Insight Category: Insurance Core Modernization

The insurance industry is undergoing significant changes due to the emergence of new risks, advancements in technology, availability of external data, and shifts in consumer preferences. This presents opportunities for insurers to use data and insights to improve operations, personalize products and services, and compete in new ways. To stay competitive, insurers must move quickly to drive AI-driven innovation and improvements while addressing these risks. One way to do this is to focus on DataOps and MLops, popularly known as XOps, which is the ability to iterate quickly and effectively across the entire lifecycle of algorithmic models. This allows insurers to track their progress and become a more data-driven business.

However, scaling data science to achieve these rewards takes time. Successful insurance companies have built an excellent analytical process to create a steady flow of models to tap into this new opportunity. However, getting it wrong can lead to spiraling operational expenses, significant financial and reputational risks, and creating wrong models or misuse.

Successful companies have a holistic approach to increasing efficiency throughout the data science lifecycle. This approach is called Enterprise XOps/MLops, which is a combination of technologies and best practices that streamline the management, development, deployment, and monitoring of data science models at scale across an enterprise. This paper will discuss the challenges of scaling data science and will explain how a poorly defined approach can lead to obstacles and how Enterprise MLops can overcome this challenge. 

Scaling Machine Learning is Difficult 

Companies leveraging the XOps (DataOps, ModelOps and MLOps) have high expectations from their data science teams, with 25% expecting outcomes that increase revenue by 11% or more. Gartner predicts that by end of 2024, 75% of organizations will shift from piloting to operationalizing their AI usage, driving a 400% increase in streaming data and analytics infrastructures. However, despite large investments, results have not been as successful as expected. Additionally, a significant number of companies plan to scale data science capabilities within the next five years leading to the increased importance of an enterprise MLOps approach that avoids building operational silos. To overcome these obstacles, companies can apply the technical principles of MLOps to the entire data science lifecycle and consider how to apply these efficiencies to processes and people.

Entry Barriers to Scale for Models

Advancements in the last decade in using AI and Models in insurance have focused on building models with little focus on operationalizing machine learning model development at the enterprise using advanced infrastructure management and Dev/Ops automation. There have been many barriers, including analysts and data scientists waiting for data from data engineers, code refactoring for production, fixing data quality issues when developing models, retraining ML models with new training data, waiting for testing to be completed, provisioning environments for analytical workloads, packaging dev outputs for production and fixing outages due to excessive load. It is easier for platforms to solve scaling problems in the back-end production of the data science lifecycle rather than the research and development in the front end. In the back end, the model is already built and packaged as a file, supported by a data pipeline, and often wrapped in a container.

How Enterprise MLOps Scales Data Science

The core capabilities of an Enterprise MLOps platform provide a comprehensive approach for scaling data science for model-driven companies. Such an approach must address data access, model validation, model automation, and monitoring gaps. Enterprises must build models, test, deploy and monitor in a continuous cycle that integrates machine learning, software development, and IT release management and deployment.  These capabilities cover four phases of the entire data science lifecycle: manage, develop, deploy, and monitor business outcomes. By providing capabilities for the entire lifecycle, a model-driven business can avoid common mistakes and issues arising from a more limited definition of MLOps. MLOps can operationalize data science at scale.

A mitigation plan involves collaborative working in a single team, developing coding standards and best practices, creating metrics and processes around that, using model registries and version control to rerun pipelines, enabling a dynamic sandbox environment utilizing cloud services, and building a continuous deployment pipeline.

Mlops Capabilities that Organization should bring about

Managing the Data Science Lifecycle is the first phase of the data science lifecycle. A significant objective of this phase is breaking down knowledge silos that keep data scientists from collaborating. Because data scientists often work independently with various tools, there are no standard ways of working, compromising governance, auditability, reproducibility, etc. Apart from creating feature Store solutions, Model development tools, CI/CD and code repository, ML compute engines, workflow and model orchestration, Data model and experiment tools, deployment tools, and monitoring tools are important for a carrier from a capability standpoint. For example, Feature Stores make the data scientist’s work more convenient & efficient by abstracting much of the engineering work required to acquire, transform, store, and use features in ML training and inference. Strong project management capabilities are also essential for scalability in this phase. The managing stage will enable control and collaboration of large stakeholders and facilitate audit and review processes.

Developing Models for Business Use Cases  

In the Development phase of the data science lifecycle, access to the right tools and infrastructure is essential for data scientists to be productive and innovative. When data science teams cannot access the necessary resources, they may create ad-hoc workarounds that involve building and maintaining their local infrastructure, leading to inefficiencies, frustration, and increased operational and security risks. Complex problems arise when sourcing and blending raw, structured, and external/cloud data at scale. Additional complexity arises from inadequate model feature stores, model packaging, and validation capabilities. A new data orchestration approach is available to accelerate the end-to-end ML pipeline. Data orchestration technologies abstract data access across storage systems, virtualize all the data and present the data via standardized APIs and a global namespace to data-driven applications. Data orchestration can already integrate with storage systems; machine learning frameworks only need to interact with a single data orchestration platform to access data from any connected storage. As a result, training can be done on all data from any source, leading to improved model quality. There is no need to move data to a central source manually. All computation frameworks, including Spark, Presto, PyTorch, and TensorFlow, can access the data without concern for where it reside. Key benefits of the platform included shared resources, elimination of silos, centralized access to data for better governance and security, and centralized and shareable environment management, enabling the company to operationalize data science at scale across the organization.

