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More specifically: Descriptive analytics uses historical and current data from multiple sources to describe the present state, or a specified historical state, by identifying trends and patterns. Predictive analytics is often considered a type of “advanced analytics,” and frequently depends on machine learning and/or deep learning.
The output of these algorithms, when used in financial services, can be anything from a customer behavior score to a prediction of future trading trends, to flagging a fraudulent insurance claim. Once an accurate predictor of future behavior is identified, integrate the scoring measures directly into the data model.
The most poignant for me was a simple approach for measuring noise within an organization. To do this, first review quantitative decisions being made by staff – for example, settlement prices quoted by insurance claims adjusters. Measure how these decisions vary across your population.
As such banking, finance, insurance and media are good examples of information-based industries compared to manufacturing, retail, and so on. See Roadmap for Data Literacy and Data-Driven Business Transformation: A Gartner Trend Insight Report and also The Future of Data and Analytics: Reengineering the Decision, 2025. Great idea.
Augmented Analytics. DI empowers analysts to apply augmented analytics to applications, supporting predictive and prescriptiveanalytics use cases. Similarly, retailers and other less regulated industries are boosting defensive measures to ensure compliance with GDPR and CCPA. Why reinvent the wheel? Who does that?
Artificial intelligence (AI)-enabled systems are driving a new era of business transformation, revolutionizing industries through prescriptiveanalytics, personalized customer experiences and process automation. In regulated industries like finance, healthcare and insurance, XAI supports auditability, compliance and ethical AI.
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