Transforming Insurance Finance with GenAI

It can analyze a wide range of financial market data, policyholder information, and macroeconomic factors to identify potential risks and opportunities for hedging against adverse financial events. This advanced approach, integrating real-time data from sources like health wearables, keeps insurers abreast of evolving trends. The Generative AI’s self-learning capability guarantees continuous improvement in predictive accuracy.

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Innovation Strategy and Delivery

After exploring various use cases of GAI in the insurance industry, let’s delve into four inspiring success stories from global companies. Higher use of GenAI means potential increased risks and the need for enhanced governance. Insurers can improve outcomes if they also optimize their existing processes. Typically, underwriters must comb through massive amounts of paperwork to iron out policy terms and make an informed decision about whether to underwrite an insurance policy at all. Your request is being reviewed so we can align you to the best resources on our team. In the meantime, we invite you to explore some of our latest insights below.

Even as cutting-edge technology aims to improve the insurance customer experience, most respondents (70%) said they still prefer to interact with a human. Bain’s analysis also pinpoints key risk areas emerging from insurers’ developing use of generative AI including hallucination, data provenance, misinformation, toxicity, and intellectual property ownership. Insurance is a complex, regulated business built around data, IT, and people. Chris Freese argues that, by unlocking the potential of all three, generative AI promises a transformation that has eluded the sector for years. Select the first use cases to pursue by considering ease of implementation, data availability, and potential benefits.

This not only refines underwriting decisions but also allows for personalized coverage options. In 2022, a staggering 22% of customers have voiced dissatisfaction with their P&C insurance providers. The American Customer Satisfaction Index (ACSI) reveals a pressing need for improvement, especially in areas like the availability of discounts, speed of claims processing, and clarity of billing statements. The insurance industry, on the other hand, presents unique sector-specific—and highly sustainable—value-creation opportunities, referred to as “vertical” use cases. These opportunities require deep domain knowledge, contextual understanding, expertise, and the potential need to fine-tune existing models or invest in building special purpose models.

We work in a uniquely collaborative model across the firm and throughout all levels of the client organization, fueled by the goal of helping our clients thrive and enabling them to make the world a better place. By simulating various market scenarios, GenAI helps insurers make informed decisions about their investment portfolios and risk mitigation efforts. This enables companies to optimize their hedging strategies, manage their exposure to market fluctuations, and enhance their financial stability. An insurer should start with use cases where risk can be managed within existing regulations, and that include human oversight.

Generative AI automates routine insurance tasks, enhancing efficiency and accuracy. It streamlines policy renewals and application processing, reducing manual workload. Consequently, it frees staff to focus on more strategic, customer-centric duties. It actively identifies risk patterns and subtle anomalies, providing a comprehensive overview often missed in manual underwriting. This way companies mitigate risks more effectively, enhancing their economic stability. Artificial intelligence adoption has also expedited the process, ensuring swift policy approvals.

Imagine a scenario where a customer, post-accident, uploads images and details of their damaged vehicle. To learn next steps your insurance organization should take when considering generative AI, download the full report. To move beyond experimenting with GenAI in finance functions, companies across industries must manage critical challenges to implement the technology at scale. These include data accuracy and security as well as hallucinations; GenAI tools can struggle with calculations, for example, and sometimes produce incorrect responses in a highly convincing manner.

Why do customers distrust AI and ChatGPT?

The three lines of defense and cross-functional teams should feature prominently in the AI/ML risk management approach, with clearly defined accountability for specific areas. The business and the risk teams will need to embrace agile work methods in actively assessing risks, operationalizing controls and prioritizing their reviews based on the most common and highest risk Chat PG use cases. New talent and expertise in specific areas (e.g., prompt engineering) will be necessary to address all types of GenAI- related risks. To drive better business outcomes, insurers must effectively integrate generative AI into their existing technology infrastructure and processes. Generative AI is a tool within a broader set of techniques and technologies.

The real game changer for the insurance industry will likely be bringing disparate generative AI use cases together to build a holistic, seamless, end-to-end solution at scale. Insurance companies are increasingly keen to explore the benefits of generative artificial intelligence (AI) tools like ChatGPT for their businesses. It is thus crucial to choose external partners that can provide support across strategic, business, technological, and operational topics. Strategic partners can offer insights into how GenAI can align with and advance the company’s long-term vision and mission. The broader the data set available for training AI, the better the results it delivers. However, data in multinational insurance companies is often fragmented across multiple systems and departments in different countries.