Deploying Models for Production

The Deploy phase is critical for operationalizing models at scale and is traditionally where the highest value is achieved from MLOps. However, many organizations still need help with the model deployment process, which can be time-consuming and require close oversight from IT support staff. An Enterprise MLOps platform can streamline the deployment and change management processes, allowing data scientists to deploy models independently without relying on IT or software developers. This can save time and add value to the business. Given the high levels of AI failure rates, companies need a structured way to manage their models for successful ML applications. Model registry, a tool designed to manage models systematically. A model registry makes collective action easier. Thanks to its centralized storage, the most up-to-date version of all models can be found. Thus, data scientists can avoid the risk of working on overlapping problems or falling into the same mistakes. Being informed of others’ actions both enable joint work and save time. A model registry makes the lifecycle of models transparent. This way, each team member can keep track of a model’s progress. A model registry facilitates model deployment in which models are pushed into production. Data scientists can streamline by tracking, monitoring, comparing, and searching all the models. A model registry can be confused with experiment tracking. However, even though experiment tracking allows tracking different versions of a model and storing training data, they serve different purposes. This allowed them to efficiently deliver customized models. 

Monitoring the Model Portfolio for Ongoing Performance

Monitoring is about keeping track of model performance, ensuring that models continuously learn, continually rebuild (CI/CD), and preventing model drift or even the improper use of models. ML model monitoring platform that can boost the observability of your project and helping you with troubleshooting production AI. Automatic model monitoring should be proactive rather than reactive so that you can identify performance degradation or prediction drifts early on. Automated monitoring systems can help you with that, and integrations with tools like PagerDuty or Slack can notify you in real time. It demands zero setups and provides space for easy-to-customize dashboards. While these objectives may seem obvious, many (if not most) enterprises that fail to scale models in production are falling short in the Monitor phase because they are disengaged with systematically ensuring model performance and business outcomes. Enterprise MLOps needs to integrate a strong model maintenance plan to implement monitoring at scale. The risks of ignoring the monitoring responsibilities pose real consequences from wrong models or their improper use – including significant monetary and brand reputation risks. Model maintenance should make it easy to trace the history of models and quickly reproduce them in follow-up experiments, tuning, and re-validation. They are improving their model monitoring capability. It infuses data science across its operations to provide consumers with a better, faster insurance experience. The company adopted Enterprise MLOps technology and practices to get insights into how models perform in real-time and detect data and model drift once models are in production. The new approach saves us significant time previously spent on maintenance and investigation and enables us to monitor model performance in real time and compare it to our expectations. In one case, they could automatically detect drift that had previously taken three months to identify manually.

Conclusion

In summary, MLOps or Machine Learning Operations is a concept that aims to improve the collaboration and automation of the entire data science lifecycle, from research and development to deployment and maintenance.

However, there is an essential enabling capability on the Dataops side of the house, which is modern tools for batch and streaming data ingestion and also advanced tools for data quality monitoring and tools for data transformation. The traditional definition of MLOps is limited to the back end, focusing on the deployment and maintenance of models. However, a more comprehensive definition of MLOps, known as Enterprise MLOps, applies to the entire data science lifecycle, including the front-end R&D phase and the back-end model creation and management. By addressing the challenges and obstacles in the R&D phase, such as silos, resources, governance, software, security, visibility and lineage, an Enterprise MLOps platform can help organizations to achieve scalability in data science and realize the ROI they hope for the business.

Generative AI marks a significant advancement in artificial intelligence, harnessing human creativity to reshape the insurance sector. Unlike traditional technologies, Gen AI doesn’t just refine the existing data; it generates innovative outputs without explicit programming.  

This technology opens doors to fully automated insurance processes. Picture this: a customer, seeking car insurance, interacts effortlessly with a Gen AI-driven chatbot. This AI gathers information, while an “Anonymizer” bot creates a digital twin devoid of personal identifiers. This enables insurers to swiftly tailor personalized quotes, simplifying the underwriting process. The claims process also undergoes a seismic shift with the Edge AI. Car sensors gauge impact and seamlessly relay that data to insurers, automating the backend. The customer need only make the simple decision to pursue the claim.  

This glimpse into the future emphasizes how Generative AI can reinvent insurance, offering a creative and efficient alternative to conventional methods. Gen AI promises transformative changes across the insurance value chain, enhancing operations with speed and precision. 

With the application of Gen AI, the future of insurance is poised for smarter, proactive action, leaving no room for delays and uncertainties. Now, let’s delve into the diverse applications, advantages, and considerations that insurers must navigate to succeed in the Gen AI landscape.  

