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.

Risk Management 130

Risk Management Risk Cost-Benefit Technology 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. In addition to these advancements, another prominent trend in data analysis is the growing impact of data visualization.

Business Intelligence 52

Business Intelligence Software Data-driven Predictive Modeling 52

Smart Data Collective

SEPTEMBER 17, 2020

Nowadays, terms like ‘Data Analytics,’ ‘Data Visualization,’ and ‘Big Data’ have become quite popular. Here are a few of the advantages of Big Data in the banking and financial industry: Improvement in risk management operations. Big Data provides financial and banking organizations with better risk coverage.

Big Data 142

Big Data Finance Risk Management Risk 142

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. EDA is used to analyze data and summarize their main properties and characteristics using visual techniques.

Machine Learning 52

Machine Learning Predictive Analytics Predictive Modeling Risk 52

O'Reilly on Data

DECEMBER 12, 2019

Because all ML models make mistakes, everyone who cares about ML should also care about model debugging. [1] 1] This includes C-suite executives, front-line data scientists, and risk, legal, and compliance personnel. That’s where model debugging comes in. Interpretable ML models and explainable ML.

Machine Learning 191

Machine Learning Modeling Testing Risk Management 191

CIO Business Intelligence

AUGUST 23, 2023

To do so, they explored the optimization problem of “cardinality constraints” and developed a hybrid quantum-classical approach to financial index tracking portfolios that maximizes returns and minimizes risk. We started our models, pressed run, and had to wait until the morning and hope they hadn’t crashed overnight,” Broer says.

IT 97

IT Machine Learning Risk Optimization 97