The insurance sector is undergoing radical change, driven by increasing customer service expectations and opportunities presented by digital innovation. Digitalisation within claims is now more than ever essential to daily operations. Claims are often the ‘moment of truth’ for the insurance company and the ‘moment that matters’ for the customer. To stay relevant, insurers must provide personalised trusted customer experience at this point. Many insurers are also exploring potential strategies for claims prevention, creating new risk mitigation services enabled by the ever more connected world we live in. Having a strategic claims approach that focuses on prevention rather than compensation and automation of the claims process, will allow insurers to help customers reduce their exposure to risk and the need for claims while managing costs and meeting compliance requirements.

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As a part of the strategic claims approach, Insurers are asking:

  • How digital transformation can have a positive impact on our claims management processes?
  • What digital solutions can be embedded into our current processes? How do we protect against new fraud risks this may create?
  • How can data and analytics help us tackle continued claims inflation?
  • What are the effects on the customer's experience?
  • What does a digital lifecycle look like for our business and how does this impact our workforce?


Insurers provide a wide range of products and solutions to protect individuals, families and businesses from the risks they face. Whether your business is managing high volumes of claims for personal lines, motor vehicle or home insurance or managing complex high-value claims in commercial lines, there is global recognition that AI and digital innovation can bring benefits.

"66% of insurance executives said that the pace of digital transformation in their organisations is accelerating. Over 80% of insurance executives agreed that their organisation’s business and technology strategies are becoming inseparable— even indistinguishable."

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"Increased use of digital and artificial intelligence (AI) in the claims process. For simple claims, we can expect to see more automated and fully digital processes, with claims personnel focused on ensuring superior customer experience and filling in AI blind spots."

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"A bigger focus on claim prevention. Insurers are likely to proactively contact customers with data-driven suggestions on actions they can take to reduce the risk of a loss. For example, insurers could push notifications about severe weather warnings to encourage customers to garage their cars to avoid hail damage or turn up the heat to avoid pipes bursting during a winter storm."

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New products and services are emerging that are demonstrating how claims can be mitigated by using digital services to modify behaviours or inform policyholders of risks they can avoid. It involves detecting or predicting potential claim situations and encouraging or incentivising appropriate action to avoid them. Technologies like IoT are expanding beyond telematics to help insurers with claims prevention and risk mitigation in personal and commercial lines.


Digital solutions are being implemented as point solutions through chatbots, image recognition and mobile apps alongside the core traditional channels within the claims handling processes. To successfully scale and integrate digital innovation into the claims process insurers will need to overcome:

Fragmented communication

Difficult to guarantee immediate and consistent customer communication and support in every channel.

Inconsistent decisions

Optimising the quality of key claims decisions such as total loss in both automated and manual processes

Data & decision silos

Clear consideration given to the sources, types and quality of data across static, streaming, image and unstructured data sets that each product line creates. Avoid the creation of new digital silo’s and aim for consistent insight, decisions and communications across every channel for every product line.

Fraud prevention

Slow to assess claims information to detect fraud as early as possible in the new digital processes.

Decision governance

A clear strategy for decision management that acknowledges how, where and when to implement full automation for FNOL or decision support such as ‘next best’ recommendations to claims handlers.

Lack of customer trust

This is two-sided – one is the usage of additional data/privacy issues and the other is the perception of the insurer as someone who is not willing to pay for a claim.

"Providing an excellent and personalised experience to new digital customers is a must for any insurer to build loyalty and long-term relationships, with 60% of insurers saying attracting and retaining customers are their top priorities.

As consumers continue to evolve their digital needs and preferences, they require new ways of interacting with brands. Promoting Self-Service Options to insurance customers is a good example of how today's tech-savvy customers are not just ready for self-service but prefer self-service over assisted service."

