With advances in AI—especially intelligent agents and the automation patterns emerging from agentic design—insurers worldwide are recasting their most critical operations and offerings. From marketing and customer engagement through underwriting and claims processing, the industry is rapidly evolving, with AI as a central driver.

At Microsoft, we identify organizations that embed AI agents deeply across their operations as Frontier Firms. These are innovation leaders who are blending human judgement with AI agents and who, according to a November 2024 IDC study commissioned by Microsoft, report returns roughly three times higher than slow adopters.¹

Insurers and other financial services companies make up the highest concentration of Frontier Firms, which is not surprising given the competitive nature of the sector and the outsized impact of agentic AI.

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How AI is transforming the end-to-end insurance value chain 

Insurers can potentially realize transformative benefits with AI without needing to replace their core platforms, but rather by augmenting and accelerating them through targeted, extensible, AI-powered capabilities. Through advances such as intelligent agents and the automation patterns emerging from agentic design, insurers are consolidating fragmented workflows into connected, intelligent, adaptive systems.

Consider the impact on claims processing. In 2024, more than 30 million personal auto claims were reported in the US alone.² Each one typically required adjusters one to three days just to gather, read, and interpret documents. The slow, manual nature of traditional claims processing is one of the most labor intensive and high impact functions in insurance. It is also where agentic AI delivers some of the fastest return on investment (ROI). For example, AI can help automate document understanding and summarization for faster and more accurate processing. In policy and coverage validation, it can help reduce back-and-forth queries between adjusters and underwriters and speed the approval of well-qualified claims. In contextual triage and routing, it can help improve the productivity of employees across claims processing by enhancing early fraud detection and reducing delays caused by manual sorting or misrouting. With millions of claims processed annually and cycle times measured in days or weeks, even modest improvements can potentially create significant financial and customer experience gains.

Agentic AI is reshaping much more than claims. Across the value chain, a unified agentic ecosystem can deliver measurable outcomes.

In underwriting, agents can automate information gathering processing to help sales agents submit more complete requests for quotes to underwriters. Agents can help interpret submissions, orchestrate scenarios and catastrophe modeling, and assist in generating proposals aligned to client mandates.

In marketing and distribution, agents can redefine the customer experience by increasing personalization at scale with speed and boosting sales opportunities. Agents can flag top renewals and generate personalized outreach, help prioritize leads, optimize campaigns and prepare tailored client briefs and pitch materials in seconds.

In customer onboarding and service, service become more anticipatory and less reactive. Agents can help validate information across documents automatically and detect missing forms or inconsistencies early. Virtual assistants can answer inquiries around-the-clock with contextual accuracy and trigger proactive outreach if a customer shows signs of churn or claim frustration.

In risk and compliance, teams move from firefighting to orchestrating safe, scalable operations. Under the direction of qualified processionals, agents can help monitor exposures continuously across economic, climate, and portfolio data, read regulatory updates and support assessment workflows, and help detect fraud by surfacing potential issues to the appropriate teams and workflows.

How agentic AI is benefiting insurers worldwide

Already, we’re seeing the impact of agentic AI building on the benefits of generative AI to deliver transformative new benefits for insurers.

For example, Generali France is transforming insurance operations with intelligent agents that empower front‑line workers and experts across the business to achieve a people-centric vision for product and service delivery. The firm has built more than 50 agents with Microsoft Copilot Studio and Azure OpenAI to address a broad range of specialized used cases. These agents do more than generate content, they act across complex information flows, from extracting information from unstructured data and running hyper-personalized marketing campaigns, to assisting with content creation and standardizing responses to requests for proposals (RFPs). These powerful solutions allow experts to focus on judgment and customer care, measurably helping Generali achieve top‑ranked customer satisfaction.

Elsewhere, a major global insurer strengthened its crisis response in near real-time by using AI to rapidly compare property locations with public wildfire evacuation data. Instead of hours of manual analysis, teams quickly generated clear, actionable risk insights, improving situational awareness and enabling faster, more confident communication with stakeholders.

Another insurance and financial services company took a proactive approach to risk mitigation, using AI to scan records for a brittle material linked to structural failures in older buildings, helping to identify and assess risks before losses could occur.

These real-world scenarios are only the tip of the iceberg, giving an early view of the broader transformation that is quickly redefining the competitive landscape. In upcoming blogs, we will share deeper examples and customer‑aligned scenarios across the end-to-end insurance value chain.

The journey to becoming a frontier insurer starts now

To unlock the value of agentic AI, Microsoft offers an end‑to‑end cloud and AI platform that insurers can incorporate powerful agents into their technology ecosystems. Microsoft Foundry provides the developer platform for building, testing, deploying, and orchestrating AI agents and applications, and Microsoft Agent 365 offers a control plane to help govern, secure, monitor, and manage agents across an enterprise, regardless of where they were built. This means that insurers can design, customize, deploy, and integrate intelligent agents across the value chain, with enterprise‑grade governance and a comprehensive suite of AI models and services.

Microsoft further strengthens this foundation with industry‑specific data models, process frameworks, and prebuilt connectors that simplify integration with core insurance systems, analytics environments, and workflow applications. This helps ensure faster time‑to‑value and accelerates modernization of claims, underwriting, servicing, and risk operations.

And critically, insurers also benefit from a deep, global partner ecosystem of trusted technology and solution providers who are well versed in delivering mission-critical solutions on the Microsoft Cloud, combined with our deep, long‑standing expertise in the insurance sector. Together, this ecosystem empowers insurers to innovate confidently, scale securely, and realize measurable impact with agentic AI.

The journey to agentic AI involves identifying high-impact workflows early, creating a unified data platform, addressing governance from the start, and empowering teams with smart change management. By embracing a frontier firm mindset—human led, agent operated—insurance leaders can unlock new value and innovate in the new competitive landscape. To continue your AI journey, contact your Microsoft representative or technology partner.

Next steps on your journey to agentic AI

• To explore solutions and resources for insurers, visit Microsoft for Insurance.
• To learn how frontier firms in financial services are using AI to improve efficiency, innovation, and customer satisfaction, get the e-book.

¹ IDC InfoBrief: sponsored by Microsoft, 2024 Business Opportunity of AI, IDC# US52699124, November 2024.

² Verisk, ClaimSearch Trends Report, 2024 Year-end Analysis

The post From bottlenecks to breakthroughs: How agentic AI is reshaping insurance appeared first on The Microsoft Cloud Blog.

The insurance industry stands at a pivotal moment in its digital transformation journey. With deep data reserves, a tradition of analytic decision-making, and a workforce skilled in actuarial and underwriting practices, insurers are uniquely positioned to benefit from the ongoing advances in AI.

However, despite early enthusiasm and pilot projects, only 7% of insurers have successfully scaled AI initiatives across their organizations.¹ The adoption of increasingly powerful AI agents—systems that can support autonomous tasks, help make decisions, and take action under human oversight—offers a promising path forward. By embedding intelligent agents into workflows, insurers can tackle legacy constraints, talent shortages, and operational inefficiencies to unlock transformative value.

Challenges in adopting agentic AI

The broad adoption of agentic AI in insurance is hindered by several entrenched challenges.

First, a persistent talent shortage affects specialized roles such as actuarial analysis and underwriting, which limits the industry’s ability to leverage data effectively. Adding to the challenge is legacy infrastructure, including outdated systems and fragmented data architectures, which can impede integration and scalability.

Financial strain across the insurance sector is another major factor, with catastrophe losses exceeding $100 billion annually for six consecutive years, making high-frequency property losses a structural issue.²

Organizational resistance also plays a significant role; siloed teams, unclear priorities, and a conservative corporate culture slow the pace of AI adoption.