• BCG’s insurance excellence benchmarking suggests that GenAI can help insurers capture efficiency improvements of 10% to 20% in the overall finance function.
• Equally important is the need to ensure that these AI systems are transparent and user-friendly, fostering a comfortable transition while maintaining security and compliance for all clients.
• Such an enhancement is a key step in Helvetia’s strategy to improve digital communication and make access to product data more convenient.
• Insurers can improve outcomes if they also optimize their existing processes.
• These models can then evaluate new claims, pinpointing those with a high likelihood of fraudulence.

Amidst this backdrop, Generative AI for insurance emerges as a beacon of hope. Beyond its prowess in crafting content, Generative AI, powered by models like GPT 3.5 and GPT 4, offers a transformative approach to insurance operations. It promises not only to automate tasks but also to elevate customer experiences and expedite claims. Ultimately, insurance companies still need human oversight on AI-generated text – whether that’s for policy quotes or customer service. Across 65 cities in 40 countries, we work alongside our clients as one team with a shared ambition to achieve extraordinary results, outperform the competition, and redefine industries. We complement our tailored, integrated expertise with a vibrant ecosystem of digital innovators to deliver better, faster, and more enduring outcomes.

We earned a platinum rating from EcoVadis, the leading platform for environmental, social, and ethical performance ratings for global supply chains, putting us in the top 1% of all companies. Since our founding in 1973, we have measured our success by the success of our clients, and we proudly maintain the highest level of client advocacy in the industry. Actuarial models are highly complex, involving intricate mathematical and statistical calculations to estimate financial risks related to uncertain future events.

Generative AI in Insurance: Market Overview and Growth Predictions

Essentially, Generative AI generates responses to prompts by identifying patterns in existing data across various domains, using domain-specific LLMs. In an age where data privacy is paramount, Generative AI offers a solution for customer profiling without compromising on confidentiality. It can create synthetic customer profiles, aiding in the development and testing of models for customer segmentation, behavior prediction, and targeted marketing, all while adhering to stringent privacy standards. A McKinsey report titled “The economic potential of generative AI” sheds light on the transformative potential of this technology in customer service. The report estimates that Generative AI could slash the volume of human-serviced interactions by a staggering 50%.

Generative AI can also create detailed descriptions for Insurance products offered by the company — these can be then used on the company’s marketing materials, website and product brochures. “Often, if anything in that data set is wrong, incorrect, or misleading, the customer is going to get frustrated. We feel like we spend an hour getting nowhere,” said Rik Chomko, CEO of InRule Technology. Younger generations are also more likely to believe AI automation helps yield stronger privacy and security through stricter compliance (40% of Gen Z, compared to 12% of Boomers).

As a result, the underwriting process will be much more thorough, and overall claims costs will be lower. Plus, underwriters will be able to work more efficiently by processing applications faster and with fewer errors, which, in turn, can lead to higher customer satisfaction ratings. Generative AI is rapidly transforming the US insurance industry by offering a multitude of applications that enhance efficiency, operations, and customer experience. Generative AI, a subset of artificial intelligence, primarily utilizes Large Language Models (LLMs) and machine learning (ML) techniques. Although the foundations of AI were laid in the 1950s, modern Generative AI has evolved significantly from those early days. Machine learning, itself a subfield of AI, involves computers analyzing vast amounts of data to extract insights and make predictions.

Because generative AI carries potential risks, such as bias, human oversight plays a key role in its responsible deployment. Customer preparedness involves not only awareness of Generative AI’s capabilities but also trust in its ability to handle sensitive data and processes with accuracy and discretion. Surveys indicate mixed feelings; while some clients appreciate the increased efficiency and personalized services enabled by AI, others express concerns about privacy and the impersonal nature of automated interactions. Insurance companies can also use Generative AI to serve existing customers with personalized products and services. For example, you can develop a Conversational AI platform powered by Generative AI to answer specific, customer inquiries and questions about policy coverage and terms. At the end of the day, it’s impossible to list all of the potential use cases for Generative Artificial Intelligence & ChatGPT in the insurance industry since the technology is always evolving.

