5-week assessment and small pilot designed for the insurance sector to get you started with your Azure Analytics adoption.
Adastra will evaluate your current analytics state, define new analytic goals, and design a modern cloud architecture and roadmap, with Azure and Services costing, to achieve your analytic goals.
We align business, people, and technology strategy to achieve business goals with actionable, efficient, and comprehensive guidance to deliver fast results.
Evaluation of your current analytics environments and use cases:
Claims Risk Analysis: Leverage Adastra’s Azure deployed claims risk analysis solution, to detect potentially fraudulent insurance claims. Trains models from insurance claims history, to automatically identify fraudulent likelihood for new claims. Used to trigger claim investigation. Supports sophisticated querying via graph database, to perform deeper analysis on any claim the model predicted as fraudulent.
ATO Risk Analysis: Leverage Adastra’s Azure deployed Account Takeover risk analysis solution, to detect potential account takeover attempts by bad actors. Parses customer and call center data to evaluate the likelihood that interactions are ATO attempts. Warns agents in real-time, to trigger risk remediation actions. Supports ongoing retraining and model improvements, to respond to new attack vectors.
Claims Trend Analysis: Leverage Adastra’s Azure deployed claims trend analysis solution, to better identify causation for claims trends, such as seasonal hammock effects. Identifies the causes for claims patterns, to facilitate better mitigation and forecasting efforts. Enables improved workforce planning, for example, to ensure customer service levels are maintained for peaks and costs are reduced for valleys.
Product Funnel View: Evaluate insurance product performance through a funnel view, tracking product progression across application initiated, completed, approved, activated, utilized, and attrition phases. Correlate investments in marketing to product funnel impact. Analyze product performance by different channels (i.e. digital vs agent).
Document Processing and Mining: Improve customer service and reduce manual effort by automating manual analysis of customer documents. Uses AI/ML techniques to scan, score, and translate customer collateral (proof of ownership, identity documents, etc), to reduce insurance risk and speed onboarding of customers. Supports real-time product onboarding for customers, and enables a transition from paper based processes.
Digital Onboarding: Improve customer service and lower customer onboarding cost by enabling real-time digital onboarding for customers. Takes processes that historically required in person agent interactions and days to complete, and switches them to online real-time. Uses automation to execute process steps (security checks, credit checks, document validation, …) immediately.
PII Data Protection: Ensure sensitive customer / employee data is never stored in plain text, in Azure. From data entry, enables in flight encryption / decryption of data, so PII data never lands in plain text, in any step of the data journey. Adastra framework leverages native Azure tools or Adastra’s PII Protector product. Reduces organizational data risk, simplifies data protection, and creates consistency for data protection.
Enrollment Prediction: Leverage Adastra AI / ML models to predict whether customers will enroll and activate new insurance products. Train models from historical customer behaviour across relevant attributes, then predicts whether potential new product customers would enroll. Facilitates targeted marketing based on enrollment scoring.
Predictive Systems Maintenance: Leverage Azure deployed Adastra AI / ML models to predict potential critical system outages. Critical insurance systems require > 99.9% uptime, so ensuring effective system availability and performance via predictive maintenance is a key step to achieve SLA’s. Our solution monitors critical bank systems for outage indi