Regulatory frameworks like Solvency II, IFRS 17, and APRA CPS 234 require accurate, auditable, and timely reporting. Without trusted data, meeting these requirements becomes a costly challenge.
Eliminate errors in financial and regulatory reporting with a single source of truth.
Reduce manual effort and reporting delays with built-in validation and lineage tracking.
Ensure transparency and accountability with complete data traceability.
Meet diverse global and regional regulatory requirements from a unified platform.
Fragmented data across multiple systems makes it difficult to personalize interactions, cross-sell effectively, and maintain accurate records for compliance.
Improve service quality with accurate, real-time policyholder information across all teams.
Identify cross-sell and upsell opportunities by understanding customer needs and behaviors.
Eliminate duplicate records and manual data reconciliation, freeing up resources.
Maintain unified, auditable customer records to meet privacy and consent regulations.
Without accurate, well-structured claims data, you can struggle to process claims efficiently, forecast claims volume, and maximize recoveries. This impacts both customer satisfaction and financial performance.
Reduce delays and improve customer satisfaction with clean, validated claims data.
Use high-quality historical data to predict claims trends and optimize resource allocation.
Identify and maximize financial recovery opportunities with reliable data.
Strengthen anomaly detection efforts with consistent, high-quality claims data.
Poor consent and privacy data management increases compliance risks and erodes customer trust. Regulations like Pipeda, and CCPA require insurers to manage customer data securely. Without having a centralized system, inconsistencies in consent records can lead to compliance violations, and reputational damage.
Meet data privacy laws like gdpr, pipeda, and ccpa with accurate, auditable consent records.
Ensure data privacy preferences are respected, improving customer confidence and loyalty.
Reduce errors and redundant processes with a centralized approach to consent management.
Prevent fines and reputational damage by maintaining accurate records of customer data usage.
Inaccurate or incomplete claims data makes it difficult to detect suspicious patterns early and leads to higher fraudulent payouts, and potential non-compliance with fraud prevention regulations.
Identify suspicious claims faster with accurate, consistent data.
Minimize fraudulent payouts by improving risk assessment.
Streamline fraud investigations with clean, well-structured claims data.
Maintain compliance with fraud prevention regulations by ensuring data transparency.
Inaccurate risk assessments caused by poor data quality result in premiums that don’t reflect actual risk and can lead to higher claims costs, lost revenue opportunities, and pricing inefficiencies.
Use accurate data to align pricing with actual risk profiles.
Reduce financial exposure by eliminating data inconsistencies in underwriting.
Enable faster, more efficient underwriting with a unified source of truth.
Leverage complete and reliable data to refine premium strategies.
Legacy system upgrades and migrations to modern platforms like Guidewire or Duck Creek are critical for insurers, but poor data quality leads to inefficiencies, compliance violations, and customer service issues during the transition.
Ensure high-quality, structured data before moving to new core systems.
Prevent errors, duplicate records, and data mismatches post-migration.
Maintain data integrity to meet industry regulations during migrations.
Speed up migration timelines with clean, validated data from day one.
Insurers are adopting AI for underwriting, fraud detection, and customer personalization, but without high-quality, well-structured data, AI models produce unreliable insights, increasing financial and compliance risks.
Improve AI-driven underwriting and claims assessments with clean, structured data.
Enable AI to detect anomalies more effectively with consistent, reliable datasets.
Personalize insurance offerings using AI trained on high-quality customer data.
Catch and remediate issues early and maintain baseline data quality for ML training data.
Ensure AI models align with ethical and regulatory standards by eliminating data bias and inconsistencies.
Insurers rely on operational reports to track claims performance, policy trends, and regulatory metrics. Poor data quality results in reporting errors, delays, and misalignment between departments, impacting strategic and financial planning.
Make data-driven decisions with complete, reliable reports.
Ensure reports align with industry standards and compliance requirements.
Reduce manual data corrections and reconciliation efforts.
Maintain consistency in reporting across underwriting, claims, and finance.
Insurers handle massive amounts of data across claims processing, policy updates, and compliance reporting. Without automation, these tasks become inefficient, error-prone, and costly, limiting scalability and business agility.
Automate data validation and approvals to reduce turnaround times.
Eliminate manual data entry errors for better risk assessment.
Streamline reporting and auditing processes with rule-based automation.
Free up resources by reducing repetitive, manual tasks in policy and claims management.
When insurers merge or acquire new entities, they must consolidate data from multiple legacy systems. Without a structured integration process, discrepancies in customer records, policies, and claims data can cause compliance risks, inefficiencies, and reporting challenges.
Standardize and consolidate customer, policy, and claims data.
Reduce time to operational efficiency with automated data mapping and cleansing.
Maintain compliance across jurisdictions with validated, auditable records.
Minimize disruptions by ensuring data consistency across merged entities.