Contact us to learn how Aon’s analytics capabilities helps organizations make better workforce decisions. For instance, Emotyx uses CCTV cameras to analyze walk-in customer data, capturing details like age, dressing style, and purchase habits. It also detects emotions, creating comprehensive profiles and heat maps to highlight store hotspots, providing businesses with real-time insights into customer behavior and demographics. By analyzing specific customer data points, such as age, health history, and location, these models can craft policies that align perfectly with individual circumstances.

To enable actuarial applications of AI, insurers must convert complex actuarial rules to simple algorithms. Addressing this challenge requires significant engineering efforts to codify approaches and methodologies closely guarded by the most skilled actuaries. By overcoming the obstacles, insurers can develop innovations that enhance their risk-management techniques. GenAI’s data analytics capabilities go beyond traditional analysis techniques. By analyzing large and complex datasets, generative AI can uncover nonlinear relationships and dependencies that may impact sensitivity analyses. Generative AI in life insurance opens new avenues for enhancing customer support, as demonstrated by MetLife’s innovative application.

Moreover, Generative AI’s prowess in simulating varied risk scenarios is invaluable. By drawing from past customer data, these models can generate potential future scenarios, aiding in better risk estimation and premium determination. By analyzing historical data and discerning patterns, these models can predict risks with enhanced precision.

To ensure that the organization adheres to the strategy, implement supporting technology and foster a communicative, value-based culture that embraces the importance of responsible AI. Multinational insurance companies must ensure that GenAI applications are customized to enable compliance with a plethora of accounting standards. IFRS 17 is the main global standard for insurance contract accounting, and IFRS 9 regulates the accounting of the financial assets held on balance sheets. In addition, insurers must adhere to country-specific Generally Accepted Accounting Principles for public reporting. Other relevant standards are capital requirements found in Solvency II in Europe and local risk frameworks across other jurisdictions. GenAI can aid actuarial reserving processes by enhancing the accuracy of loss-reserve estimates.

Bain & Company is a global consultancy that helps the world’s most ambitious change makers define the future. GenAI systems must be flexible enough to switch between these standards, ensure correct application, and remain up to date with evolving standards and interpretations. Within the industry, usage of the technology indicates three main objectives. For more, check out our article on the 5 technologies improving fraud detection in insurance. This article delves into the synergy between Generative AI and insurance, explaining how it can be effectively utilized to transform the industry.

Anthem Inc. partnered with Google Cloud to create a synthetic data platform. Their strategy involves generating an immense 1.5 to 2 petabytes of information. The records will encompass AI-generated medical histories and healthcare claims. The aim is to refine and train artificial intelligence algorithms on these extensive datasets, while also addressing privacy concerns around personal details. Such hyper-personalization goes beyond convenience, building trust and loyalty among customers. Insurers, by showing a deep understanding of individual needs, strengthen their relationships with the audience.

At Aisera, we’ve created tools tailored to enterprises, including insurance companies. We offer products such as virtual assistants, personalized policy recommendations, claims automation, dynamic forms, workflow automation, streamlined onboarding, live AI agent assistance, and more. By harnessing Generative AI-driven customer analytics, insurers gain profound insights into customer behaviors, prevailing market trends, and nascent risks. https://chat.openai.com/ This data-centric approach equips insurance companies with the tools to craft innovative services and products, precisely aligned with the dynamic needs and preferences of their clientele. In doing so, they not only address immediate customer requirements but also secure a formidable competitive edge in the market. Moreover, it’s proving to be useful in enhancing efficiency, especially in summarizing vast data during claims processing.

Generative AI in insurance

What are your thoughts on InRule Technology’s findings about customers and ChatGPT? “Generally, it’s a frustrating experience to interact with chatbots,” Shayman said. They could run a rough semantic search over some existing documentation and pull out some answers. InRule’s survey, conducted with PR firm PAN Communications through Dynata, found striking generation differences between customer attitudes towards AI.