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By taking the points above into consideration, a customer-centric approach to the claims handling process becomes a reality, using data and analytics to drive decisions and deliver a personalised digital claims experience. This will help insurers achieve:

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Shorter Claims Handling Time through full automation or ensuring claims handlers make the best possible decisions

Lower Claims Management and indemnity costs

Higher Customer Satisfaction through consistent decision making bringing competitive differentiation

More efficiency and better claims network collaboration.
The required control of fraud, subrogation and personalised customer communications processes.

By applying AI to automate the claims process and optimise decisions, insurers can create a shorter claims lifecycle. It will result in minimising costs, allowing claims handlers to focus on more complex cases, and responding to customers faster.

Customers are looking for personalised communication from their insurance provider. With AI, insurers could learn from past touchpoints and create a communication strategy that matches their customers’ needs and preferences. This can encompass chatbot services, giving customers access to their provider 24/7 and insurers can enhance the communications to be personalised based on the past experience of the customer. This service would allow customers to ask questions and get answers at the moment they are most needed. The shorter response times will help insurers gain higher customer satisfaction ratings. Using automated personalised communications, claims handlers can focus on cases that are more complex and need extra attention. Automating easier decisions with AI will save insurers time and, in turn, reduce operational expense.

Analytics can support every step of the claims lifecycle starting with claims prevention. An example - third-party data and connected devices can help identify emerging claim risks or provide early warning systems. Another way analytics can help is by setting up automated claims forms. For example, allowing witnesses to upload photos by default would save time for insurers and create more accurate claims – reducing claims fraud and cost leakage.


Creating a fully automated process takes time and planning. Insurers need to first identify areas within their current process that can be automated and then find the right tools for the job.

Fully automate settlement for less-complex claims and provide decision support to experts & engineers for more complex situations. There are analytical tools that can learn from past claims and behaviour. By combining machine learning and human knowledge of claims handlers, Insurers can build the business rules to optimise decisions.

From the data, we gain an understanding of different things: circumstances when the claims happened, ability to better optimise assessment criteria and manage the claim and communication appropriate for the specific customer.

Provide effective claims interactions with real-time support and suggestions for both claims handlers and customers. Combine insight from vast databases, presenting your claims handlers with the recommendations they need to make consistent and timely decisions.

Real-time fraud network analysis and scoring helps us to find hidden connections between different entities – automotive body shops, drivers and passengers, telephone numbers, and home addresses. Those connections can reveal relevant insights and patterns associated with potential fraud.

Intelligent workflows for automated claim handling and agent support

Real-time interaction using chatbots can respond quickly and answer simple claims questions. With SAS, chatbots can be integrated with data from all channels and external data partners. SAS is able to orchestrate the entire claims journey using AI and decisioning, embracing machine learning algorithms, and many other analytical techniques.

Using API’s and microservices, AI tools are meant to enhance, not replace, existing application processes or the claims handler judgement. Unlike core claims administration systems, it’s important that these services support rapid change, with no-code or low-code capabilities, allowing quick responses to change in customer preference or market dynamics. Although the AI has the potential to support full automation, the cohesion with human interaction and decision-making are still necessary when handling complex cases.

With SAS technologies, insurers can integrate analytics & automate complex decisions within the standard claims process. Insurance companies can identify and segment claims for fast-tracking vs. claims that require expert intervention – and doing this early in the process.

SAS has worked with insurance companies from around the globe providing technologies and services to help them achieve their strategic goals around claims. As a result, SAS can deliver insight across the complete customer lifetime journey and manage many of the complex decisions of the claims handling process itself. Insurance companies can rely on SAS to lead them to an automated claims process with our expertise and experience in supporting customers on their claims handling, fraud and customer experience strategy.


SAS has a strong presence in the insurance industry. Insurers successfully use our advanced, cloud-native analytics in areas as diverse as customer marketing and servicing, pricing and underwriting, claims and fraud management, as well as compliance and financial management.