Opportunities with agentic AI

Despite these hurdles, agentic AI presents transformative opportunities. Workforce augmentation is one of the most promising areas. For instance, Sedgwick’s Sidekick Agent, developed in collaboration with Microsoft, enhances claims processing efficiency by more than 30% through real-time guidance and decision support.³

AI also enables personalized customer experiences at scale, using embedded systems to tailor communications and support. Operational efficiency can be improved significantly in some implementations, with end-to-end redesigns yielding 30–40% gains in net efficiency.¹

Furthermore, agentic AI, under human guidance, can enhance quality assurance by improving consistency and helping insurers adhere to compliance processes, which is especially important as seasoned professionals retire and institutional knowledge can be lost.

With agentic AI, chatbots can also be improved to more effectively enhance customer experience. Instead of answering a question and handing a customer off to a queue, an agentic solution can help orchestrate a more end-to-end experience. Potentially, this can include everything from capturing first notice of loss, to requesting missing documentation, updating policy and billing systems, scheduling appraisals, and proactively notifying customers of next steps (all subject to insurer workflows and regulatory review, of course).

This “resolve, not route” approach is already showing measurable impact in claims operations: For example, according to McKinsey, one major insurer rolled out more than 80 AI models in its claims domain, cutting complex-case liability assessment time by 23 days, improving routing accuracy by 30%, and reducing customer complaints by 65%.⁴

For carriers, such outcomes translate into faster cycle times, higher customer satisfaction, and better loss-adjustment expense control—all while preserving necessary human oversight where judgment, empathy, and regulatory accountability are required.

Strategies for success with agentic AI

To successfully adopt agentic AI, insurers must align technology initiatives with customer needs and business goals.

Establishing an AI Center of Excellence (CoE) is a foundational step. A CoE provides governance, strategic direction, and technical expertise, helping organizations avoid fragmented AI adoption and scale responsibly.

Iterative testing is also essential, beginning with high-volume, repeatable tasks that help insurers refine models through feedback loops and production pilots.

Targeting scarce talent areas, such as fraud detection and underwriting, can maximize impact by augmenting roles that are difficult to fill.

Industry accelerators like Cognizant’s Agent Foundry offer prebuilt tools and frameworks that can help reduce implementation time and support compliance efforts. This platform-agnostic solution supports the full lifecycle of agent deployment, from discovery to scale, and integrates seamlessly with existing enterprise systems.

Finally, cultural transformation is critical. Since 70% of scaling challenges are organizational, insurers must foster a culture of change, accountability, and long-term thinking to fully realize AI’s potential.¹

Move to agentic AI with confidence

Agentic AI is not just a technological upgrade, it is a strategic imperative for insurers seeking to thrive in a rapidly evolving landscape. By addressing structural challenges and embracing AI as a catalyst for transformation, insurers can unlock new levels of efficiency, personalization, and resilience.

The path forward requires bold leadership, cross-functional collaboration, and a commitment to continuous learning. Those who invest in scalable frameworks, such as AI Centers of Excellence and industry accelerators, will be best positioned to lead the next era of insurance innovation.

Explore solutions for agentic AI in insurance

• Explore insurance solutions and resources on Microsoft for Insurance.
• To learn about how Cognizant and Microsoft are partnering to help insurance companies advance their cloud modernization journeys, see the Cognizant and Microsoft Partner Page.

¹ Insurance leads AI adoption. It’s time to scale

² 2025 marks sixth year insured natural catastrophe losses exceed USD 100 billion, finds Swiss Re Institute

³ Sedgwick optimizes claim workflows with AI application Sidekick and Microsoft integration

⁴ The future of AI in the insurance industry

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Yet as organizations deepen their partnerships with major cloud and AI providers, regulators and executives alike are sharpening their focus on concentration risk, the concern that reliance on a relatively small number of technology providers might create critical business vulnerabilities. 

Rather than viewing cloud dependency as a threat, forward-looking leaders regard it as an important facet of modernization. The challenge is not to avoid concentration; it is to manage it intelligently, helping a firm maintain control, enhance resilience, and remain flexible amid changing conditions.  

For financial services firms in many jurisdictions, exit planning—a structured process to safely disengage from critical providers—has moved from a theoretical consideration to a regulatory expectation and an important component of operational resilience. 

Managing risk and exit planning in an evolving landscape 

Concentration risk has long been framed as systemic exposure (“What if a key provider fails?”), prompting regulators to mandate exit plans that assume full termination. In theory, this seems straightforward; in practice, it rarely is. 

Modern financial institutions operate in a deeply interconnected ecosystem where critical third-party providers are embedded in core operations and strategic innovation. These partnerships go beyond simple outsourcing; they often underpin transformation initiatives and are key to resilience when managed well by the organization. As a result, in highly integrated environments, full disengagement may be operationally complex and unlikely in practice, but firms are still required to maintain feasible, risk based exit plans. 

In this regard, Microsoft has introduced important capabilities (such as standardized architectures, diversified cloud regions, and built-in failover options) that customers can incorporate into their resilience and exit planning strategies. They can effectively reduce dependency risk for critical services and ensure continuity, but they stop short of enabling a full provider exit. Regulators increasingly acknowledge that perfect exits are not always technically or economically feasible. What they require are proportionate, well tested plans that reflect operational reality. The priorities are transparency, control over critical workloads, and pragmatic dependency management.  

Against this backdrop, regulators are recalibrating expectations, focusing on actionable, tested strategies rather than theoretical full exits. Two major frameworks illustrate this shift: 

• The European Union Digital Operational Resilience Act (DORA): Requires institutions to maintain tested transition plans that enable the removal or migration of contracted information and communication technology (ICT) services and data.
• The United Kingdom Prudential Regulatory Authority (PRA) SS 2/21 and the Critical Third Party (CTP) oversight regime: Requires firms to maintain documented and tested exit strategies for any “material” (such as critical and high-impact) outsourcing arrangement, with clear definition of roles, responsibilities, and continuity plans. 

Both frameworks emphasize proportionality, focusing on critical or important business functions, and integration into broader business continuity and resilience of governance.  

Integrating exit planning within a broader resilience strategy 

Exit planning is no longer optional, it is a compliance essential. Fortunately, given the complexity of today’s hybrid and multi-cloud environments, regulators do not expect “perfect” exit plans. Instead, they encourage risk-based, practical, and tested practices that dovetail with broader efforts.  

Exit planning should be embedded within a comprehensive, structured approach to strengthen operational resilience. To support such an integrated approach, Microsoft has developed a six-step resilience framework that aligns closely with the requirements of DORA: 

1. Update cloud risk governance: Systematically review policies and controls to ensure that cloud adoption aligns with business priorities, regulatory requirements, and risk tolerance.
2. Identify concentration: Specify critical third-party and indirect nth-party dependencies, such as a vendor’s suppliers, subcontractors, or technology partners. 
3. Assess alternatives: Evaluate potential providers and exit strategies—comparing cost, resilience, and compliance to ensure continuity and mitigate concentration risk before making final decisions. 
4. Design for resilience: Plan systems and recovery processes that can withstand disruptions from hardware failures and service outages, recover quickly, and maintain critical operations. 
5. Test business continuity plan: Prepare for loss of a data center or region, or long-term failures, with regular testing that identifies gaps and validates recovery procedures. 
6. Prepare exit plans: Develop and test detailed exit strategies—including timelines, resource allocation, and contingency measures—to ensure seamless provider transition and maintain compliance under stress scenarios. 

This integrated approach ensures that exit plans remain both practical and sustainable, and do not exist in isolation. Ultimately, exit planning is part of a larger system of controls and safeguards, evolving alongside the business’s cloud and AI innovation cycles. 

