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

Empower actuaries and data scientists to quickly and accurately assess losses, expenses and profits in a market that's highly regulated and always changing.

The value of this solution:

With SAS Dynamic Actuarial Modeling, insurers can:

• Reduce costs.
• Increase revenue.
• Achieve greater productivity.

AI techniques used in this solution:

Advanced AI and machine learning models can understand complex relationships, improving how accurately we can predict and segment data. With machine learning, actuarial pricing becomes easier, faster and more accurate. Actuaries and data scientists can handle a lot of data collected for every policyholder. Most importantly, the model's intelligence allows for more detailed pricing segmentation.

How AI helps:

• Strengthen competitive position through integrating, real-time data
• Optimize actuarial resources
• Increase customer retention
• Increased trust with regulators and policyholder by ensuring AI transparency
• Quickly respond to market conditions

Enhance investigational outcomes and streamline processing by leveraging data-driven insights and decisions.

The value of this solution:

With SAS Intelligent Decisioning, insurers can:

• Elevate customer experiences.
• Drive operational efficiency.
• Accelerate decision-making.

AI techniques used in this solution: 

Harness the power of advanced analytics and machine learning to automate manual data analysis and evidence extraction. Using these technologies, adjusters can expedite claim settlement times, make quicker first notice of loss decisions and streamline claims processes. Investigators can gain new, valuable insights and proactively identify patterns with speed and precision.

How AI helps:

• Instant access to comprehensive information empowers insurers to provide exceptional customer experiences.
• Customers are provided a mindful and seamless experience by being spared unnecessary and intrusive investigations.
• Efficiency gains result in significant cost savings and enable staff to focus on impactful tasks.
• Proactive identification of patterns and relationships through network analysis facilitates informed decision-making.
• Real-time understanding of total loss decisions.
• Reduction in false positives further enhance operational effectiveness.

Use AI, machine learning, advanced statistics and anomaly detection to detect and adapt to fraud trends in real-time, validate customers’ digital identities and streamline digital processes.

The value of this solution:

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

• Accelerate innovation.
• Increase fraud detection and prevention.
• Increase customer engagement.

AI techniques used in this solution:

• Machine learning models and advanced analytics enable instant identification and authentication of individuals behind each device, minimizing identity fraud risk.
• Hybrid analytics as-a-service help identify trustworthy customers quickly, minimizing identity fraud risk and reducing false positives.
• Champion and challenger models, along with A/B testing, are used to rapidly deploy the most effective identity proofing strategy.

How AI helps:

• Ensure the integrity of digitalization journeys.
• Enable real-time identity authentication.
• Stay ahead of evolving fraud patterns and novel attack vectors.
• Maximize straight through processing percentages.

Process, organize and extract valuable insights from large volumes of textual data. Encover hidden trends, structured connections, key terms and sentiments through a combination of natural language processing, machine learning, deep learning methods and linguistic rules.

The value of this solution:

With SAS Visual Text Analytics, insurers can:

• Improve customer service.
• Boost policyholder retention.
• Deliver a better customer experience.

AI techniques used in this solution:

• AI, machine learning and advanced statistical methods enable insurance companies to swiftly identify and respond to emerging fraud patterns in real time.
• These technologies streamline digital processes, verify customer digital identities and minimize obstacles.
• Machine learning models and advanced analytics provide instant identification and authentication, reducing identity fraud risk.
• Champion and challenger models, along with A/B testing, rapidly deploy effective identity strategies.
• Real-time identity authentication ensures the integrity of digitalization journeys, increasing straight through processing percentages.
• Natural language processing efficiently processes and extracts insights from large volumes of textual data.
• In large language modeling, machine learning algorithms extract meaning and context from provided texts.

How AI helps:

• Strengthen customer decisions with insights from chat, email and social media streams.
• Identify upsell/cross-sell opportunities.
• Proactively identify poor customer service and complaint situations.
• Optimize resources and improve combined ratio performance.
• Increase customer satisfaction while maintaining data privacy and ensuring AI transparency.
• Be better prepared to quickly respond to requests or problems, reducing customer attrition and cancellation.

Make better decisions with comprehensive analytics. Using a combination of Computer Vision, Machine Learning, and Text Analytics, intelligent document processing meticulously extracts contextual information from scanned document images.

The value of this solution:

With document vision, insurers can:

• Reduce costs.
• Make better decisions faster.
• Improve accuracy and quality of information extraction.
• Accelerate innovation.

AI techniques used in this solution: 

Computer vision and machine learning models automate the extraction of critical information essential to the claims adjustment process.

How AI helps:

• Modernizes claims filing and information systems.
• Significantly reduces human capital required to research and find information.
• Expands the amount of useful information available for insights.

The AI models provide: 

The machine learning models pinpoint and extract information, replacing the need for time intensive, manual review.

Model events to improve actuarial decision making. The absence of data can significantly impact pricing and underwriting decisions, as well as staffing models such as claims. Synthetic data can enhance actuarial processes by enabling your teams to model rare events like earthquakes or by supplementing existing data for geospatial analysis or the analysis of evolving weather patterns.

The value of this solution:

With SAS Viya, insurers have access to "point-and-click" synthetic data generation capabilities, which can:

• Accelerate innovation.
• Provide greater agility.
• Accelerate decision making.

AI techniques used in this solution: 

Generating data that accurately simulates real data while maintaining its statistical properties helps avoid losses from real-world learning scenarios. GANs (Generative Adversarial Networks) and SMOTEs (Synthetic Minority Oversampling Technique) save time, eliminate the need to purchase or rent data, and safeguard privacy. Specifically, SMOTEs train machine learning models to address class imbalance, promoting fairness in pricing and underwriting decisions.

How AI helps:

• AI and GANs enhance data quality, enabling more accurate risk modeling.
• GenAI improves precision in predictions, reducing errors in risk assessment while retaining explainability for pricing and underwriting decisions.
• With GenAI, we can streamline analytics, speeding up data processing and decision making.