Risk Flow Academy
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Curriculum
30
mins1
.
Intake Method
This module explores the role of internal and external data sources in digital risk processing. It covers different data types, examines data enrichment techniques and introduces the five pillars of data analytics strategy, offering insight into how data shapes decision-making and risk assessment.
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45
mins2
.
Classification
This module offers a thorough introduction to classification systems and taxonomy design, crucial for efficient data management. It encompasses fundamental concepts, strategies, and iterative methods for optimising taxonomies, aiming to improve classification accuracy and usability.
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45
mins3
.
Extraction
This module is a comprehensive guide to implementing data extraction techniques. It covers foundational concepts, deployment strategies, and optimization methods to meet specific success criteria. Emphasising practical methodologies and best practices, it ensures effective data extraction.
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45
mins4
.
Validation
This module explores validation in classification tasks, covering principles, approaches, and techniques. It examines methods for evaluating classification steps and results, emphasising practical application and critical analysis for robust validation practices.
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60
mins5
.
Data Sources
This module explores the role of internal and external data sources in digital risk processing. It covers different data types, examines data enrichment techniques and introduces the five pillars of data analytics strategy, offering insight into how data shapes decision-making and risk assessment.
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45
mins6
.
Risk Flows
This module offers a deep dive into digitization's impact on risk processing workflows. It covers existing digital workflows, highlights the benefits of continuous optimization, and discusses the influence of digital workflows on productivity and broker experience.
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60
mins7
.
Schema
This module provides a guide to creating schemas, extractors, and workflows for data extraction and processing. It includes information on the submission process to ensure accurate and effective data extraction workflows.
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30
mins8
.
Outputs
This module offers an exploration of output connectors in digital risk processing , highlighting the significance of "Human in the Loop" for data accuracy and examines the functionalities of the Console: Inbox, focusing on its role in managing exceptions.
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1
.
The Role of Data in the Underwriting Process
coming soon
1
.
The Role of Data in the Underwriting Process
Data Sources
2
.
Automated LLM (Large Language Models) Driven Technology
coming soon
2
.
Automated LLM (Large Language Models) Driven Technology
Classification
2
.
Approaches and Techniques used in the Validation Process
coming soon
2
.
Approaches and Techniques used in the Validation Process
Validation
2
.
Digital risk processings: the existing workflows
coming soon
2
.
Digital risk processings: the existing workflows
Risk Flows
2
.
Challenges and Complexities in Extraction
coming soon
2
.
Challenges and Complexities in Extraction
Extraction
3
.
Challenges experienced in receiving and collecting items
coming soon
3
.
Challenges experienced in receiving and collecting items
Intake Method
3
.
Evaluation of the Classification Steps
coming soon
3
.
Evaluation of the Classification Steps
Validation
3
.
Continuous Optimisation: what do digital workflows solve
coming soon
3
.
Continuous Optimisation: what do digital workflows solve
Risk Flows
3
.
Maximising the value of digitized risk data
coming soon
3
.
Maximising the value of digitized risk data
Extraction
5
.
Application Programming Interface (API)
coming soon
5
.
Application Programming Interface (API)
Intake Method
5
.
Leveraging Digital Risk Processing platforms for Data Extraction
coming soon
5
.
Leveraging Digital Risk Processing platforms for Data Extraction
Extraction
7
.
Cytora Configuration of Classification and Extraction
coming soon
7
.
Cytora Configuration of Classification and Extraction
Classification
9
.
The Five Pillars of Data Analytics Strategy in Insurance
coming soon
9
.
The Five Pillars of Data Analytics Strategy in Insurance
Data Sources
