Elevate your business with insurance analytics solutions powered by data and AI from SAS

Understand customer behaviors, preferences and needs, then turn those insights into more relevant experiences. AI helps insurers uncover meaningful patterns in customer data, create more precise audiences and orchestrate personalized journeys across channels to strengthen engagement, loyalty and growth.

The value of this solution:

With SAS Customer Intelligence 360, insurers can:

• Develop a more complete understanding of customer behaviors, preferences and needs.
• Identify meaningful patterns and changes across customer interactions.
• Create more precise audiences and customer segments based on behaviors, characteristics and context.
• Orchestrate personalized customer journeys across channels.
• Deliver real-time personalization at scale using customer data across multiple sources.
• Use agentic AI to accelerate audience and journey creation and recommend opportunities for continuous improvement.
• Identify the next-best offer or action based on customer needs, behaviors and context.

AI techniques used in this solution:

• Agentic AI (AI agents)
• Generative AI
• Large language models (LLMs)
• Machine learning
• Deep learning
• Predictive analytics
• Natural language processing
• Computer vision

Machine learning, deep learning and predictive analytics identify patterns, relationships and changes in customer behavior to help insurers better understand customer needs and identify relevant audiences, offers and actions. Natural language processing and large language models help interpret customer interactions and other unstructured information. Agentic AI helps marketers turn these insights into audiences and personalized customer journeys while maintaining human oversight and governance. Computer vision can also help transform images and whiteboard concepts into actionable journeys.

How AI helps:

• Develop deeper insights into customer behaviors, preferences and changing needs.
• Enhance customer segmentation and audience creation.
• Deliver more relevant, personalized customer experiences.
• Create consistent omnichannel engagement across digital and traditional channels.
• Respond to customer needs with the right offer or action at the right time.
• Improve marketing efficiency and productivity.
• Strengthen customer satisfaction, engagement and retention.
• Identify opportunities to deepen customer relationships and drive profitable growth.

Detect suspicious claims faster and uncover hidden connections that traditional rules and manual reviews can miss. AI and intelligent decisioning help insurers identify potential fraud earlier, prioritize high-risk cases and focus investigators where they can have the greatest impact.

The value of this solution:

With SAS Fraud Decisioning for Claims, insurers can:

• Bring decades of policy, claims, customer and other relevant data together to create a more complete view of potential fraud.
• Analyze millions of claims and uncover hidden relationships among claimants, providers and other parties that may indicate organized fraud or collusion.
• Use machine learning to continuously improve fraud detection as new cases and investigation outcomes become available.
• Automate risk scoring and decisioning to identify and prioritize suspicious claims for investigation.

AI techniques used in this solution:

• Machine learning
• Intelligent decisioning
• Network analytics
• Advanced analytics

How AI helps:

• Detect suspicious claims and hidden relationships faster.
• Reduce losses from fraudulent or illegitimate claims.
• Separate meaningful fraud indicators from noise.
• Categorize potential fraud by type and risk.
• Assign risk scores and prioritize high-risk cases.
• Improve investigator productivity and focus.
• Support faster, more consistent fraud decisions.

Anticipate where flooding may occur before it causes significant damage. AI-powered location intelligence combines environmental data, sensor information and advanced flood modeling to help insurers understand changing risks, alert customers and take action to protect property and lives.

The value of this solution:

Using SAS flood analytics and IoT capabilities, insurers can:

• Predict, prepare for and respond to potential flooding events.
• Combine weather, geographic and real-time sensor data to create a more complete view of emerging flood risk.
• Monitor changing conditions and identify properties and locations most likely to be affected.
• Provide timely alerts and risk insights that help customers and businesses take preventive and life-saving action.
• Strengthen risk management with more precise, location-specific flood intelligence.

AI techniques used in this solution:

• Digital twins
• Synthetic data
• Machine learning
• Advanced hydrological modeling
• Location intelligence

Digital twins and advanced hydrological models simulate changing flood conditions using real-time sensor, weather and geographic data. Machine learning and synthetic data enhance these models to help predict where flooding may occur and how conditions could evolve, while location intelligence translates those insights into more precise, property-level risk assessments.

