Insurance — Risk Analysis and Strategic Decision Support Pipeline

Overview

The primary purpose of the assurance_kmds_risk_analysis DAG is to provide an in-depth analysis of risks associated with insurance claims and fraud, ultimately aiding strategic decision-making. The architecture of this DAG is designed to collect both historical and current data from various sources, including claims reports, fraud detection logs, and market trends. The ingestion pipeline begins with the extraction of these data sources, followed by a series of processing and transformation steps

The primary purpose of the assurance_kmds_risk_analysis DAG is to provide an in-depth analysis of risks associated with insurance claims and fraud, ultimately aiding strategic decision-making. The architecture of this DAG is designed to collect both historical and current data from various sources, including claims reports, fraud detection logs, and market trends. The ingestion pipeline begins with the extraction of these data sources, followed by a series of processing and transformation steps that utilize advanced analytical models to evaluate trends and identify potential risks. The processing logic involves data cleansing, normalization, and the application of predictive analytics to uncover insights into loss potential and fraud patterns. The outputs of this DAG include interactive dashboards that present key performance indicators (KPIs) such as potential loss forecasts, claims trends, and fraud risk assessments. Monitoring these KPIs allows stakeholders to assess risk exposure and make informed decisions. The business value of this DAG lies in its ability to enhance risk management strategies, improve operational efficiency, and ultimately drive profitability by enabling proactive decision-making based on data-driven insights.

Part of the Supply/Demand Forecast solution for the Insurance industry.

Use cases

• Improved risk assessment capabilities for insurance providers
• Enhanced strategic decision-making based on data insights
• Increased operational efficiency through automated processes
• Proactive identification of fraud, reducing financial losses
• Better alignment of resources with market demand and risk