Gen AI Applications are Reinventing the Insurance Value Chain 

In the dynamic industry landscape, staying competitive means harnessing the latest technologies. Gen AI stands at the forefront, reshaping the insurance value chain with its exciting capabilities. Let’s delve into how Gen AI applications are disrupting the insurance value chain:

Product Design and Development: 

Generative AI enables the analysis of vast volumes of customer data, empowering insurers to design and develop tailored products and meet customer needs rapidly.   

Sales, Marketing, and Broker Management: 

Gen AI facilitates the generation of deep insights into agency performance, empowering agents to optimize their strategies. Through personalized nudges, Gen AI enhances productivity and fosters stronger client relationships. 

Product Recommendations: 

Gen AI enables the delivery of highly customized cross-sell and upsell recommendations at the point of sale. By triangulating internal and external data, insurers can offer targeted suggestions that enhance customer satisfaction and drive revenue growth. 

Pricing and Underwriting: 

With Generative AI, insurers gain access to on-demand analysis and comprehensive risk assessments. By synthesizing data from various sources, insurers can make more informed pricing and underwriting decisions, leading to improved risk management. 

Contract Management: 

Gen AI streamlines contract management processes by summarizing and identifying key details. This ensures greater accuracy and efficiency in managing policies and agreements. 

Policy Administration: 

Gen AI provides smart recommendations for coverage enhancements and automates policy renewal and endorsement processes. This enhances operational efficiency and improves the overall customer experience. 

Claims Management:

Through Gen AI, insurers can generate detailed claim histories and proactively detect and prevent claim irregularities. This minimizes claim leakage, ensures compliance, and enhances customer satisfaction. Additionally, by analyzing patterns and detecting inconsistencies, insurers can mitigate fraud risks and safeguard the integrity of the insurance process. 

The Emergence of Vertical AI for Insurance  

Generative AI Continues to undergo rapid development, offering a myriad of enterprise opportunities. While many applications span industries, the insurance sector presents unique “vertical” use cases tailored to its intricacies, where AI can enhance human intelligence. Unlike “horizontal” applications, which are broadly applicable, these vertical use cases demand a deep understanding of industry nuances and targeted investments to refine models. Examples include: 

1. Tailored solutions for analyzing unstructured insurance data. 

2. Identifying risk patterns to inform underwriting decisions. 

3. Providing claimants with instant information upon filing a claim. 

The transformative power in insurance lies in integrating these diverse use cases into a comprehensive, scalable solution tailored to industry needs. This shift toward sector-specific capabilities demonstrates a commitment to crafting precise solutions for the insurance industry. 

Generative AI’s Promise for Property and Casualty (P&C) Insurers

Property and Casualty (P&C) insurers can harness the advantages of Generative AI to streamline claims processing and bolster risk management, reaping significant benefits across various domains. 

Generative AI promises to help reinvent the insurance landscape by enhancing decision-making for underwriting and claims professionals. It crafts concise reports that elevate decision quality, productivity, and efficiency. By delving into customer data through natural language processing, it facilitates tailored interactions, boosting revenue, satisfaction, and loyalty while curbing attrition. Moreover, it refines risk assessment and claims estimation by dissecting unstructured data, expediting operational tasks like rate filings, and even generating synthetic data. Its capabilities extend to content summarization, improving comprehension, aiding policyholders, and fueling marketing insights. 

In customer interactions, Generative AI empowers chatbots to engage customers more naturally, educating them on products, facilitating comparisons, and addressing queries. It tailors’ insurance quotes and claims recommendations to individual needs, fostering a personalized experience.

For customer-facing teams, Generative AI enables nuanced discussions tailored to each client, facilitating cross-selling and up-selling opportunities based on unique profiles. Internally, it seamlessly integrates backend systems with front-end interactions, enhancing efficiency. 

In operations, it augments marketing communications, automates documentation generation, and assists underwriters with risk assessments. It is integral in analyzing claims and properties and even helps with coding tasks. 

In product development, Generative AI offers competitive insights, supports IoT trend analysis for pricing models, and identifies consumer needs critical for ecosystem partnerships. 

Streamlined Claims Processing 

Gen AI can help to reinvent insurance by automating key steps in claims processing. Using cutting-edge natural language processing (NLP), it swiftly extracts essential information from claim documents, including policy details and incident descriptions. This automation accelerates claims handling and helps insurers address policyholders’ needs promptly. Gen AI also handles routine tasks like creating standard communications for claimants and drafting messages for external service providers. By freeing adjusters from these tasks, Gen AI allows them to focus on strategic efforts, improving their overall impact on the claims process. Ultimately, Gen AI’s automation boosts efficiency, streamlines operations, and enhances customer satisfaction by facilitating quick claim resolutions. 

Enhanced Loss Prevention and Control 

Generative AI plays a crucial role for P&C insurers in identifying and reducing risks, while also boosting workforce productivity and creating new revenue opportunities. By analyzing diverse data sources such as the Internet of Things, video, and text, alongside historical claims and external factors like weather patterns, Gen AI models facilitate the identification of areas prone to losses. This insight proves invaluable in developing effective risk mitigation strategies and plans, ranging from recommending safety improvements to suggesting policy adjustments that reduce the likelihood of future losses. 