Enhancing exit planning with guidance and tools from Microsoft 

Recognizing the criticality of continuity, reversibility, and secure data transfer in financial services organizations, Microsoft has developed a comprehensive framework of contractual commitments, technical solutions, and support services to empower firms to manage exit scenarios with confidence and control. 

For example, if a regulator intervenes in a company’s operations, Microsoft is committed to granting the regulator full administrative control over the institution’s cloud environment. In cases of reorganization or acquisition, Microsoft enables the assignment or transfer of service rights to successor entities, ensuring that critical services remain uninterrupted. Importantly, Microsoft will not suspend or terminate services solely due to a transfer of rights, provided contractual obligations are met, and offers flexible service extensions to facilitate smooth transitions and data retrieval. 

Beyond contractual measures, Microsoft equips customers with a suite of advanced technical tools to support seamless data migration and workload portability. These include:  

• Azure Arc, a bridge that enables hybrid and multi-cloud management, letting firms extend Microsoft Azure services to on-premises or other clouds for flexible migration and reduced concentration risk.
• Containerization and portability: Using containers (such as Azure Kubernetes Service and Docker) and microservices makes applications portable—simplifying workload transfers between Azure and other environments.
• Automated data migration: Built-in tools like Microsoft Azure Data Factory automate extract transform-load (ETL) processes, streamlining bulk data migration during exit events.
• Microsoft 365 data management, provided with Microsoft Purview and other solutions, to provide key capabilities, including:
  • eDiscovery tools that can export emails, documents, and collaboration data in standard formats for easy transfer.
  • Backup solutions to create point-in-time snapshots, supporting reversibility and continuity.
• Hybrid, private, and sovereign cloud options for Microsoft Exchange, SharePoint, OneDrive, and Skype for Business enable migration across platforms. 

By combining clear contractual safeguards, advanced migration tools, and ongoing investment in hybrid cloud and open APIs, Microsoft empowers financial institutions to plan and execute exit strategies that align with regulatory mandates and business objectives. Exit planning then becomes a proactive process, one that safeguards business continuity and regulatory compliance at every stage of the cloud journey. 

Learn more 

• For an in-depth analysis, download our whitepaper, “Addressing concentration risk and building practical exit strategies for Microsoft Cloud.”
• Read more about our approach for managing operational risk in financial services on Microsoft Learn.
• Visit our blog for stories of how Microsoft for Financial Services helps firms accelerate business value.

The post Managing concentration risk and exit requirements: A framework for financial institutions appeared first on The Microsoft Cloud Blog.

Subtle and complex in nature, social inflation impacts profitability by driving up claims payments. Outpacing economic inflation by 1.7% in the United States from 2017 to 2022,¹ social inflation drove a 57% surge in liability claims over the past 10 years,² and led to a USD20 billion increase in commercial auto liability payouts from 2010 to 2019.³ 

In response, insurers are increasingly turning to technology, particularly AI, to help predict trends, enhance underwriting processes, and automate workflows. And now, with the potential application of a cloud-based solution that lets companies explore insights collaboratively, insurers can have more options.  

Helping insurers forge effective long-term technology strategies is core to our vision for intelligent insurance and our work with Microsoft Cloud for Financial Services. In the case of social inflation, cloud and AI can help insurers mitigate the incidence and impact of unpredictable outcomes. 

How cloud and AI can help solve the social inflation challenge 

To improve competitiveness amid a volatile landscape, most insurers have invested in cloud modernization over the past decade. This provides an essential foundation for many critical benefits. For example, solutions built on Microsoft Power BI can track and analyze key performance indicators (KPIs), perform predictive analytics, and generate real-time insights.  

Generative AI is now expanding upon these core benefits to deliver dramatic improvements in productivity, operations, and enhanced workflows. For many firms, the first step in realizing value from generative AI is to adapt Microsoft 365 Copilot, which is integrated seamlessly in Microsoft productivity applications. Drawing on the full scope of Microsoft 365 data within the firm (such as emails, Word and Excel documents, Teams communications, and more) Microsoft 365 Copilot can, for instance, help a claims analyst generate a report that draws on the best available information from research reports, emails, calls, and external data sources.  

Many firms are also building custom agents to address specific use cases on challenges such as streamlining processes, enhancing claims processing workflows, and improving overall productivity. Additional value is also being unlocked as firms transform their unstructured data into structured formats, which enables even more robust predictive models and other advanced data analytics.  

To help address social inflation, generative AI and agent functionality can be used to gain important new insights, such as the following:  

• News monitoring: AI-powered tools can continuously scan and analyze news articles from myriad sources. Thanks to natural language processing (NLP), these tools can identify relevant news items, summarize key points, and highlight trends or significant events. This helps businesses stay updated on industry developments, competitor activities, and market shifts.
• Market insights: AI can process vast amounts of market data, such as economic indicators, consumer behavior patterns, and stock prices. With the power of machine learning algorithms, AI can detect patterns, predict market trends, and offer actionable insights. An AI agent can then present these insights seamlessly in daily workflows, helping decision-makers to make informed choices. 
• Marketing campaigns: AI can track the performance of marketing campaigns by analyzing data from multiple channels such as social media, email, and web analytics. It can measure engagement, conversion rates, and return on investment (ROI), providing real-time feedback on campaign effectiveness. AI agents can also assist in generating reports and suggesting optimizations based on the data. 

Looking forward, these capabilities will be further enhanced with agentic AI—autonomous systems that can plan, adapt, and act to achieve goals, requiring minimal human input as they interact with other tools and environments. For insurers, agentic AI can help in ways such as the following: 

• Automating data collection: Agents can autonomously gather data from multiple sources, helping ensure that information is always up to date.
• Providing real-time alerts: Agents can monitor and respond to specific events or changes in data and send real-time alerts to stakeholders to help ensure prompt responses.
• Generating insights: By continuously analyzing data, agents can identify patterns, correlations, and trends, helping businesses to stay ahead of the curve. 

Confidential computing: Opening new opportunities while protecting sensitive data 

To gain new benefits from more sensitive data, such as claims or confidential information, an additional layer of security and confidence is now offered by Azure confidential computing.

Azure confidential computing creates a protected environment called Azure Confidential Clean Rooms that lets different teams within a company or across multiple companies perform joint data analysis and develop risk models, fraud detection models, and more, using advanced encryption techniques to anonymize data.

Across the financial services industry, confidential computing is increasingly being enlisted to help unlock new opportunities. For example, financial messaging provider Swift is using it in an innovative anomaly detection model, enabling the model to be trained on distributed datasets without copying or moving data from secure locations. Beyond regulated industries like financial services and healthcare, confidential computing is also being used for solutions in retail, manufacturing, and energy sectors. 

In addressing social inflation, confidential computing can help insurers understand the hidden drivers that contribute to rising costs, distort risk assessments, and influence claim outcomes. This involves identifying patterns and connections between contributing factors, such as litigation trends or the influence of social media and viral campaigns that can amplify public sentiment.  

With the power of AI combined with confidential computing, actuaries, underwriters, and claims professionals can use natural language prompts to ask questions of data, semantic search can recognize their meaning and intent, and the system can write coherent responses and deliver appropriate resources. This offers an entirely new and transformative way where, by analyzing interconnected factors, insurers can identify high-risk cases and insights that suggest propensity towards social inflation claims, enabling insurance to develop proactive strategies such as early settlements or policy adjustments in high-risk areas. 

Of course, bringing teams or organizations together to consider the adoption of a broad solution approach like confidential computing involves more than just technology. It also requires structure, guidance, cooperation, and leadership. Microsoft is here to help for the long run and enable such collaboration starting with the industry leading Microsoft Azure cloud platform, and our ongoing commitment to security and responsible AI, and our long-term leadership in insurance and financial services.  

