Risk Flow Academy
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Curriculum

1
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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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Chapters:
2
.
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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Chapters:
3
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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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Chapters:
4
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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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Chapters:
5
.
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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Chapters:
6
.
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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Chapters:
7
.
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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Chapters:
8
.
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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Chapters:
1
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Introducing Cytora

Intake Method

1
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Introduction to Classification

Classification

1
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Introduction to Extraction

Extraction

1
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Introduction to Validation

Validation

1
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Introduction: Digitising the Workflow

Risk Flows

1
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Introduction to Schema

Schema

1
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The Role of Data in the Underwriting Process

Data Sources

1
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Output connectors

Outputs

2
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How the Input process begins

Intake Method

2
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Classification Deployment Strategy

Classification

2
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Data Extraction Deployment Strategy

Extraction

2
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Approaches and Techniques used in the Validation Process

Validation

2
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Digital risk processings: the existing workflows

Risk Flows

2
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Creating a Schema

Schema

2
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Data Standardisation

Data Sources

2
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Console: Inbox

Outputs

3
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Challenges experienced in receiving and collecting items

Intake Method

3
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Defining a Taxonomy

Classification

3
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Success criteria for evaluating the effectiveness of data extraction

Extraction

3
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Evaluation of the Classification Steps

Validation

3
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Continuous Optimisation: what do digital workflows solve

Risk Flows

3
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Question Block

Schema

3
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Answer Block

Schema

3
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Data Integration

Data Sources

3
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Concept: Human in the Loop

Outputs

4
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Input Methods

Intake Method

4
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Iterative Approach to Taxonomy Optimisation

Classification

4
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Strategies and methodologies for effectively deploying data extraction

Extraction

4
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Evaluation of Results

Validation

4
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Productivity and Broker Experience

Risk Flows

4
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Creating an Extractor

Schema

4
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The Four Types of Data

Data Sources

4
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Exceptions

Outputs

5
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Application Programming Interface (API)

Intake Method

5
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Creating a Workflow

Schema

5
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Data vs Inferred Data

Data Sources

6
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Fully Digitised Intake

Intake Method

6
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Uploading submissions for training

Schema

6
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Where data comes from

Data Sources

7
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Training an Extractor

Schema

7
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Where inferred data comes from

Data Sources

8
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Submissions

Schema

8
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Data Enrichment

Data Sources

9
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The Five Pillars of Data Analytics Strategy in Insurance

Data Sources

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