Customer Interactions

The customer journey has evolved into a seamless omnichannel experience, with more remote interactions directly with insurance providers, particularly during claims. Gen AI virtual assistants can transform these interactions, though adoption rates vary across markets and companies. These assistants can elevate customer satisfaction, reduce wait times, and offer 24/7 support, thus enhancing the overall customer experience. Powered by Gen AI, intelligent chatbots or voice bots grant policyholders immediate access to assistance and information. They are accessible through websites, mobile apps, and messaging platforms, offering personalized support by understanding queries, furnishing updates on claims, and outlining coverage specifics. Furthermore, they can direct customers through the claims process, furnishing clear instructions and gathering necessary details for a smooth experience. 

Data-Driven Business Insights

Generative AI’s advanced capabilities empower insurers to extract valuable insights from the vast amount of data generated during insurance claims, identifying emerging trends, streamlining operations, and making data-driven decisions. This transformation occurs as insurers use Gen AI to convert unstructured data into actionable formats that seamlessly integrate with their systems. By analyzing customer data, insurers gain deeper insights into patterns and preferences, allowing them to tailor communications and provide personalized claims experience. Gen AI’s ability to identify patterns within claims documentation, like loss appraiser reports, is a game-changer for insurers. This helps pinpoint areas of risk concentration and refine feedback loops for underwriting and product design teams. For example, a European insurer recently used Gen AI to analyze thousands of historical loss appraisals from weather-related events, gaining valuable insights into correlations and cost drivers. This empowered the insurer to develop more effective claim-resolution strategies and refine policy underwriting terms. 

Generative AI’s Promise for Life and Annuity (L&A) Insurers  

Life and Annuity (L&A) insurers’ persistent misconceptions about Gen AI and slower adoption of emerging technologies have proven to be significant barriers to technology transformation. Additionally, the complexity of L&A products creates barriers to growth among the millennials who overestimate the cost of life insurance, and often abstain from obtaining life insurance due to misconceptions about eligibility or perceived lack of value. 

Product Personalization

Generative AI can analyze customer data and preferences, enabling insurers to recommend bespoke insurance products. By comprehending customers’ nuanced needs and risk profiles, insurers can provide personalized coverage options, thereby enhancing the potential for upselling or cross-selling additional policies.  

Agent Assistance

While many policyholders demand digital processes, some still value personalized support. However, a challenge emerges when in-person agents lack the tools to tailor insurance quotes effectively, creating hurdles for potential policyholders. But with Gen AI, agents can seamlessly interact with clients, fine-tune quotes, and track the entire process in real-time. This not only fosters transparency but also strengthens the bond between consumers and agents by offering a clear view throughout the purchasing journey. In major financial decisions like buying a home or a car aren’t everyday occurrences. They represent pivotal moments often associated with stress and confusion, especially when it comes to insurance. This is where Gen AI steps in, providing invaluable guidance and support to navigate these significant transactions much easier.

Optimized Underwriting and Pricing

Integrating AI into underwriting processes optimizes risk assessment and pricing by consolidating diverse datasets, reducing error susceptibility, and enhancing efficiency. This enables the implementation of predictive analytics models, algorithms, and machine learning, streamlining due diligence processes and saving time. Additionally, AI-assisted underwriting addresses pricing inconsistencies in commercial insurance, suggesting optimal pricing options and coverage terms based on risk visibility. As insurers embrace AI-driven underwriting, they can lower expenses, improve profitability, and position underwriters as strategic assets within their organizations.

One of the key contributions of Exavalu is in helping P&C insurance providers with Gen AI for risk assessment. Through our advanced data analytics capabilities, Exavalu has enabled insurance providers to analyze vast amounts of data in real time, allowing them to identify potential risks and assess their impact accurately. This has not only improved the accuracy of risk evaluation but also expedited the process, resulting in faster response times and improved underwriting decisions. 

Why Should Insurers Invest in Gen AI Vertical Use Cases?  

Insurers find themselves on the precipe of technological advancements, where embracing Generative AI goes beyond making a step forward to making a strategic leap towards accelerating growth and operational prowess. We summarize below why investing in Generative AI has become imperative for insurers: 

Profitability and Growth  

Judicious investments in Gen AI can empower insurers to discern untapped avenues for growth, elevate the quality of their product offerings, and broaden their market footprint. The realization of Gen AI’s potential to generate new revenue streams is exemplified in the technology sector, where offerings like Google Bard have already used advanced features to drive revolutionary shifts.  

Cost Savings and efficiency  

Consider another frontier, where Gen AI-driven solutions applied to content creation in low-risk contexts enable insurers to streamline expenditures across various functional domains. This targeted spending approach promises substantial cost savings and operational efficiencies, particularly in functions such as marketing, human resources, and legal processes.

Operational Intelligence and Effectiveness  

Insurers can derive immediate benefits by integrating Gen AI into autonomous coding, expediting the software development life cycle and diminishing training requirements. Recent advancements like the Code Interpreter for ChatGPT bring automation to document analysis and data visualization, contributing significantly to the operational prowess of sales and support teams. 