We are excited to partner with insurers and the industry at large to help innovate new solutions and business opportunities through cloud and AI. To get started with your business, reach out to your Microsoft representative and we’ll be happy to explore the possibilities. 

Learn more 

• To learn more about our solutions for insurers, visit our Microsoft Cloud for Financial Services website.
• For a deeper look at confidential computing, read the Azure Confidential Computing Blog.  

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¹ Swiss Re Institute, Social inflation: litigation costs drive claims inflation, September 2024.

² Risk and Insurance, Social Inflation Drives 57% Surge in US Liability Claims Over a Decade, September 2024.

³ Boston Consulting Group, P&C Insurance topics for 2024.

The post Social inflation is costing insurers—here’s how cloud and AI can help appeared first on The Microsoft Cloud Blog.

Consider, for example, that Americans who live past the age of 70 can expect on average to spend $172,000 for long-term care over their lifetimes.¹ But most families (as many as 83%²) say it would be impossible or very difficult to afford $60,000 for annual in-home or assisted living care expenses.

Life and health insurance companies are working to help bridge this ever-widening gap with long-term care insurance (LTCI). And thanks to a broad range of new innovations enabled by AI, Microsoft and our partners are helping to improve LTCI for policyholders and insurers alike. This is part of our vision for intelligent insurance and our work with Microsoft Cloud for Financial Services. 

In this case, helping to make long-term care more accessible and affordable is good not just for enhancing societal well-being and reducing the strain on public resources, but also for the viability of a critical insurance product.

The unique challenges of long-term care insurance

LTCI insures people for a very costly circumstance that is very likely to occur.

About 70% of seniors will require long-term care at some point in their lives,³ and Americans spend more than $471 billion annually for long-term care.⁴ Yet Medicaid covers only 42% of long-term costs. LTCI aims to address a significant portion of long-term care costs by covering a range of services for people who need assistance with daily living activities—for example, bathing, dressing, and eating—over an extended period, often provided at home.

Despite the prevalence and urgency of the need, however, LTCI has proven challenging for insurers. Rising healthcare costs and higher-than-expected claims have created unforeseen financial pressures. People are living longer, increasing the likelihood of needing to use LTCI, and fewer customers than expected are letting their policies lapse. For insurers, this results in profitability challenges and uncertainty, while customers face higher premiums, and thus reduced access.

How AI can help improve LTCI profitability and growth

The success of insurance companies in addressing the growing need for LTCI will depend on a variety of factors, including education, awareness, and the efficiency and effectiveness of regulation. Above all, technology holds the key for transformation in profitability and growth.

We are seeing tangible results in AI innovation with our insurance customers worldwide that have direct relevance for LTCI providers. These potential benefits are prompting many insurers to accelerate their cloud migration and data management investments—a transition that is key to the LTCI sector, which lags other insurance segments. With the scale, security, and resilience of the Microsoft Cloud combined with the advanced data and analytics capabilities of Microsoft Fabric and the AI development opportunities enabled by Azure AI Studio, insurance companies can innovate rapidly and confidently to meet their specific needs.

The future of insurance in the era of AI

Read the e-book u0026gt;

Here are some of the important benefits that insurers can apply to their LTCI offerings.

Enhance underwriting and claims management

AI can streamline underwriting and claims processing in ways that improve both accuracy and efficiency. One important area of focus is straight-through processing (STP)—the automation of an entire workflow, from the initial data entry to the final decision, without the need for human intervention. STP helps to reduce delays, minimize errors, and free up valuable human resources.

In the underwriting process, AI helps enable STP for tasks such as analyzing historical data, assessing risk factors, and predicting the likelihood of claims, which helps underwriters make more informed decisions and reduces the time required for manual reviews. It can also handle a larger volume of applications without a corresponding increase in resources.

In claims processing, STP can automate the assessment and triage of claims—for example, by quickly extracting and analyzing information from a wide range of documents, including medical records, policy applications, and claims forms. Many insurers have long used optical character recognition (OCR) technology to digitize these types of documents. But the addition of generative AI supercharges how they can be understood, evaluated, and acted upon.

Automate contact center experiences

With generative AI’s natural language processing and content creation capabilities, insurers can optimize contact center operations in ways that help both the customer and the company.

AI-enabled copilots and virtual assistants can handle larger volumes of routine inquiries, helping agents and customer service representatives provide faster, more accurate responses to policyholders’ questions about coverage, claims status, and more. For example, John Hancock implemented a new AI solution to provide support for common customer issues and questions, which helps call center representatives focus their efforts and expertise on the most complex cases, with better customer experiences and reduced wait times.

Automated systems can understand and respond to customer inquiries in a conversational way, and even authenticate caller identities with voice biometrics, streamlining the identification process and enhancing security. For LTCI, AI can enable corresponding benefits through more efficient operations, better resource allocation, and enhanced customer experiences.

Prevent fraud, waste, and abuse

In the realm of fraud detection, advanced analytics and AI-powered tools can analyze vast amounts of data from healthcare vendor invoices to identify patterns and anomalies indicative of fraudulent activities in a timely manner. With better insights, insurers can proactively detect and prevent fraud, helping ensure that legitimate claims are processed swiftly while minimizing financial losses.

AI can also help insurers identify unusual patterns or anomalies that could indicate fraudulent or wasteful activities, such as flagging a particular facility if it consistently submits higher-than-average claims for certain treatments. Analyzing historical data can also help inform insurers to create more robust, data-driven processes to determine which facilities to audit or to benchmark best-in-class operators.

Expedite regulatory, contracting, and auditing activities

LTCI is inundated with regulatory, contracting, and auditing activities, many of which rely on cumbersome manual processes. AI can improve the efficiency of many of these workflows while also improving accuracy, turn-around times, and regulatory compliance. Data validation, risk assessment, and regulatory monitoring can all benefit. Moreover, AI’s predictive analytics can spot potential compliance issues, and its enhanced reporting capabilities can aid strategic decision-making.

Advancing LTCI with AI and Microsoft

We believe that with focused, creative innovation with AI, LTCI providers and their customers can look forward to a bright future in which more people can live with dignity and financial security in their senior years, thanks to high-quality, robust insurance products and services. We are excited to work with industry and our global partner ecosystem to strengthen LTCI, in line with Microsoft’s responsible AI principles and our Secure Future Initiative. 

To learn more about all our solutions, visit our Microsoft Cloud for Financial Services website.

¹AARP, “Long-Term Care Costs May Double to $5.6 Trillion by 2047,” March 2018.

²KFF, “The Affordability of Long-Term Care and Support Services: Findings from a KFF Survey,” November 2023.

³Morningstar, “100 Must-Know Statistics About Long-Term Care: 2023 Edition,” March 2023.

⁴A Place for Mom, “Long-Term Care Statistics,” September 2023.

The post How AI is improving long-term care insurance for insurers and customers alike appeared first on The Microsoft Cloud Blog.

When it comes to AI, insurers no longer need much convincing about the potential value of the technology to their business. What they need is help, partnership, and a path to adoption that recognizes the unique demands of the insurance industry.  

Some of the world’s largest insurance companies are busy innovating on early use cases to help them evaluate the potential impact of generative AI on their operations and businesses. Most other insurers are not far behind.                         

With the recent availability of Microsoft Copilot for Microsoft 365, which integrates the magic of the technology into everyday applications like Microsoft Teams and Excel, insurers understand that their employees and customers alike will welcome generative AI into their operations (if not demand it). Most insurers want to innovate quickly but carefully, deriving maximum value from even the earliest steps while incurring minimal risk to the business. 

Helping insurers unlock business value and deepen customer relationships through technology is what Microsoft Cloud for Financial Services is all about. In our work with early AI adopters, we have identified a set of progressive milestones that can help insurance companies explore generative AI so that its value can be assessed and scaled as quickly and productively as possible.  

