Business Analytics Modeling Predictive Modeling Risk 
Smarten
SEPTEMBER 14, 2018
Just Simple, Assisted Predictive Modeling for Every Business User! No matter the market or type of business, there is no room in today’s business landscape for guesswork. And, with Assisted Predictive Modeling , you can make these tasks even easier. No Guesswork!
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. For predictive analytics to deliver high accuracy, a lot depends on the combination of domain knowledge and technical expertise.
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FineReport
APRIL 9, 2024
Evolving BI Tools in 2024 Significance of Business Intelligence In 2024, the role of business intelligence software tools is more crucial than ever, with businesses increasingly relying on data analysis for informed decision-making.
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. Predictive Analytics can help businesses in reducing risk (eg.
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. What are the four types of data analytics? In business analytics, this is the purview of business intelligence (BI).
Smarten
JUNE 27, 2018
To explore this technique further, let’s conduct the SVM classification using the following variables: Here we see a sample output for the actual versus predicted outcome. The prediction accuracy is useful criterion for assessing the model performance. Model with prediction accuracy >= 70% is useful.
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. Differing levels of detail: SKUs/ Products vs Product Groups/ Lines of Business.
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