How Can Insurers Address Risks and Mitigate Them Effectively? 

While Generative AI holds significant promise, it also introduces potential risks that can impede adoption, if not carefully addressed during scaling efforts. Threats include malicious activities such as deep fakes and phishing that can jeopardize customer trust. The inherent tendency of Gen AI to replicate algorithmic biases and discriminatory behaviors demands the implementation of guardrails and continuous monitoring for ethical deployment. Training AI models on proprietary, internal insurance data necessitates compliance with regulations, node isolation, and traceability. Furthermore, excessive reliance on AI-driven automation in customer interactions within the insurance industry may compromise the essential human touch and judgment, potentially lowering customer satisfaction or causing compliance issues. Regulators have started increasing their oversight of the use of AI algorithms in decision-making, emphasizing the need for insurance companies to increase algorithmic transparency and effective management of AI risks.  

To mitigate these challenges, insurers must prioritize ethical AI practices, increase the deployment of diverse and unbiased training data, and establish robust governance models for consistent evaluation and auditing of AI-enabled decision-making models. Staying abreast of AI legislation, conducting regular surveillance, ensuring transparency in decision-making, and actively managing customer interactions are essential steps. Building organizational awareness of rapidly evolving regulations and involving experienced marketing and communications professionals can effectively manage brand risk during Gen AI implementations. 

Also, read our Whitepaper on How to Choose the right data warehousing platform for your data needs 

Conclusion  

To optimize Gen AI’s impact in the insurance sector, Insurance organizations must pivot from haphazard experimentation to a focused, strategic approach. This entails cultivating cross-disciplinary collaboration to fully grasp its potential and risks. Educating senior management on Gen AI fosters alignment and transparency, while forming a diverse stakeholder group that ensures well-informed decision-making. 

Prioritizing AI applications with tangible ROI and crafting a coherent technology strategy is foundational. Identifying competitive advantages, fostering proactive partnerships, and remaining aware of regulatory shifts can allow for seamless integration. 

Embedding generative AI can reinvent your insurance operations and customer interactions, bolstering competitiveness. Collaborating with various teams ensures a comprehensive evaluation of its utility and threats. Engaging software vendors for seamless integration aligns enhancements with business goals. 

Proactively working with risk management ensures robust governance, mitigating potential liabilities. Integrating Gen AI into data strategies under expert guidance unlocks its potential for informed decision-making. Strengthening data science capacities is pivotal for compliance, governance, and strategic implementation. 

Partner with Exavalu to Harness the Power of Generative AI  

From large insurance providers to mid-sized and small firms, Exavalu plays a pivotal role in modernizing operations across the insurance sector. With many decades of industry expertise, certified consultants, and state-of-the-art technology solutions, we have transformed risk assessment and customer experiences, and automated claims processing. It has enabled P&C insurance providers to stay ahead of the competition, drive growth, and deliver maximum value to their customers. Reach us to discover how you can step into a new era of efficiency and accuracy with Gen AI.  

About the Author: 

Rahul Chakladar is a Consulting Manager with Exavalu Data and Analytics Practice. He has more than 16 years of experience in Management and Strategy consulting. He has worked extensively across industries such as Insurance, Banking, Retail, and Consumer Packaged goods. You can reach him at Rahul.Chakladar@exavalu.com 

Underwriting plays a crucial role in the success of an insurance carrier by assessing and evaluating the risks associated with insuring individuals, businesses, and assets. Despite the significant progress made in digitization and access to prefill and third-party data, there is still ample room for innovation to realize straight-through processing and improve the underwriting process.  

Fortunately, integration of data and analytics capabilities into selling, underwriting and servicing customers are becoming mainstream in the insurance industry.  The top carriers are investing heavily and leading the pack by building advanced data and analytics underwriting capabilities that deliver substantial value. Leading insurers are improving upon loss ratios, generating healthy new business premiums and driving profitable growth by leveraging data-informed underwriting. We anticipate that carriers will increasingly use the power of data and analytics to proactively assess their portfolio underwriting practices—similar to what hedge funds do in predicting capital markets—and identify market opportunities ahead of competition. 

This article explores the power and promise of data-informed underwriting, examining the unique challenges that underwriters face and the latest data and analytics capabilities and innovations that insurers must adopt to fully transform the underwriting process and cater to the ever-changing needs of their customers. Join us on this journey to discover how these advancements are poised to disrupt the industry and create a brighter future for insurance underwriting. 

Underwriting Challenges Still Prevailing In The Industry 

In recent years, significant advancements in the use of third-party data and analytics models in underwriting have transformed the way insurers approach the process. Utilizing advanced analytics, insurers have been able to analyze vast amounts of data and incorporate a more insight-driven approach to assess and evaluate risks.  

Despite the notable advancements, challenges persist in the underwriting process, such as outdated technology, poor data quality, and a lack of inline analytics and models to augment underwriting decisions. Addressing these challenges is crucial to improving the accuracy and efficiency of underwriting and meeting the evolving needs of producers and policyholders.  