How AI helps:

• Assess the risk of flood loss at individual properties and locations using a grid-based approach, from 250 ft. by 250 ft. down to as granular as 10 ft. by 10 ft.
• Predict the location and depth of potential flooding using flood inundation simulations based on GIS data, real-time sensor information and AI.
• Deliver more precise, location-specific insights to help carriers and brokers understand where flood losses are most likely to occur.
• Provide earlier warning of changing flood conditions.
• Send timely alerts to insurers, brokers, businesses and customers so they can take action to help prevent property damage and loss of life.

Empower actuaries and data scientists to assess risk, losses, expenses and profitability quickly and accurately. AI and machine learning help insurers uncover complex relationships in data, refine pricing segmentation and respond to changing market conditions while maintaining transparency and governance.

The value of this solution:

With SAS Dynamic Actuarial Modeling, insurers can:

• Improve pricing accuracy with more sophisticated risk segmentation.
• Analyze large volumes of policyholder and external data more efficiently.
• Accelerate actuarial modeling and pricing processes.
• Identify opportunities to improve profitability and competitive positioning.
• Adapt pricing strategies more quickly as risks and market conditions change.

AI techniques used in this solution:

• Machine learning
• Predictive modeling
• Advanced analytics
• Explainable AI

Machine learning and predictive modeling analyze large volumes of data to uncover complex relationships and identify factors that influence risk and loss. Advanced analytics helps actuaries develop more granular pricing segments, while explainable AI provides greater transparency into model outputs to support regulatory requirements, governance and stakeholder trust.

How AI helps:

• Improve pricing accuracy and risk segmentation.
• Strengthen competitive positioning by incorporating new and real-time data.
• Optimize actuarial resources and improve productivity.
• Respond more quickly to changing risks and market conditions.
• Support customer retention through more precise, risk-based pricing.
• Increase transparency and trust with regulators and policyholders.

Accelerate claims processing and improve decision-making with AI-powered insights and automation. AI helps insurers analyze claim information, extract evidence and validate claims faster, enabling adjusters and investigators to focus their attention where human expertise matters most.

The value of this solution:

With SAS Intelligent Decisioning, insurers can:

• Accelerate claims processing and settlement with automated, data-driven decisions.
• Analyze claim information and supporting evidence more efficiently.
• Improve first notice of loss decisions with faster access to relevant insights.
• Reduce unnecessary manual reviews and investigations.
• Deliver faster, more seamless experiences for policyholders.

AI techniques used in this solution: 

• Agentic AI
• Machine learning
• Advanced analytics
• Intelligent decisioning
• Network analytics

Machine learning and advanced analytics automate data analysis and evidence extraction to help insurers make faster, more informed claims decisions. Network analytics uncovers patterns and relationships that may require further investigation, while intelligent decisioning applies insights consistently throughout the claims process. Agentic AI accelerates analysis by interrogating multiple data sources to help validate claims and move legitimate cases toward resolution faster.

How AI helps:

• Give adjusters and investigators faster access to comprehensive claim information.
• Accelerate first notice of loss, total loss and settlement decisions.
• Reduce unnecessary and intrusive investigations for legitimate claims.
• Identify patterns and relationships that support more informed decisions.
• Reduce false positives and unnecessary manual reviews.
• Improve operational efficiency and enable staff to focus on higher-value tasks.
• Deliver faster, more seamless customer experiences.

Detect and adapt to evolving fraud threats while delivering seamless digital experiences. AI, machine learning and advanced analytics help insurers validate digital identities, identify suspicious activity in real time and streamline interactions for legitimate customers.

The value of this solution:

With SAS Identity 360 and SAS Fraud Decisioning, insurers can:

• Validate digital identities and identify potential fraud in real time.
• Detect and adapt to evolving fraud patterns and emerging threats.
• Reduce false positives and unnecessary customer friction.
• Increase straight-through processing for legitimate customers.
• Strengthen fraud prevention while supporting seamless digital experiences.