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4 stages of AI maturity in insurance 

Today, most of the insurance companies we work with are evaluating projects at what we call the “exploring” phase—the early horizon where the technology is deployed on a limited basis. While these use cases tend to focus on internal business scenarios, they are executed with an eye to the far horizon of opportunity, beyond improving operations to being central to new product development and reimagined processes.  

To help insurance manage this long-term approach, we recommend following a four-level maturity model that describes the AI adoption journey from early consideration to innovation at scale. 

1. Exploring: Conducting research and developing plans and demos to learn what AI can and cannot do.  
2. Experimenting: Building a set of limited use cases to determine value and inform next-step planning.  
3. Scaling: Generating an innovation flywheel of more advanced use cases impacting the business at multiple levels. The most innovative insurers are generally here. 
4. Innovating: Integrating AI into core business processes and new offerings, with governance and training to create a new business-as-usual.  

From exploring to experimenting 

In a remarkable way, generative AI can feel almost too appealing. Brainstorming ideas for use cases can produce lists that are quite lengthy. So, it is important to prioritize.  

The north star in this phase is speed to value, which can be achieved by building use cases that are relatively simple to design and easy to deploy. Use design thinking techniques to ideate use cases and map them in a two-by-two “value versus implementation” matrix to find the ones that will deliver higher business value.  

Early use case scenarios are often designed to help employees do their jobs more efficiently. For example, in underwriting, it can take the form of an internal chatbot to answer agent questions or help triage submissions. Claims managers can realize immediate benefits using generative AI to transcribe first notice of loss conversations. In marketing, it can speed the process of developing presentations or drafting new content. 

We work together with insurers by first conducting envisioning workshops, choosing the most strategic options, then building and deploying rapid prototypes. This is where leveraging your technology partner or service provider can reap great benefits.  

 Keys to success in this phase: 

• Begin with three to five use cases.
• Focus on inward-facing scenarios with defined business value.
• Define timelines and success metrics that can be validated.

From experimenting to scaling generative AI 

With the learnings from early efforts in hand, insurers can gain the confidence to move up to more substantive use cases. Business value is the key criterion, and so every candidate should be evaluated on scalability (for example, if it won’t scale, don’t do it). Use cases can also include more than just text-based, with visuals or audio incorporated for richer experiences. Already, we are seeing insurers building on early success to generate an innovation flywheel that generates speed, scale, and learning through experience.  

To move to this next level, the IT landscape needs to be made AI-ready. The most important step is to prepare your data estate by migrating to a modern platform such as Microsoft Fabric, which unifies data and analytics, and has generative AI built in. This positions the company to build custom copilots, chatbots, and other AI enhancements using Microsoft Azure OpenAI Service and other cutting-edge solutions. It also ensures that critical concerns such as privacy, security, and compliance are fully addressed.  

This is also the point to consider the organizational implications of AI, not only identifying how roles will be impacted but also ensuring that frameworks and training are in place to ensure responsible AI over the long term.  

Keys to success in this phase: 

• Don’t stay small—apply learnings to larger efforts tailored to the business’s unique needs.
• Get your data estate ready for AI.
• Set up steering committees to ensure responsible AI and quality assurance frameworks. 

From scaling to long-term innovation  

Early adopters of AI in insurance are already building solutions designed to directly impact their operational efficiencies and, increasingly, the products and services they deliver. It won’t be long before deeper innovation with AI will create significant differentiation among competitors, and that has implications for every organization.  

To enable the greatest competitive advantages with AI, insurers will require a comprehensive cloud foundation that identifies and manages data from many sources, and ensures that AI tools integrate smoothly with existing systems. This is key to enabling AI development to scale as quickly as business requirements demand. You want to be sure that cloud and AI are being provided responsibly, and that it meets the industry’s stringent requirements for data privacy and protection.  

Organizationally, business processes will change to ensure safety and responsibility. We advocate creating committees or offices to define company values for using AI, ensuring guardrails in operations, and managing pipelines of use cases and measurement across the company.  

Finally, you want to ensure that your people are ready. Roles will change over time, and the workforce will accommodate this evolution best with training to build on their skill sets. A great step you can take today is to put AI in their hands now with Microsoft Copilot for Microsoft 365, which integrates powerful capabilities into the productivity tools they use every day.  

Keys to success in this phase: 

• Build a comprehensive AI technology foundation.
• Evolve business processes and deploy AI governance frameworks.
• Engage with and upskill employees to foster adoption and unlock creativity.

Continue on your generative AI journey 

As your organization considers how best to embrace generative AI, we invite you to reach out to your Microsoft representative or technology partner for insights and ideas to move forward with confidence.  

You can learn more about how Microsoft Cloud for Financial Services is helping our customers realize the future of insurance in the era of AI, unlocking business value, and deepening customer relationships.  

The post From exploration to innovation: 4 key stages of AI adoption for insurers appeared first on The Microsoft Cloud Blog.

Transacting through our marketplace connects you to thousands of pre-vetted Microsoft partner solutions, enabling you to rapidly accelerate your AI transformation and drive business outcomes. Whether that’s helping to enrich employee experiences, reinvent customer engagement or reshape business processes.  

It also represents smart spend. To move at the speed of business today, many companies prefer buying to building cloud apps, handing off the associated costs and management to SaaS partners while provisioning end-to-end solutions quickly and reliably. 

In this series of four blogs, we’ll dive into some exciting new AI-powered software solutions and how they can benefit your business. Here’s a taste of what follows.

Decision intelligence for service operations 

Imagine if every decision that service operations teams make were consistently more accurate, timely and planned for. That’s countless micro-gains every minute of the day – at your fingertips. Delivering “decision intelligence” for service operations in banks, insurance companies and healthcare providers,

ActiveOps’ blend of AI and human intelligence delivers the most complete and useful set of predictive and prescriptive insight to help make better decisions at the right time – resulting in over 20% more capacity, over 30% boost in productivity, and significant business impact, quickly. 

Find ActiveOps ControliQ on the marketplace

Guarding against cyber disruption, 24/7 

Darktrace offers global leadership in cybersecurity AI. Rather than study attacks, Darktrace DETECT’s “Self-Learning AI” technology continuously learns about your organisation, inside and out, and applies that understanding to optimise your state of cybersecurity. Darktrace is fuelling a continuous end-to-end security capability that can autonomously spot and respond to novel in-progress threats within seconds.

Find Darktrace DETECT on the marketplace

Read Blog 2: Safeguard your business with AI-powered security solutions 

Harness emerging Causal AI to go beyond prediction 

Causal AI is a new class of machine intelligence that overcomes many of the issues seen with traditional machine learning and AI. Using Causal AI models, organisations can now go beyond making predictions to answer “what-if?” questions.  

causaLens is the only company to have productised Causal AI through its decisionOS solution – enabling customers to optimise pricing and promotions strategies, fine-tune the marketing mix, anticipate and pre-empt customer churn, and much more. decisionOS is available via the Microsoft marketplace.

Find causaLens decisionOS on the marketplace

Read Blog 3: Optimise business operations through AI-powered solutions 

Delivering outstanding people processes 

Zellis is the largest provider of payroll and HR software, and managed services, to companies in the UK & Ireland. Built on Microsoft Azure, Zellis HCM Cloud connects into PowerBI for analytics, and Power Automate to create integrated solutions for payroll, HR, benefits, and recognition – driving efficiencies and staff satisfaction across your entire organisation.

Find Zellis HCM Cloud on the marketplace

Read Blog 4: Deliver transformational employee experiences through AI-empowering solutions

Empowering smooth operations from start to finish 

Innovation is thriving across sectors such as banking, finance and insurance, with support from the Microsoft cloud. Enate helps large enterprises to better manage end-to-end workflow smoothly, harnessing the power of automation and AI.