Organizing for success with data and analytics in underwriting 

Diverse internal and external data sources are available to serve as fuel for a new underwriting engine, and inline artificial intelligence–based models may unlock valuable new insights. Data is one of the most valuable assets insurers have, and predictive analytics has been helping businesses make the most of that data. However, most carriers are behind in their use of machine learning and cloud enabled analytics platforms that leverage modern data science and machine learning platforms.  From anticipating customer behavior to supporting underwriting straight-through processing, use of AI and Analytics have been enabling leading carriers who have seized the opportunity to leverage cloud and Machine Learning Operations (MLOps) to enable data-informed operations. 

1. Data Acquisition 

High underwriting expenses often stem from the efforts associated with obtaining customer and risk information through back-and-forth communication involving producers, customers, investigators, and third parties, among others. Streamlining data collection from multiple digital sources including internal enterprise systems and external data providers through pre-fill will improve the decision-making accuracy and bring down expenses drastically.  

For example, various Client interaction data can be gathered from multiple CRM systems, Loyalty management systems, Contract center systems etc., while Policy and claims related data can be gathered from Policy and Claim centers systems, Premium payments can be collected from billing systems etc. Insurers may also utilize external systems to gather relevant data for new business or renewals. For instance, environmental risks can be evaluated from satellite or drone imagery overlayed with weather events, such as floods. Meanwhile, internal operational risks can be assessed through data sharing on machinery breakdowns, accidents, and other unforeseen events.    

2. Data Management system 

Inaccurate or incomplete data caused by the lack of proper data management processes can lead to serious underwriting errors. This in turn negatively impacts Carrier’s growth, profitability and reputation. 

To prevent this from happening, carrier’s data strategy must include diverse ways of obtaining and securing access to internal data, and ways to combine this with internal sources to formulate insights and models that informs carrier’s underwriting practices. The starting point of this transformation needs to be the improvement of the data assimilation process through application of a Data Fabric capability to rationalize, unify, and enhance sources of data. The ability to identify valuable data sources and streamline the data collection will eliminate the otherwise costly affair of going back and forth between different stakeholders to obtain customer and risk-related information.   

To address these challenges, insurers must adopt effective data management techniques and systems, such as data ingestion, transformation, and integration capability, and usage of centralized data stores like Data Lake and Data Warehousing systems. Also, the adoption of data quality management, Data Governance, and Master data management plays a key role empowering experimentation and model development at scale. A cloud based big data capability to ingest, manage and govern enterprise data in cloud-based data lakes can be a right fit solutions for today’s enterprise. Data lakes serve as massive storage reservoirs that house vast quantities of raw data in their native form, encompassing unstructured, semi-structured, and structured data types. The data structure and requirements remain undefined until the data is needed. Together, these systems enable the carrier to provide data where it is needed, from inline insights for decision support to descriptive, predictive and prescriptive analytics. 

3. Data Governance and DataOps capability 

To help utilize the raw data that arrives from sources in a data lake, organizations use a governed data lake that houses structured and unstructured data and also create trusted, secured and governed data layers for consumptions. Managed data lakes provide possibilities to discover, comprehend, exchange, and confidently take action based on that information. Data without context lacks meaning. Data governance is a principle that ensures data is secure, private, accurate, recent, and usable. It allows setting up internal standards—data policies—that govern how data is gathered, stored, processed, and disposed of. It determines the access permissions for different types of data and establishes governance over which data falls under its purview. This certifies that the enterprise data is fit for consumption for Analytical and business decision support.  

Inspired by the DevOps concept, the DataOps strategy strives to speed the production of applications running on big data processing frameworks. The objective is to guarantee that an organization’s data is utilized in the most adaptable and efficient way to achieve favorable and dependable business results. DataOps spans a number of technology disciplines such as data extraction, data ingestion & transformation, data quality, data governance, access control, data center capacity planning and system operations. DataOps-enabled tools foster teamwork, coordination, data integrity, security, accessibility, and user-friendliness. 

4. Adopt Master Data Management (MDM) Solution 

MDM is the process of creating and maintaining a single, authoritative source of Master data for an organization. Insurers should deploy multi-Domain MDM system to ensure accurate and unique Customer, Agent, Insured Assets and Claims master data is available for further consumption. Customer data is spread across multiple enterprise systems today, prohibiting Insures to obtain a Unified View. MDM systems provide the ability to Consolidate, Standardize and Uniquely Identify key Master data and their relationship like Customers, Assets, Claims or Agents for further downstream systems to consume. The unique Gold copy data is then utilized by Enterprise systems to streamline its operations such as point-of-sale cross sell, customer services, claims intake and analytical use cases.  

The Multi-Domain MDM system also adds the capability to group individual policies and accounts within a household or family that allow Insures to identify patterns and trends that are not otherwise visible at the individual policy level while also serving customer preferences for omni-channel communication, billing and notices. Data privacy and security can also be addressed. Improving customer insights through a deeper understanding of their behaviors, preferences, and needs can help underwriter to make more informed decisions, offer more targeted and personalized products and services, reduce risk, and enhance customer satisfaction.  