Most LLMs are built on third-party data streams, meaning insurers may be affected by external data breaches. They may also face significant risks when they use their own data — including personally identifiable information (PII) — to adapt or fine-tune LLMs. Cyber risk, including adversarial prompt engineering, could cause the loss of training data and even a trained LLM model. EY refers to the global organization, and may refer to one or more, of the member firms of Ernst & Young Global Limited, each of which is a separate legal entity.

It can analyze vast financial datasets for inconsistencies, errors, and discrepancies, allowing for prompt corrective actions. Additionally, GenAI can fill in missing data, standardize formats, and verify the accuracy of financial records, which is crucial for reliable financial reporting and compliance with regulatory standards. By consistently monitoring data quality and implementing corrective measures, generative AI promotes more informed decision making, reduces operational risks, and improves overall financial performance. GenAI can support investor presentation processes by automating the generation of data-driven reports and presentations for shareholders and potential investors. It can analyze financial and operational data, extract key insights, and create visually compelling and easily understandable presentations.

More comprehensive coverage for the insured and heightened customer satisfaction. Boston Consulting Group partners with leaders in business and society to tackle their most important challenges and capture their greatest opportunities. Communicating the potential improvements helps mobilize the organization and underscores the importance of the transformation. Off-the-shelf tools and best practices tailored to insurance finance functions are available to support the evaluation. GenAI can automate transaction processing tasks, such as data entry, reconciliation, and verification.

Develop risk-based controls to promote innovation and speed to market

Aaron has experience across a range of industries, including food and beverage, life sciences, manufacturing, retail, technology and utilities. Helvetia has become the first to use Gen AI technology to launch a direct customer contact service. Powered by GPT-4, it now offers advanced 24/7 client assistance in multiple languages.

By partnering with us, you can elevate your claim processing capabilities and bolster your defenses against fraud. Generative AI is not just the future – it’s a present opportunity to transform your business. This AI-enhanced assistant efficiently handles queries about are insurance coverage clients prepared for generative ai? insurance and pensions. Bot’s integration of Generative AI improves accuracy and accessibility in consumer interactions. Such an enhancement is a key step in Helvetia’s strategy to improve digital communication and make access to product data more convenient.

In this webcast, EY US and Microsoft leaders discuss how generative AI can fundamentally reshape the insurance industry, from underwriting and risk assessment, to claims processing and customer service. Generative AI affects the insurance industry by driving efficiency, reducing operational costs, and improving customer engagement. It allows for the automation of routine tasks, provides sophisticated data analysis for better decision-making, and introduces innovative ways to interact with customers. This technology is set to significantly impact the industry by transforming traditional business models and creating new opportunities for growth and customer service excellence. GenAI can bolster data quality controls in finance by automating data validation, cleansing, and enrichment processes.

Generative AI models can be employed to streamline the often complex process of claims management in an insurance business. They can generate automated responses for basic claim inquiries, accelerating the overall claim settlement process and shortening the time of processing insurance claims. Generative AI can be used to simulate different risk scenarios based on historical data and calculate the premium accordingly. For example, by learning from previous customer data, generative models can produce simulations of potential future customer data and their potential risks. These simulations can be used to train predictive models to better estimate risk and set insurance premiums. If you’re an insurance company looking to leverage AI for insurance, you’ve come to the right place.

One common objective of first-generation deployments is using GenAI to take advantage of insurers’ vast data holdings. Generative AI can generate examples of fraudulent and non-fraudulent claims which can be used to train machine learning models to detect fraud. These models can predict if a new claim has a high chance of being fraudulent, thereby saving the company money. Generative AI can analyze existing customer data and create synthetic data from the existing data, which can be particularly useful when there’s a lack of certain types of data for modeling. Whatever industry you’re in, we have the tools you need to take your business to the next level. However, companies that use AI to automate time-consuming, mundane tasks will get ahead faster.

In this article, we delve into the reasons behind this synergy and explain how Generative AI can be effectively utilized in insurance. Our Technology Collection provides access to the latest insights from Aon’s thought leaders on navigating the evolving risks and opportunities of technology. Reach out to the team to learn how we can help you use technology to make better decisions for the future. The insurance market’s understanding of generative AI-related risk is in a nascent stage. Insurance policies can potentially address artificial intelligence risk through affirmative coverage, specific exclusions, or by remaining silent, which creates ambiguity.