Powered by the Microsoft Azure OpenAI Service, EnateAI saves Operations teams from having to buy, train and test costly AI MLops solutions from third-party vendors. Instead, just “switch on” EnateAI to extract data from documents, categorise and automate email processing, and understand your customers’ sentiments, driving efficiency and cost savings straightaway.

Find Enate solutions on the marketplace

Making smarter decisions, faster 

Imagine building your own AI-powered data extraction models with no data scientists required. That’s AI with real ROI, a solution offered by Eigen that reduces the amount of time your organisation spends on manual processes by up to 90%.  

Eigen’s no-code AI platform automates the extraction, classification, and understanding of data from any kind of document, so customers can make faster, smarter decisions. Leveraging Microsoft capabilities, Eigen’s software integrates multiple AI technologies, including natural language processing, machine learning, and computer vision.  Building a model requires only a small number of training documents, which means business users can start automating their document workflows quickly. 

Find Eigen solutions on the marketplace

Using AI to make working capital more accessible 

Trade Ledger’s mission is helping every business get the capital they need to thrive, through enabling banks and alternative lenders to simplify complex business lending. Using Large Language Models, finance professionals can query their business systems using natural language and get rich analysis and insights into their working-capital needs. AI matches their funding needs with appropriate lending products; once a product is selected, it performs the loan application process.  

By integrating AI, Trade Ledger bridges the gap between what businesses need and what lenders have on offer. It also speeds the application and decision-making process, contributing to a more accessible and transparent working-capital market. 

Find Trade Ledger solutions on the marketplace

Reimagining trade transactions 

Meanwhile, Traydstream – a trade finance document-checking automation and digitisation platform – has partnered with Microsoft Azure to reimagine the paper-based processes that support trade finance. Available on the Microsoft marketplace, Traydstream uses a machine learning-based engine, slashing the time to complete checks on the dozens of documents and over 400,000 rules-permutations generated by a single transaction. Traydstream is now collaborating with Citi to provide their clients with access to this cutting-edge and automated trade-document processing capability. 

Find Traydstream solutions on the marketplace

Start your AI transformation journey today  

Whether it’s safeguarding your organisation and data, amplifying human ingenuity or delivering transformational customer experiences, buying cloud-driven AI software solutions through the global marketplace allows your business to be more innovative, agile and resilient, with less complexity, time and cost. 

That’s because the Microsoft marketplace offers the most comprehensive catalogue of certified cloud solutions anywhere. We’ve made procurement simple, enabling you to complete your entire journey in one place, with straightforward invoicing.

Your organisation’s existing Azure cloud commitment means you can benefit from faster time-to-value, integrating solutions that work with your current technology. In addition, software/IP costs incurred by buying solutions contribute 100% off your Azure Marketplace invoice. You can also rest assured that you’re buying and running solutions on a trusted cloud that boasts industry-leading security. 

We hope you enjoy the other blogs in our series. Meanwhile, why not check out more solutions on the Azure Marketplace? For more information and partner introductions, contact our ISV team.

Other blogs in this series

Blog 2: Safeguarding your business with AI-powered security solutions 

Blog 3: Optimising business operations through AI-powered solutions 

Blog 4: Deliver transformational employee experiences through AI-empowering solutions

The post Driving AI transformation with the Microsoft commercial marketplace appeared first on The Microsoft Cloud Blog.

Back in June 2023, Sasha Sanyal, Microsoft Global Insurance Leader, blogged about the unprecedented excitement among insurance customers over the promise of generative AI. Customers were inspired by Microsoft’s vision for intelligent insurance and how the Microsoft Cloud and our global partner ecosystem were providing opportunities to build solutions that could not just deliver efficiencies but also create industry-changing competitive advantages.

Today, many visions are on their way to becoming powerful realities. The level of interest and creativity we’ve seen from insurance customers has been off the charts. Working alongside our partners, we have been more than busy brainstorming with business leaders and technology teams, hacking solution concepts, and collaborating with customers to build differentiating capabilities.

Microsoft Cloud for Financial Services

The future of financial services in the era of AI

Explore capabilities

Use cases help to drive AI innovation in insurance

Central to our exploratory engagements with insurance companies is the challenge of finding effective use cases—real-world scenarios that identify specific business problems and define how capabilities or features can be implemented to deliver measurable results in solving them. A good use case can serve as a north star to help in the design, development, and measurement of a solution. And because a use case is tight and distinct, it can help a customer make fast, efficient, and insightful early investments in experimenting with generative AI. Out of our customer deep-dives, a set of distinctive use cases has emerged that we recognize as especially worthwhile for insurance companies as they take their first important steps on their AI journeys.

Context is important here. Above all, insurers are anxious to use generative AI to transform their businesses in virtually every aspect of operations, customer engagement, and product innovation. They see, for example, the potential of content generation to write automatic responses to customer inquiries, of summarization to assist in producing support conversion logs or financial reports, of semantic search to enable fast information discovery and knowledge mining, and code generation to convert natural language to structured query language (SQL) (or vice versa) for telemetry data. This list barely scratches the surface of what’s possible.

Insurance customers are choosing Microsoft Cloud for Financial Services because it provides enormous flexibility and control in creating solutions based on an organization’s unique needs. For companies that want to build their own generative AI solutions, Microsoft Azure OpenAI Service brings together advanced models from OpenAI with the enterprise capabilities and advanced security of Microsoft Azure. AI is also being integrated into the Microsoft Cloud and in the Microsoft products that customers already use and love, including the upcoming Microsoft 365 Copilot, which will create and edit documents, presentations, and emails.

Top generative AI use cases for insurance companies

In considering options to deploy generative AI, insurers and other risk-sensitive companies are starting with use cases that can help them learn without creating risk for customers and clients. Where generative AI is being used to generate content, humans need to be in the loop to review output and ensure compliance. Still, these early-horizon requirements leave plenty of room for innovation in early deployments.

 Three categories of use cases are gaining traction with customers today:

1. Helping deliver meaningful and engaging customer experiences

Generative AI can have a tremendous impact in transforming each phase of the insurance customer journey.

• In the discovering and exploring phase, company agents can receive automatically generated notes and recommendations following a customer call, or produce tailored, auto-generated emails to follow up on issues or opportunities.
• In the buying and using phase, agents can generate scripts to drive conversations based on customer profiles and conversation histories.
• In the asking phase, agents can automate follow-up activities or retrieve fast knowledge-based responses to customer queries, in simple terms.
• In the engaging phase, agents can be alerted to birthdays or other important customer moments and generate tailored emails and messages to nurture relationships.

2. Driving efficiencies in corporate functions

Generative AI will potentially change the way people work in insurance, impacting core function across the business.

• In contact centers, the ability of customer service representatives to respond faster and more effectively will be enhanced through capabilities that enable streamlined workflows such as automated follow-up on emails and the ability to search and retrieve knowledge-based information in contextualized natural language.
• In human resources, the ability to attract and retain high quality candidates will be enhanced with tools that more effectively screen resumes, engage candidates through the hiring process, and deliver the knowledge that new employees need to do their jobs from day one and beyond.
• In legal, the ability to help attorneys and support staff to work more efficiently will be enhanced with tools that generate legal and regulatory summaries and assist with compliance and other specific challenges.  
• In marketing, the ability to connect with key stakeholders and tell the brand story will be accelerated by AI-generated ideas for content across channels and personalized, targeted email campaigns to promote products or services to specific audiences.

