Insurance Accelerator Report has been designed after extensive research with insurance industry experts. We have incorporated Insurance KPIs and thoughtful insights in this report.
We have built a standard reporting structure for all the insurance sub-functions (Sales, Claim Management, Fraud detection, Employee performance), with a functional coverage of 45-60%*.
Key features of Insurance report:
*Quick to plug-in the Customer Data Model with Pre-Build Data Model through mapping key dimensions and measures (metrics) using Power BI Dataflows.
*Inbuilt accurate Claim Fraudulence detection analytics using 8 proven data science algorithms like Gradient Boost, Random Forest and inbuilt sampling techniques.
*Report theme used in the report is easily configurable through configuration file that comes with the report.
*Covered policies for Life, Vehicle and Automobile insurance
*Tracking the clients by Lead channel like company website, traditional media, email marketing, search engine, database.
We have brainstormed with our team and come up with the list of dimension and fact tables and the fields required for this usecase. We have added sample data in Excel files which is used as the source file for this report.
In this report, we are focussing on US and Canada data.
The Power BI data model is built using Star schema.
The report has 4 dashboards namely- Sales Overview, Claim Management, Fraudulence Claim and Employee Performance.
Sales Overview dashboard gives the Summary of the Sales in US and Canada region.
Key Insights:
*Insurance KPIs like Total Policies, Total Clients who have purchased policies, Total Policy Amount and the Premium Paid Amount
*Tree Map charts for # Clients by Lead Channels and Sales Amount by Policy type.
*Stacked Column Chart with # Clients by Policies and Policy Types.
*Map represents Sales overview of US and Canada.
*Interactive filter selection at period, policy type, region level.
Claim Management dashboard gives insights on claims by various dimensions like gender, claim status, marital status, reason, policy name.
Key Insights:
*KPIs like Total Claims, Claim Amount. Insightful KPIs like Average Cost Per Claim and Clam Frequency by Month.
*Claim Amount by Gender and Marital Status, Reason and Policy Name shown in bar charts.
*Claims by Claim Status shown in Pie chart.
*Interactive filter selection at period and region level.
Fraudulence Claim dashboard is the key dashboard of this report. It uses data science alogorithms to predict fraudulence claims.
This dashboard has 5 tabs- Summary, Accuracy, Precision, Recall and F1-Score.
Summary tab: This tab gives the fraud prediction by incident type, police report, by gender, etc.
Accuracy tab: Accuracy is the ratio of correct predictions out of all predictions made by an algorithm.Logistic regression shows higher accuracy of 73%.
Precision tab: Precision is the measure of correctly identified positive cases from all predicted positive cases. Random Forest shows higher precision of 59%.
Recall tab: Recall is the measure of correctly identified positive cases from all the actual positive cases. Logistic regression has highest recall of 93%.
F1 score is the harmonic mean of precision and recall and gives a better measure of the incorrectly classified cases than the accuracy matrix.Smote has the highest average F1 score at 60.36%.
Employee Performance dashboard describes the role of employees in converting Leads to Sales,their call answered ratio, etc.
Key Insights:
*KPIs like Total Calls, Total employees and Leads. Insightful KPIs like Answered Ratio and Sales Ratio.
*Line charts showing Average Call duration in seconds, and Sales Converted by Employees
*#Clients by Lead Status in Pie chart.
*The Narration gives the summary of the employee performance.
*Interactive filter selection at period, employee level.
This report is a sample to showcase our capabilities. We also have experience in working with Power BI Dataflows, working on complex data transformation using DAX queries, implementing security,
performing master data management, working with different Power BI data sources.
We have a team of high performing data engineers and data scientists with python and machine learning capabilities who have helped in building this report for us.
Please, visit our website https://www.preludesys.com/power-bi-reporting/ and check LinkedIn profile. We are open to cooperation and are ready to work on new interesting projects.
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