We offer a range of implementation options supported by an extensive catalogue of services to deliver quick wins and rapid return on investment. And we can help you achieve your digital transformation vision while managing risk, ensuring business continuity, and realising a faster time to value. We offer a platform to deliver decision support and decision automation capabilities in every channel. Easy API integration with call centre, web and app services can ensure insight is delivered into every step of the decision-making process, maximising the return from previous digital investments and avoiding the creation of new digital silos.

SAS is one of the very few AI & Analytics providers with the breadth of technology, expertise and experience to deliver the right solution for automating your claims process. As a result, we can help insurers achieve their strategic goals around claims, build a unique customer view, and solve complex issues of the claims handling process.

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SAS has more than 1400 insurance customers in more than 75 countries and 47 of the top 50 largest global insurers rely on SAS. 

With SAS, insurers can...

  • Use advanced analytics with embedded AI to better identify and gain a deeper understanding of customer needs.
  • Find fraud on day 1 at FNOL and through the life cycle of a claim using machine learning and business scenarios.
  • Automate real-time customer experience decisions at scale across all channels.
  • Improve workflow management, support the orchestration of job flows, and take advantage of dynamic disclosures and dashboards to drill down into details.
  • Ensure consistency across all customer journey touchpoints by orchestrating optimised, contextually relevant engagements.
  • Learning from historical experience to identify missed leakage opportunities.
  • Ensure transparency and traceability across the entire process.
  • Anticipate customer needs and craft contextual, real-time digital engagements that customers value.
  • Predict with a 90% confidence level, whether a customer will leave within 24 hours of FNOC.


  • Identifying fraudulent claims

    As part of their claim transformation strategy, Covea is using SAS Viya to speed up and simplify the claims processing experience for their customers. When a new claim is logged, sophisticated AI algorithms work behind the scenes on things such as, assigning liability or determining whether a claim looks suspicious. Covea’s data scientists were able to, within a short time frame, provide claim handlers with an intelligent decisioning framework powered by AI models. Each AI decision is backed by transparency and explainability reports that have helped build trust and adherence by the user community. SAS Viya has enabled Covea to uncover more fraudulent claims but more importantly process claims faster, offering a better customer experience for their policyholders.

    Using SAS Viya(R), Covea created a model to identify new claims and updates within a 24-hour cycle. The data is fed through a model to make predictions about each claim allowing Covea to adjust its communications and strategy in real-time. The model enables them to offer a better customer experience, smarter decisioning, and a faster turnaround. The claims liability and fraud models are tools to assist the claims handler and present an overview to quickly identify which claims can be resolved and which ones need more care.       

  • Motor Vehicle insurance – Total Losses:

    Using the SAS Intelligent Decisioning solution a Large UK Insurer was able to:  

    • Combine multiple internal and external data sources via API to enhance decision making
    • Execute a repair cost estimation model in conjunction with fraud scoring and real-time vehicle valuation data to identify total losses
    • Recommendations provided to the claims handlers to make an offer to settle the claim at FNOL
    • Working in conjunction with their Guidewire Claims Platform through API integration

    The results:

    • 85% of Total Losses now correctly identified on Day 1 at FNOL
    • Average settlement time for total loss reduced from 28 to 12 days
    • Storage savings between 3 to 11 days per claim
    • Excellent Customer (and Claims Handler) feedback due to quick claim resolution
    • Savings of £10 M+ per year
  • Medical Treatment Management

    SAS helps a Large Australian Insurer to optimise its services by:  

    • Segmenting claims to enable effective and efficient allocation of cases to the "right" service segments throughout the claims process, regardless of the simplicity or complexity of the claim.
    • Supporting the needs of customers throughout their journey to recovery and return to work to maximise injured customers' chances of returning to employment as fully as possible.

    The results:

    Using SAS predictive analytics, the insurer was able to:

    • Determine the most appropriate treatment path for claimants.
    • Determine which service providers may give them the most effective products for their particular needs.


    Contact Emma Chester, the SAS engagement lead for Insurance.

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