edX
MOOC / Non-credit
0
Machine Learning Use Cases in Finance
About this course
In the last six years, the financial sector has seen an increase in the use of machine learning models in financial, banking and insurance contexts. Data science and advanced analytics teams in the financial and insurance community are implementing these models regularly and have found a place for them in their toolbox.
C
59/100
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- 23/45
- Who stands behind it
- 20/35
- How complete the listing is
- 16/20
Scores how much the provider publishes and who stands behind it — not how well it is taught.
What you'll learn
- Identify common financial industry problems that are addressed with machine learning
- Explain the data requirements and preprocessing steps for finance‑focused ML projects
- Compare model types such as regression, classification and time‑series methods for banking and insurance use cases
- Interpret evaluation metrics and model performance in a financial risk context
- Outline governance, compliance and ethical considerations for deploying ML in finance
Course objectives
- Expose learners to actual ML implementations in finance and insurance
- Provide frameworks for selecting and validating models for financial tasks
- Illustrate the end‑to‑end workflow from data acquisition to model monitoring in financial settings
Machine Learning
Financial Analysis
#machine learning
#financial modeling
#data science
#banking
#predictive analytics
#analytics
#finance
#data-driven decisions
#insurance
#stats modeling
#credit scoring
#fraud detection
#risk modeling
#time series forecasting
#model evaluation
#data preprocessing
#regression
#classification
#model governance
#compliance
#ethical ai
$189.00
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