. Risk Management (most likely within context of governance). Product Management. Storytelling is a nice one to use early on to test the approach. Saul Judah is our main person focusing on D&A risk management.

Data Analytics 52

Data Analytics Analytics Data-driven Finance 52