AI techniques used in this solution:

• Agentic AI
• Machine learning
• Anomaly detection
• Advanced analytics
• Hybrid analytics
• Champion and challenger modeling

How AI helps:

• Protect the integrity of digital customer journeys.
• Authenticate digital identities in real time.
• Detect evolving fraud patterns and emerging attack methods.
• Reduce false positives and unnecessary investigations.
• Increase straight-through processing for legitimate interactions.
• Reduce customer friction while strengthening fraud prevention.
• Improve investigator efficiency and reduce the cost of fraud operations.

Accelerate claims decisions while detecting manipulated, synthetic and AI-generated documents that may evade traditional fraud controls. Multimodal AI analyzes images, text and supporting claim information to authenticate documents, uncover fraud indicators and turn unstructured content into actionable insights while reducing manual review.

The value of this solution:

With SAS multimodal AI and document vision capabilities, insurers can:

• Detect synthetic receipts, altered estimates and manipulated supporting documentation before claims are settled.
• Automate the extraction and analysis of critical information from claims documents and images.
• Analyze document authenticity, text and fraud indicators using multiple AI techniques.
• Combine document analysis with claims and historical data to provide additional fraud context.
• Generate fraud risk scores and supporting insights to help prioritize suspicious submissions.
• Reduce manual reviews and accelerate legitimate claim approvals.
• Give adjusters and investigators more complete information to support faster, more confident decisions.

AI techniques used in this solution:

• Multimodal AI
• Computer vision
• Optical character recognition (OCR)
• Large language models (LLMs)
• Machine learning
• Intelligent document processing
• Intelligent decisioning

Multimodal AI combines computer vision, OCR, large language models and machine learning to analyze document authenticity, text and potential fraud indicators from multiple perspectives. These insights can be combined with claims and historical data to provide additional context and generate a consolidated fraud risk score. Intelligent decisioning then applies configurable thresholds, business rules and workflows to help automate decisions while escalating uncertain cases for human review.

How AI helps:

• Identify manipulated, synthetic and deepfake documents that may evade traditional fraud controls.
• Detect potentially fraudulent receipts, repair estimates and supporting claims documentation earlier in the claims process.
• Analyze images, text and structured data together for a more holistic fraud assessment.
• Improve the accuracy and quality of extracted claims information.
• Prioritize high-risk cases so investigators can focus on suspicious submissions.
• Reduce unnecessary manual reviews and accelerate legitimate claim approvals.
• Route uncertain cases to investigators for human review and oversight.
• Support faster, more consistent claims decisions with fraud risk scores and supporting insights.

Explore potential risks and scenarios without exposing sensitive customer or business data. Synthetic data creates artificial data that reflects the characteristics and patterns of real-world data, helping insurers train and test models, simulate emerging risks and evaluate potential outcomes in a privacy-preserving environment.

The value of this solution:

With synthetic data, insurers can:

• Train and test models when real-world data is limited, sensitive or difficult to access.
• Simulate a wider range of potential risks, events and business scenarios.
• Evaluate fraud detection, risk and other analytical models without exposing sensitive data.
• Accelerate experimentation and model development while supporting data privacy and governance.
• Better prepare for emerging risks, market changes and low-frequency, high-impact events.

AI techniques used in this solution:

• Synthetic data

Synthetic data generates artificial data sets that preserve relevant patterns and relationships found in real-world data without directly exposing sensitive records. Insurers can use these data sets to train and test analytical models, explore potential scenarios and supplement limited or imbalanced data while supporting privacy and information security requirements.

How AI helps:

• Train and test models across a broader range of potential scenarios.
• Model rare or emerging risks when sufficient historical data may not exist.
• Improve fraud detection by simulating different fraud patterns and behaviors.
• Assess the potential business impact of significant events and changing market conditions.
• Accelerate model development and experimentation without disrupting in-process business activities.
• Reduce reliance on sensitive customer and policyholder data for analytics and testing.
• Support stronger risk management, scenario analysis and business planning.