5. Establish Best-fit Enterprise Data model to enable Analytics! 

Data holds immense value for insurance companies, and leveraging analytics can unlock significant business benefits. However, harnessing the full potential of data requires robust data modeling capabilities. An Enterprise Data Model offers a comprehensive perspective on the data generated and used within an organization. It provides a unified and unbiased representation of data, and their interrelationships, independent of specific source systems or applications. The model presents a holistic view of the data relevant to the business and the governing rules associated with them.  

Analytics is generated from data that is already organized through data processing as defined in the data model. There are several categories of Analytics, one such category is Descriptive Analytics which is generated from organized modelled data in data warehouse or data marts that supports everyday Business intelligence reporting and decision making. There are other categories of Analytics like Predictive or Prescriptive analytics that is generated from past or real time data (structured or unstructured) through Machine learning algorithms to generate additional business insights. Embedded Analytics is also gaining popularity which is generated while ingesting real time streaming data. This can be used to trigger real-time campaigns or next best offers for the target customers. 

6. Artificial Intelligence (AI), Machine Learning (ML) and MLOps 

At its core, AI/ML utilizes algorithms and statistical models to enable machines to learn and make decisions on their own without human intervention or augment human judgement with additional insights for better decision making. Analytics, Artificial Intelligence and Machine learning Solutions thrive on Data. In other words, modern data eco-systems that generate quality enterprise data can improve effectiveness of AI and Analytics solutions. It helps identify patterns and anomalies in vast amounts of data, providing valuable insights that may be missed by human analysts. 

Though ML technology is being applied to solve a variety of issues with great success, most data scientists hit bottlenecks while deploying ML model. Research shows that lack of structure and formalized processes around ML lifecycle is responsible for that. This is where MLOps comes into play. Inspired by the DevOps concept, MLOps offers a collection of practices that establish a structured and segmented approach to deploying, monitoring, and retraining machine learning models. It helps to improve the quality of production models, while incorporating business and regulatory requirements and model governance. It provides a framework for managing the ML lifecycle effectively by matching business expertise with technical knowhow, through iterative workflows. Machine learning is a relatively young field, and regulatory bodies consistently modify their requirements and revise their guidelines. MLOps takes ownership of staying in compliance with shifting regulations, as prevalent in the financial service industry.  

7. Cloud Computing trends in Insurance 

Cloud Computing and Big data Analytics in Insurance is rapidly becoming mainstream as it has realized the benefit over the last few years. The Insurance sector is highly competitive and regulated, and the carriers need to quickly react to the changes in the market by offering new services and products. 

New capabilities, business features, and products can be more quickly developed, tested, and launched in a cloud than in traditional environments. This advantage is especially beneficial for those who are quick to adapt, as they can swiftly respond to market changes. Cloud technologies and tools enable better asset utilization and more-flexible operating models. Advanced cloud capabilities allow companies to generate insights that previously demanded intensive resources to develop.  To achieve successful cloud transformations, it’s crucial to select and train employees to consider operational expenses when evaluating cloud costs. Collaborating with teams to foster innovation and cultivating cloud champions are vital in helping the entire organization grasp the business advantages of the cloud. 

How To Start Optimizing Underwriting For Success:  

Progressive underwriting organizations are empowered by Predictive Analytics, Artificial intelligence and digital capabilities that allows them to scale, improve sales and be profitable. These Underwriters have significant leverage through interactive tools and data-driven insights, allowing them to handle substantially larger books of business with more precision and control. They are able to use data throughout the underwriting process to inform underwriter decisions in prioritization of prospects, identification of risks, policy structuring, and pricing. They can get full control of continuously evolving risk models that incorporate ever-expanding views of risk characteristics, tailored by line, segment, and emerging-loss trends. 

The success of an underwriting platform will depend on real-time availability of relevant data from the ecosystem, scalability of the platform to access new data sources, and maturity of risk-monitoring models to generate relevant insights for underwriters. Data can be collected from telematics, agent interactions, customer interactions, smart homes, satellite imagery, hazard databases and  social media to better understand and manage their relationships, claims, and underwriting. This data can be utilized to build next-generation analytical solutions that augment underwriting decisions in real time to prevent material losses and promote straight-through process efficiencies. 

1. Overreliance On Outdated Data & Pricing Models 

The traditional underwriting processes rely on manual work and outdated data management capabilities, which leads to time-consuming and inefficient practices. Moreover, the industry’s overreliance on outdated data and pricing models that don’t reflect current market conditions, or the unique risks associated with specific policyholders or assets accurately can result in underwriting errors that lead to policyholders being underinsured or over insured, negatively impacting policyholder experience.  

Thus, insurance carriers must adopt new risk models that incorporate a broader range of data sources, including real-time data and advanced analytics, to improve their risk assessments and pricing decisions. They largely utilize data directly from sources that are current and relevant. Data and feedback collected from social media, smart devices, weather data and interactions between claims specialists and customers is straight from the sources.  

