The insurance industry is undergoing a profound transformation, and artificial intelligence (AI) is at the forefront of this change. While the potential of AI has been widely discussed, its real-world impact is already being felt across the insurance landscape. This blog post delves into how AI is revolutionizing key insurance functions, enhancing operational efficiency, and ultimately improving the customer experience.

Insurance: A Data-Driven Industry Embracing AI

Insurance relies heavily on data. From assessing risk and pricing policies to processing claims and detecting fraud, data analysis is crucial. Gen AI, with its ability to analyze vast amounts of structured and unstructured data, is a natural fit for the insurance industry. This adoption is further accelerated by the ongoing cloud migration and data platform modernization efforts within the sector.

With our extensive data expertise (over 25 years of enterprise data management experience) and a deep understanding of the challenges that AI can solve in the insurance sector, Bitwise offers advanced data and AI solutions for insurance to help achieve transformational results. Below are key areas that we’ve identified in working with Fortune 500 insurance customers where Gen AI is making a major impact.

Three Key Areas Where AI is Making a Major Impact

Artificial intelligence is rapidly transforming numerous industries, and the insurance sector is no exception. From streamlining intricate workflows to bolstering security measures, AI is proving to be a powerful catalyst for positive change. This section will delve into three key areas where Gen AI is currently making a significant impact within insurance: claims processing, underwriting, and fraud prevention.

• Claims Processing: The claims process is a critical touchpoint in the customer journey, but it’s often plagued by inefficiencies and errors. AI is streamlining this process by automating data retrieval and reconciliation from various internal and external sources. This enables faster and more accurate claims adjudication, reducing processing times and improving customer satisfaction. Bitwise offers data insights and analytics solutions to achieve business results by making insurance claims faster and easier using AI and ML techniques for data-driven personalized pricing models.
• Underwriting: The underwriting process can be lengthy and cumbersome, especially in life insurance. AI is accelerating this process by quickly gathering and analyzing customer information, enabling faster onboarding and more personalized policy offerings. The automated underwriting process opens opportunities to cover a wider range of individuals, including those who may have been previously underserved. We offer legacy data maintenance solutions to diagnose and fix data problems using custom quality checks and data validations to achieve optimal query performance and accuracy.
• Fraud Prevention: Insurance fraud is a significant challenge, costing billions of dollars annually. AI is enhancing fraud detection by analyzing diverse data sources in real-time, including claims histories, public records, and medical guidelines. This proactive approach helps identify and prevent fraudulent claims more effectively.

AI: Driving a Better Customer Experience

While AI’s immediate impact is often focused on operational improvements, the ultimate beneficiary is the customer. Faster AI-powered claims processing, personalized policies, and reduced fraud contribute to a smoother and more satisfying customer experience. AI is also enabling insurers to deliver more empathetic and efficient service, particularly during emotionally challenging situations like claim filing after the loss of a loved one.

Insurance companies trying to implement Gen AI use cases struggle due to issues like scattered data, long turnaround time and inability to assess data. We offer a proven methodology in our data platform modernization solution for transitioning legacy databases, database objects, data ingestion, orchestration and data visualization to the cloud using automation through each phase of assessment, code conversion and data validation – establishing a data foundation designed for AI success.

Other Key Trends Shaping the Future of Insurance

In addition to AI, other key trends are shaping the future of insurance:

• Consumer acceptance of AI is growing: Research indicates increasing comfort with AI-powered tools in insurance, particularly in areas like premium determination. A recent study published in InsurTech Digital illustrates the trend with “59% of UK respondents willing to accept fully AI-driven claims approval if it delivers accuracy and speed.”
• IoT is gaining traction: The growing use of activity trackers and other IoT devices provides opportunities for personalized insurance offerings and data-driven insights. Bitwise offers solutions to develop mobile applications that speed up processes, such as claims processing, and provide greater convenience, transparency and accessibility for customers.
• EVs and Sustainability: The shift towards electric vehicles (EVs) necessitates innovation in underwriting and risk assessment.
• ESG considerations: Environmental, social, and governance (ESG) factors are becoming increasingly important for consumers and investors, influencing insurance product development and investment strategies.
• Cyber risk management: As cyber threats evolve, robust cybersecurity measures are crucial for protecting sensitive customer data and ensuring business continuity.

Case Study of Solving Real Business Problems with AI

A leading Fortune 500 insurer struggled with inefficient and inaccurate data extraction from diverse unstructured documents, hindering claims processing. Bitwise developed an AI data analyzer solution for streamlined processes on claims platforms that addressed the challenge with an AI-driven solution built on LLMs and Databricks. The result: automation of key data point extraction, achieving 85% accuracy and dramatically accelerating quote turnaround by 90%, showcasing the power of AI in transforming operational efficiency.

Conclusion

By embracing these trends and adopting a strategic approach to technology integration, insurers can unlock new opportunities, enhance customer experiences, and thrive in a rapidly evolving market. The future of insurance is technology-driven, and those who embrace innovation will be best positioned for success. Contact us to learn how technology can transform your insurance business.