3. Creating copilots for each phase of the insurance life cycle

For virtually every aspect of the insurance life cycle, there is an opportunity to enhance experience and add value through a copilot—an application that uses generative AI and large language models to assist with complex tasks and offer a suggested course of easily executable action. Insurers are exploring early generative AI use cases across the business. For example:

• Customer copilots can help customers find the information they need about policies and services across a variety of web and mobile interfaces.
• Underwriter copilots can help underwriters augment decision-making with automatic summaries of applications and policy documents.
• Claims examiner copilots can help claims managers evaluate filings, process settlements, extract data from documents, or facilitate data entry.
• Actuarial copilots can help actuaries expedite trend analysis and automate mundane tasks.

Get engaged now on your generative AI journey

Now is the time for insurers to evaluate and experiment with AI, as it is revolutionizing how insurers assess risk, process claims, interact with policyholders, and empower employees.

We invite you to learn more by contacting your Microsoft representative or solutions partner. Or, for those who plan to attend InsureTech Connect (ITC) from October 31 to November 2, 2023, in Las Vegas, Nevada, we invite you to meet with us in person at Mandalay Bay’s South Convention Center, Lagoon L, where we will showcase the use cases and capabilities that we and our partners are bringing to market. In addition, Microsoft leaders will be presenting at the event in main program and partner sessions on key topics in the industry, including the following:

• Data-Smart Life & Annuities: Embracing ChatGPT and AI for Insurance Transformation—Jeffery Williams, Director, Insurance Digital Strategy, Worldwide Financial Services, on Tuesday, October 31, 2023, from 2:20 to 3:20 PM PST in Mandalay Bay Ballroom J.
• Microsoft: Embracing Open Insurance: Unlocking Opportunities within Distribution— Sasha Sanyal, Worldwide Financial Services, Americas Regional Leader, on Wednesday, November 1, 2023, from 2:40 to 3:00 PM PST in Mandalay Bay Ballroom F; and “Women’s Leadership Forum,” Wednesday, November 1, 2023, from 3:00 to 5:00 PM PST in the Foundation Room.
• How AI Is Changing the Way We Operate in Insurance—Jim DeMarco, Insurance Practice Lead, on Thursday, November 2, 2023, from 12:30 to 1:30 PM PST in Mandalay Bay Ballroom G.
• Automating the Submissions Intake Process with AI to Drive Better Decisions and Better Outcomes (Presented by Indico Data)—Tom Wilde, CEP, Indico Data, and Naveen Dhar, Director, Insurance Digital Strategy, Worldwide Financial Services, on Thursday, November 2, 2023, from 2:40 to 3:00 PM PST, Reef A-F.

Next steps

You can learn more about how Microsoft Cloud for Financial Services is helping our customers realize the future of insurance in the era of AI, unlocking business value, and deepening customer relationships.

The post Insurance in the era of generative AI: Use cases that transform the industry appeared first on The Microsoft Cloud Blog.

The contact center agent who answered Matt’s call immediately authenticated him with voice biometrics, removing potential friction from this highly charged moment. Guided by AI-powered insights and suggestions, the agent then walked Matt through a quick yet complete intake process. Automatically, the contact center platform sent Matt an SMS link to upload photos of the damage, as well as follow-up communications with details of his claim. Afterwards, the platform generated a call summary, and extracted details and insights for future calls. And Matt, despite the fender bender, felt better about his insurer for making the process so easy.

This scenario, which until very recently would have seemed futuristic, is just one of many examples of how generative AI is redefining the very notion of a contact center for insurance companies, banks, and other capital markets firms. It is only one example of the kind of transformation we at Microsoft together with our global partners are helping customers realize with Microsoft Cloud for Financial Services.

The contact center cost conundrum

COVID-19 highlighted the critical importance of contact centers to financial services businesses, as housebound customers had only phone calls, emails, and digital access to connect with their financial service providers. It was suddenly left to contact center agents and customer-facing digital tools to handle the lion’s share of many key functions, such as responding to customer problems, providing important information, and assisting with transactions and account issues.

As COVID-19 pressures ease and new business requirements emerge, companies now face new challenges to keep their contact centers vital and efficient. Among them:

• Modern customers expect personalized experiences, and they get frustrated by disconnected interactions and incomplete information that can result from siloed systems and data.
• Customers demand simple and effective self-service experiences and are turned off by limited solutions.
• Contact center agents want to focus on solving problems for customers, but struggle to use cumbersome tools and difficult training requirements, which can result in high rates of attrition.

The leaders we’ve talked to in financial services firms recognize that contact centers represent a major opportunity to enhance the bottom line and drive new revenue. But contact centers have traditionally been cost centers. Now with generative AI, companies have quickly recognized that the new capabilities of large language models (LLMs) and data analytics can further unlock business value.

The generative AI contact center

Generative AI is transforming contact centers with a set of powerful new capabilities such as:

• Content creation: authoring content that is similar in style and tone to what people are used to.
• Summarization: writing summaries from large volumes of data.
• Semantic search: using natural language to understand the intent behind queries.

These provide the basis of innovations such as intelligent chatbots, virtual assistants, and customized solutions that use natural language processing, interactive voice response (IVR), trend and sentiment analysis, and much more.

In practical terms, generative AI transforms the contact center into a highly efficient asset that understands abstract context, provides engagement at a near-human level, and empowers agents and employees to deliver service faster and more effectively.

The potential impacts of generative AI innovations are huge. Personalized interactions can dramatically boost service-to-sales conversions. Perceptive self-service tools can spike customer satisfaction through instant gratification. And tools that reduce data entry can improve agent productivity. On the expense side, AI can reduce labor costs by reducing training requirements for agents who, in turn, can deliver better customer experiences that increase customer loyalty.

We see generative AI providing near-term benefits for financial services firms in the following broad categories of use cases:

• Agent assistance: Empowering customer service and helpdesk agents with bots to provide insights and next-best-action guidance derived from disparate knowledge sources across the organization. Assistance can also incorporate multi-language transcription, management bots for forecasting simulations, and automated call scoring.
• Fraud detection: Continuously monitoring and analyzing incoming data streams for potential fraud. For example, advanced biometric detection not only enables immediate authentication based on a customer’s unique voiceprint, but it also recognizes an attempt to impersonate the customer.
• Interaction analytics: Identifying patterns in customer behavior to deliver increasingly personalized responses. Generative AI can look across every conversation (voice, text, email, and so on) to identify trends, inform product improvements, enhance agent coaching, and ensure compliance.
• Self-service: Automating routine tasks and repetitive queries with conversational chatbots and IVR capabilities that enable computers to have conversations with customers. Over time, generative AI can also create personalized responses in customer engagements based on experience and learning from across the organization.

Put generative AI to work in your contact center

Microsoft offers a broad suite of solutions and capabilities that addresses the contact center needs of financial services organizations. The generative AI use cases listed above, enabled by Microsoft technology, can be provided on any contact center platform including those from NICE CXone and Solgari, which run on Microsoft Azure. In addition, they can be provided through solutions in Azure from partners such as Verint and Callminer. Microsoft Azure data and AI services application programming interfaces (APIs) integrate easily with call center platforms to enable new use cases, which are enhanced greatly through the solutions and contributions of our global partner ecosystem and can be integrated into Microsoft Teams and Dynamics 365.

Contact center transformation at Money20/20 USA

We were excited to showcase Microsoft’s remarkable innovations in contact center transformation at the Money20/20 USA conference in Las Vegas, Nevada from October 22 to 25, 2023. We demonstrated innovative solutions and highlighted the progress our customers and partners are making with solutions built on Microsoft Azure OpenAI Service and the Microsoft Cloud. Among the highlights:

• At the Microsoft AI Lounge Powered by Nvidia, we hosted 30 presentations from more than 20 partners across a broad range of demos, case studies, and thought leadership topics. We were delighted to showcase how Microsoft is using generative AI in our own contact centers (with all 55,000 of our agents), which is delivering remarkable benefits.
• Microsoft partners presented an innovative set of solutions featuring generative AI scenarios in our lounge and in the Money 20/20 pavilion.
• Microsoft’s Daragh Morrissey, Director, Worldwide Financial Services, discussed Microsoft’s broader generative AI approach in a panel discussion on “AI, Quantum, and Crypto: Get Ready for the Impact on Payments.”