2. Predicting Customers At Risk 

Predictive analytics can help carriers to identify customers who are at risk of cancelling their policy or not paying their premiums. More advanced data insights from contact center data or other third-party data sources may help insurers identify customers who are likely to churn. Having this knowledge will give carriers an advantage, enabling them to proactively address potential issues and provide personalized attention. Without predictive analytics, insurers risk overlooking important warning signs and wasting valuable time resolving problems. 

3. Identifying Risk of Fraud 

Despite battling various instances of fraud, insurers often remain unsuccessful. The Coalition of Insurance Fraud estimates that $80 billion is lost annually from fraudulent claims in the United States alone. Additionally, fraud makes up 5-10% of claims costs for insurers in North America. 

With predictive analytics, carriers can proactively detect and prevent fraud or take corrective action afterward. Insurers often utilize social media to uncover signs of fraudulent behavior by monitoring insured individuals’ online activities after a claim is resolved.  Insurers are also relying on predictive modeling for fraud detection. “Where humans fail, big data and predictive modeling can identify mismatches between the insured party, third parties involved in the claim (e.g. repair shops) and even the insured party’s social medial accounts and online activity,”. 

By continuously collecting and analyzing data, risk categories can be swiftly reclassified based on newly available information. This alters the probability and impact of risk factors in the current underwriting model. With real-time assessment of dynamic economic scenarios, timely recommendations for risk mitigation can be communicated to customers for necessary corrective action. 

4. Predicting Future Claims 

Insurers can identify claims that may become unexpectedly high-cost losses in the future. By employing effective predictive analytics, P&C insurers can examine past claims for similarities and proactively notify claims specialists. This early alert system allows insurers to reduce outlier claims by anticipating potential losses or related complications. Moreover, insurers can proactively utilize insights gained from outlier claim data to develop strategies for handling similar claims in the future. 

Advancements in analyticshave also helped transforming the claims process. A systematic approach makes it possible to propose risk mitigation measures and convert certain risks into acceptable categories. It can help insurers avoid unfavorable risks in certain cases. 

5. 360-degree view of Customers 

Insures can obtain a 360-degree view of customers by aggregating data from the policy systems and other touch point systems that a customer uses to contact the company to purchase products and receive services and support. The Master data Management solution creates a single view of customers, agents and assets and their relevant data attributes and relationships into the data management system. Customer data is utilized to accurately generate new insights that provide a more complete picture of a customer, like their contact preferences, relationship with the company, house-holding, cross sell opportunities, buying propensity, risk profile and others.  

Customers today value a customized experience.  Creating a foundation to know your customers and their relationships with the carrier enables opportunities to serve them better.  This will provide a competitive advantage that saves carriers time, money, and resources, while helping customers feel connected to the carrier’s product, service and claim experience.  

Case Study: Enhancing Underwriting For Large Worker’s Compensation Insurers With Improved Scoring Engine 

When a large US-based worker’s compensation insurer wanted to enhance its underwriting operation, it partnered with Exavalu for expert consulting and implementation services. Our team leveraged advanced analytics and machine learning to deliver a custom scoring engine, accelerating the underwriting decision-making process for our client.    

Riddled with the challenges of manual risk assessment and score generation process, the USA-based worker’s compensation insurer reached out to Exavalu for a new automated scoring engine that will-  

• Eliminate manual risk assessment processes,   
• Accelerate underwriting risk score generation with improved accuracy,  

Our client also needed the risk scoring engine to be integrated with Guidewire PolicyCenter, for a real-time risk generation process. 

Our Response To Client’s Requirements 

Exavalu leveraged our deep insurance domain and Guidewire integration expertise to help our client transform the risk assessment and score generation process for underwriting. We started by integrating Guidewire PolicyCenter and other sub-systems with cloud-based models developed using Python and Experian integration. Our team also developed APIs using MuleSoft to automate the underwriter risk assessment and implemented a reusable caching mechanism for third-party information retrieval and cost reduction. 

Successful Implementation of A Real-time Scoring Engine Boosts Underwriting Efficiency And Risk Management For Client 

Through our innovative approach, we were able to fulfill our client’s requirements and significantly enhance their straight-through processing and risk avoidance capabilities. The implementation of our new scoring engine greatly improved the response time for risk assessment and score generation to just 2-3 seconds, resulting in a much more efficient and effective risk management system. Our solution also resulted in a higher availability of their risk-scoring solution and operational dashboard, which effectively improved their business visibility.  

Conclusion 

The insurance industry is on the cusp of a major transformation, thanks to the integration of advanced Analytics, enablement of a data fabric and abundance of internal and external data for analysis and model development to support the underwriting process. This shift presents a unique opportunity for insurers to leverage cutting-edge solutions and optimize data management practices to enhance risk assessment capabilities, streamline underwriting operations, improve conversions and cross sell, and provide more personalized coverage to customers. By embracing innovative technologies such as machine learning, artificial intelligence, and predictive modeling, insurers can gain a competitive edge in a crowded marketplace while improving operational efficiency and reducing costs. As the insurance landscape continues to evolve, those who are quick to adopt these transformative solutions will be the ones who thrive in the future of insurance.