The post AI is Revolutionizing Insurance: Here’s How appeared first on Bitwise.

Databricks

Databricks has gained popularity as a platform of choice for data scientists and engineers due to its ease of use and features like Databricks SQL, artificial intelligence and machine learning. One of its unique features is the Unified Analytics Platform, which integrates various data analytics and machine learning workflows into a single, collaborative environment. This feature streamlines the process of building and deploying data applications, assessing and handling large volumes of data.

Figure:Databricks Platform

Source: Databricks.com

Databricks is built on open-source technologies and founded by the creators of Apache Spark. It offers strong support for machine learning and artificial intelligence projects. Its integration with popular machine learning libraries like TensorFlow and PyTorch makes it a powerful tool if you are looking to develop and deploy advanced models.

Databricks fits well with data engineering teams that prefer programmatical coding over drag-and-drop capabilities and visualizations for more flexibility. However, some clients find the Lakehouse hard to understand and learn, partly because of the large number of integrations, tools and features available. In such scenarios, Bitwise experts help organizations in navigating their data modernization objectives with understanding and implementing Data Lakehouse concepts.

Microsoft Fabric

Microsoft Fabric is an all-in-one enterprise solution providing a suite of facilities to democratize data pipelines, analytics and lakehouse practices resulting in an empowered data journey. It is also appealing to smaller businesses and startups that require robust analytics capabilities.

Figure:Microsoft Fabric

Source: Microsoft

The AI-powered analytics platform provides a user-friendly interface, making it accessible to a wide range of users, including those without extensive technical backgrounds. Microsoft Fabric’s simplicity can accelerate the onboarding process and reduce the learning curve for new team members. It provides a solid foundation for data analytics without the added complexity or expense of more advanced platforms.

The end-to-end platform simplifies data management by connecting data services like Data Lake, Power BI, Microsoft Purview and Synapse Workspaces. Analytics users can also use Microsoft Copilot (a Generative AI assistance tool) in Power BI to find more insights, ask questions and develop Power BI reports. Microsoft Fabric is a great fit for building reliable and scalable applications with AI-powered analytics and provides several features involving support for microservices based applications and integration with other Azure services. For a more detailed analysis, read our blog on Microsoft Fabric and best practices for achieving data modernization objectives.

Snowflake

Snowflake is designed to handle massive datasets and complex queries with exceptional speed and efficiency which makes it an attractive option for enterprises dealing with large-scale data analytics and mission-critical applications.

Figure:Snowflake Platform

Source: Snowflake

Snowflake helps to provide fresh insights and develop AI/ML capabilities by breaking down data silos and integrating with external data. With its Cortex AI, Snowflake enables business users to build generative AI applications and find answers in their data faster.

A key feature of Snowflake is the distinct separation of storage and compute. Separation of these resources allows high-performance and effective zero-copy cloning. Other than this, the platform’s time travel abilities allow you to do retroactive analysis and track the historical versions of your data in a time-period. Depending on the requirements of organizations, the cloud-agnostic nature of Snowflake can be both a disadvantage and an advantage. However, Snowflake gives the flexibility to run in Google, Microsoft and AWS public clouds as there is no vendor lock-in.

Choosing the Right Platform for Your Needs

The choice between Databricks, Microsoft Fabric, and Snowflake hinges on your organization’s specific needs and priorities. Here are some considerations to help you make an informed decision:

• What skill sets do you currently have in your organization and what new training or hiring will be needed?
• Can you perform data profiling to better understand your data readiness for AI and advanced analytics?
• What type and how much data does your company possess?

Consider Microsoft Fabric when your business has:

• Existing users of Microsoft or Azure products
• Requirement to add new features like Fabric’s AI powered Copilot and Power BI Direct Lake mode
• Need for multiple development studios

You may want to use Databricks if your organization:

• Requires integration of latest engineering features such as GPU clusters
• Needs to host and build databases withing the platform
• Supports more languages for data analysts and scientists like Python, Java and SQL

You might select Snowflake if your company:

• Wants to leverage the data vault architecture or has real-time data
• Has a large volume of data at business intelligence level and data warehouse
• Handles large amounts of semi-structured and structured data

Conclusion

Databricks, Microsoft Fabric and Snowflake are all excellent data analytics and AI platforms. The best platform for you will depend on your specific needs and requirements. Ultimately, the best way to choose the right data analytics platform for your business is to evaluate your specific needs and requirements. Consider factors such as the size and complexity of your data, the types of analysis you need to perform, what AI use cases you want to develop, and your budget.

By doing your research, you can find the platform that best meets your needs. Evaluating these platforms based on your unique context will help you make an informed decision that best serves your data-driven goals. Connect with our experts to explore how Bitwise can assist in this evaluation process, tailoring recommendations to your specific objectives.

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The race is on for companies of all sizes and industries to deliver impactful artificial intelligence and machine learning applications. Fueled by the hype and fear of being left behind, many business leaders are throwing money and resources at implementing AI/ML use cases without having a clear understanding of a specific business problem to solve or business outcome to achieve. Having successfully implemented machine learning solutions at a wide range of customers, Bitwise has seen first-hand the importance of identifying the right use case and building a strategy around identified success metrics to achieve a positive ROI with AI/ML.