Get started on your journey today

Using generative AI to reinvent the contact center is an unparalleled opportunity not just to deepen customer relationships but to unlock new business value. With generative AI and the Microsoft Cloud, financial services businesses can optimize costs, reduce time to value, enhance collaboration, and use data to deliver more impactful business outcomes.

Learn more by visiting our website where you can discover how Microsoft and our partners are working together to help our customers transform financial services. If you want to accelerate your transformation journey, feel free to reach out to us or contact your Microsoft representative or technology partner.

Microsoft Cloud for Financial Services

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The post How Microsoft and generative AI are transforming financial service contact centers appeared first on The Microsoft Cloud Blog.

Every insurer is keenly attuned to addressing the challenges the industry has faced in recent years—economic uncertainty and industry-specific concerns around climate change and geopolitical upheaval, to name but a few. Another challenge is technology, which holds the promise of either competitive differentiation (winning) when done well, or the threat of market loss or disintermediation (losing) when done poorly. There’s no middle ground.  

All this was true before 2023. Then, the world shifted. With the arrival of ChatGPT and generative AI, the power of AI became more accessible to everyone. For Microsoft, this will drive an evolution of our vision for intelligent insurance. For insurance companies—with their data-centric product focus, to their vast ecosystems of digital-savvy agents and customers—it is simply a red-hot opportunity.  

Microsoft and generative AI: Creating new possibilities 

We are seeing an unprecedented level of interest in AI technology that we’re seeing now around generative AI, which I generally define as a system for generating text, images, or code in response to user input or prompts, drawing on vast sets of data. Our customers are asking: What can it do for us now? What are the best use cases? How do we get started? 

The first thing we tell them is that generative AI is close at hand. For companies that want to build their own generative AI solutions, Microsoft Azure OpenAI Service brings together advanced models from OpenAI, including ChatGPT and GPT-4, with the enterprise capabilities of Azure. Critically for insurers, all data uploaded—including training data and content—is isolated within your Azure subscription so it stays within the bounds of your organizations. By being fully integrated into Azure, insurers also get all the advantages of enterprise-grade security and role-based access included. And it’s only getting better, as we recently announced the availability of GPT-4 in a preview release. GPT-4 is OpenAI’s most advanced large language model, enabling you to drive insights with greater accuracy than previous large language models developed by OpenAI. 

This is in addition to the fact that AI is already being integrated into the Microsoft Cloud and in the Microsoft products that customers already use and love. We recently announced Microsoft 365 Copilot, which integrates amazing new generative AI capabilities across the Microsoft productivity suite, and this is complemented by copilots for Bing Search, Microsoft Dynamics 365, Viva Sales, and GitHub. 

That’s a ton of innovation—so much that it may seem overwhelming. Let me offer some insights into what we see as the most important capabilities for insurers and how those will play out in use cases.  

Here are the top three generative AI capabilities that we expect will quickly take hold among insurance companies and deliver differentiating benefits:  

1. Content creation and summarization. Generative AI will be widely used within organizations to generate things like proposals, reports, presentations, and summarizing internal meetings and customer conversations.
2. Semantic search. Using natural language and context, searching will become smarter and faster, and will be continuously trained as you interact with your customers.
3. Code generation. With copilot capabilities for generating sophisticated code, developers will spend less time writing lines of code and more time designing new statistical models and mathematical tools for actuarial challenges.

Generative AI use cases insurers will love 

With these capabilities, we can enable some compelling scenarios. The following use cases can empower and drive productivity across the organization: 

• Empowered contact center agents. With the ability to transcribe, summarize, and get insights on every customer engagement, agents can instantly measure sentiment from the start to the end of a conversation. Contact center staff can get coaching in real time. And new intelligence can be fed into a contact center knowledge base to give agents better and faster responses to future questions. Insurers can benefit from aggregated insights for tracking key performance indicators for customer satisfaction, engagement, and Net Promoter Score impact, continuously improving the experience for customers.
• Empowered underwriters. In complex lines of business—as in commercial, specialty, and reinsurance—underwriting involves the processing of large amounts of unstructured data. Generative AI, in conjunction with other AI capabilities like optical character recognition (OCR), can play the role of an “underwriting assistant” by extracting and organizing the various forms of data, summarizing the content, and then suggesting areas of opportunity for underwriters to consider based on appropriate logic. Documentation can then be generated to bind the policy. This should free underwriters from non-core tasks and administrative tasks that typically take up to 40 percent of their time.
• Empowered claims managers. Generative AI will impact almost every step in the claims lifecycle. Customer service representatives can use it to summarize policy documents during the first notice of loss and more quickly respond to customer inquiries along the claims journey. Claim adjusters and handlers will use it to quickly ensure that high-priority claims are handled promptly and that lower-priority claims don’t cause delays or backlogs. Insurance recovery specialists will use it to help in the coding of subrogation and reinsurance claims to expedite the recovery of capital due from third parties or reinsurers.
• Empowered insurance brokers and agents. Generative AI enables you to deploy powerful knowledge base systems. As customers seek to buy new insurance products or renew their existing coverage, product knowledge bases enabled through a chat-like experience could empower brokers to answer customer questions about coverage levels and other benefits.
• Bigger and better virtual assistants. A new customer journey often begins online, where digital agents play a large role in helping them explore insurance. Generative AI reimagines the whole notion of a chatbot and enables new types of engagements. Customers and prospects will be able to ask all kinds of questions in natural language and quickly get contextualized answers. Employees and agency partners will also have better experiences by getting answers fast to the questions needed to do their jobs. 

Responsible AI by design 

Generative AI can sometimes generate inaccurate output, so for all use cases, human oversight and supervision are key. As next-generation AI innovation gains momentum, we are optimistic about what it can do for people, commerce, and society. Microsoft advancements in AI are grounded in our company mission to help every person and organization on the planet to achieve more. We’re committed to making the promise of AI real—and doing it responsibly. Our approach to AI is based on three principles: meaningful innovation, empowering people and organizations, and responsibility. 

Accordingly, we’re dedicated to the responsible development of AI systems for the industry, ensuring they will function as intended and be used in ways that earn trust. We were one of the first major technology companies to call for thoughtful government regulation on facial recognition technology and are committed to creating responsible AI by design through our Responsible AI standard. Read “What is Microsoft’s Approach to AI?” for more information.

What’s next for intelligent insurance

Our mission is centered around empowering insurers, and we are thrilled to introduce them to our generative AI advancements through our Azure OpenAI Service and Microsoft 365 Copilot offerings. Moreover, we will collaborate with our industry partners to facilitate the integration of these powerful capabilities into their own solutions. I am eager to witness the innovative creations that our insurers and partners will develop with generative AI. Our collective efforts will allow us to use the most advanced AI models available to meet business objectives with responsibility, security, and the assurance that comes with the Microsoft Cloud. 

Find out more about our vision for intelligent insurance and how the Microsoft Cloud and our global partner ecosystem can help you address evolving customer needs, hybrid workplace requirements, complex regulations, new competitors, solution integration, and the increased frequency and severity of claims.

Finally, learn more about the era of generative AI across the financial services industry by reading other posts in this series:

• The era of generative AI: Driving transformation in banking
• The era of generative AI: Driving transformation in capital markets

Empowering intelligent insurance

Find new ways to engage customers and improve risk modeling to achieve your business outcomes.

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The post The era of generative AI: Driving transformation in insurance appeared first on The Microsoft Cloud Blog.