Let’s look at five use cases for driving ROI with machine learning, including success metrics, to help identify the right use case for you to start with AI/ML in your organization.

Identifying the Right Use Case for Machine Learning

Finding the right use case to get started sounds easy enough but can often be a challenge, especially with all the noise around the transformational nature of AI/ML technology. To cut through the noise, Bitwise recommends to start small and identify a pain area or gap in your business processes. Even a simple use case can achieve a meaningful ROI, which will provide a strong foundation to build your internal competency and stakeholder buy-in to develop more use cases.

Some of the early use cases like recommendation engines and reducing customer attrition, for example solving merchant attrition using machine learning in the financial industry, have been widely adopted and continue to deliver results. The below use cases provide other examples for implementing machine learning with associated success metrics to help you get the ball rolling.

5 Machine Learning ROI Use Cases

1. Billing Optimization

Complex billing processes can be difficult to manage and provide a poor customer experience. Optimizing billing with machine learning can streamline the process and improve efficiencies and accuracy.

• Cost Savings: 15% reduction in operational costs. This metric reflects the reduction in overall operational costs achieved through streamlined billing processes, directly enhancing profitability.
• Error Rate: Decrease from 2.1% to 0.5%. This measures the reduction in billing discrepancies, ensuring accuracy and enhancing customer trust.
• Cycle Time: Reduced from 30 days to 18 days. This tracks the time required to complete the billing process from initiation to closure, facilitating quicker revenue recognition and improved cash flow.

2. User Support Chatbot

User support is a critical business process that typically requires heavy human interaction. Using machine learning to optimize the support process can help support staff be more efficient and provide a better customer experience.

• Resolution Rate: 85% of queries resolved without human intervention. This metric indicates the proportion of queries resolved by the chatbot augmenting human interaction, showcasing the efficiency and reliability of automated systems.
• Customer Satisfaction Score (CSAT): Increased up to 90%. This score assesses customer approval of interactions, reflecting commitment to providing seamless support.
• Average Handling Time: Reduced to 3 minutes per interaction. This measures the time it takes to handle inquiries, demonstrating the speed and efficiency of augmented customer support.
• Service Efficiency: Optimized support workforce for critical customer support, reducing support cost by 40%.

3. Predicting Wait Times

Industries like Hospitality and Heathcare are often plagued by wait times for their customers. Using machine learning to predict wait times for patrons and patients can provide a competitive edge for organizations where customer service is critical.

• Accuracy of Predictions: up to 95% accuracy level. This metric evaluates the precision of predicted wait times against actual experiences to ensure accurate expectations.
• Customer Satisfaction: Improved by up to 20%. This tracks improvements in customer or patient satisfaction, which correlate with accurate wait time predictions and efficient service delivery.
• Service Efficiency: 30% increase in patient throughput. This assesses how effectively wait time predictions contribute to optimizing workforce allocation and enhancing service speed.

For example, Bitwise helped a national restaurant chain with predicting order wait time using machine learning to provide an improved experience for their online and dine-in customers by showing how long they will have to wait for their order to be served with 87% accuracy.

4. Pricing Optimization

In today’s omnichannel digital landscape, customers have a variety of options to purchase goods and services. Optimizing pricing with machine learning can provide a competitive advantage while maximizing margins.

• Revenue Growth: 10% increase in quarterly revenue. This metric monitors the increase in revenue attributable to strategic pricing decisions, underlining the effectiveness of pricing models.
• Price Elasticity: Sales volume changes by 5% for every 1% price adjustment. This measures how sensitive customer purchase volumes are to changes in pricing, helping to tailor pricing strategies based on consumer behavior.

5. Competitor Analysis

Companies have a trove of data that can lead to valuable insights when asking the right questions. Analyzing the competition using machine learning can help optimize marketing and customer acquisition efforts.

• Customer Acquisition Cost (CAC): Reduced by 12%. This measures the efficiency of the customer acquisition initiatives, especially those driven by insights from competitor analysis, optimizing marketing spend and improving return on investment.

Identify Your AI/ML Use Case to Drive ROI

The examples of billing optimization, user support chatbot, predicting wait times, pricing optimization, and competitor analysis showcase a variety of machine learning use cases but by no means cover the full gambit of what’s possible with AI/ML.

Looking internally at business processes and pain areas can be a great place to start identifying your AI/ML use case with a focus on driving ROI. For example, we’ve found that for some of our customers, errors with data pipelines and data inconsistencies caused problems impacting their critical applications and resulted in cost increases and revenue losses. By implementing proactive monitoring of data platform using machine learning, potential problems can be prevented and avoid costly delays in time-to-production.

Ready to take the next step? Let’s talk about your AI/ML objectives and explore how Bitwise can help you identify the optimal use case and success metrics to put you on the path to achieving ROI and driving adoption in the organization.

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