Smarter processing: AI taking the lead in insurance innovation

October 2025 | SPOTLIGHT | SECTOR ANALYSIS

Financier Worldwide Magazine

October 2025 Issue

The insurance industry has been increasingly implementing artificial intelligence (AI), which has transformed traditional processes, providing benefits ranging from operational efficiency to enhanced customer experience and satisfaction.

According to a 2024 KPMG survey of insurance company chief executives, 81 percent consider generative AI (genAI) as a top priority investment for their organisation.

With new transformative technologies comes significant risk and concerns, particularly around unintended discrimination or bias and privacy and security concerns regarding data collection.

The KPMG survey also found that 86 percent of chief executives are concerned about the ethical implications of AI, while nearly three-quarters were apprehensive about security and compliance issues amid increasing regulatory scrutiny of new technologies.

Despite these challenges, the potential benefits of AI in insurance are already being realised. AI can help insurers better serve their customers, manage risks more effectively and reduce operational costs, ultimately leading to a more efficient and accessible insurance market.

Analysis by McKinsey predicts that AI will “increase productivity in insurance processes and reduce operational expenses by up to 40% by 2030”. The future of insurance hinges on leveraging AI responsibly while addressing the associated risks effectively.

This article discusses the implementation of current use cases of AI (both traditional and generative) in the insurance industry.

Underwriting automation and risk assessment

AI is helping transform insurance operations through automated underwriting and advanced risk assessment. These technologies are improving operational efficiency and the quality of decision-making processes.

Allianz has implemented intelligent chatbots capable of addressing queries to Allianz underwriters and employees regarding risk appetite and underwriting. These chatbots are available 24/7 and can be accessed in multiple languages. These chatbots utilise generative AI to summarise key exposures, significantly improving the risk assessment process by leveraging extensive databases and cited sources for accuracy.

Similarly, Swiss Re has introduced the Swiss Re Life Guide Scout, a genAI assistant designed to expedite risk assessments. This tool allows underwriters to ask questions in natural language and receive AI-generated insights quickly, facilitating faster and more informed decisions. By integrating Microsoft Azure OpenAI Service, Swiss Re enhances its underwriting manual with curated expert knowledge, streamlining the information retrieval process for underwriters.

Daido Life Insurance has developed a predictive AI model that automates preliminary assessments for medical claims underwriting. This system analyses applicants’ medical records and exam results while visualising its decision-making process, allowing human underwriters to verify results efficiently.

Fraud detection

Insurance companies are using AI tools to help identify potentially fraudulent claims by analysing patterns in claims data. These systems examine large datasets to spot signs that may indicate fraudulent activity, allowing humans to investigate suspicious cases more thoroughly.

By learning from past claims data, AI and machine learning (ML) systems continue to refine their ability to detect new types of fraud, helping insurers protect themselves and their customers.

PassportCard, a travel insurer, utilises AI to understand each customer’s profile and then flag unusual behaviours for staff to review across more than 70 administrative processes. This proactive approach enables swift identification of potential fraud, streamlining operations and improving response times.

Anthem Inc., in collaboration with Google Cloud, is developing a synthetic data platform that will generate approximately 1.5 to 2 petabytes of synthetic data, including medical histories and healthcare claims. This innovative platform employs algorithms and statistical models to train AI systems to detect fraudulent claims and abnormalities in health records. By utilising synthetic data, Anthem aims to enhance its fraud detection capabilities while maintaining compliance with privacy regulations.

Claims processing

The integration of AI in claims processing is helping insurance companies not only improve operational efficiency but also increase claim accuracy and boost customer satisfaction.

Companies like PassportCard leverage AI to automate claims payments, utilising a vast medical database to set financial boundaries for services. This allows them to automatically process approximately 95 percent of their $250m in annual claims, with only 5 percent requiring human review.

Similarly, Lemonade employs its AI assistant, Jim, to expedite claims handling. In 2019, Jim processed nearly 20,000 claims with a payout of about $2.5m, all without human intervention. Customers can submit video claims via a mobile app, enabling rapid processing that has set records; one claim was settled in just two seconds.

Allianz has also embraced AI with Neptune, a tool which automates the assignment of claims to adjusters based on real-time workload data. This innovation replaces the previous manual assignment process, significantly improving operational efficiency and allowing for better resource management.

Customer service and chatbots

AI chatbots are becoming a valuable tool for customer service in the insurance industry, offering 24/7 support and handling routine inquiries. These digital assistants help insurance companies streamline their operations while providing customers with quick, personalised responses to common questions and policy-related matters.

Allstate has introduced the Allstate Business Insurance Expert (ABIE), an AI-driven chatbot designed to assist small business owners with their insurance needs. ABIE streamlines the purchasing process, achieving a containment rate of 38 to 40 percent, meaning it successfully handles a significant portion of inquiries without human intervention.

Additionally, it correctly understands user intent 80 percent of the time, ensuring relevant and timely responses to customer queries. Similarly, Zurich Insurance employs Zara, its chatbot, to facilitate claims reporting and answer common questions. Zara has achieved a transactional net promoter score of 80, indicating high customer satisfaction, and has reduced claims processing time by approximately 30 percent.

Personalised policy pricing and actuarial analysis

Insurance companies are also using AI to tailor their policy offering to individual customer needs. Furthermore, insurers are adopting dynamic pricing models powered by AI which adjust premiums in real-time based on behaviours, such as driving habits or health metrics. By enhancing predictive accuracy and other actuarial tools, companies can develop more accurate and personalised pricing strategies.

AXA has developed a deep learning model using Google’s TensorFlow platform, analysing historical data from 1.5 million customers across over 70 variables, including claims history and driving behaviour. This sophisticated model improves prediction accuracy for significant traffic accidents from 40 to 78 percent, allowing AXA to generate customer risk scores that inform tailored insurance pricing and provide actionable feedback on driving habits.

Another notable example is Oscar Health, which employs AI to provide customised health insurance plans. By analysing individual health data, lifestyle choices and medical history, Oscar Health’s AI system recommends tailored plans that include specific services and wellness programmes, enhancing the overall healthcare experience for policyholders.

Marketing and customer acquisition

AI is enabling insurance companies to identify, attract and engage potential customers across digital channels. By analysing vast amounts of customer data and behavioural patterns, insurers can now deliver more targeted marketing campaigns and personalised product recommendations that resonate with specific customer segments.

Afiniti has helped various large US insurance companies by assisting their marketing strategy and customer interactions through intelligent pairing. By analysing behavioural patterns, Afiniti matches callers with the most suitable agents, leading to improved conversation quality and higher customer satisfaction. This personalised approach boosts sales, retention rates and overall profitability for insurance companies.

BGL Group has leveraged AI technologies to review extensive customer data to identify trends and preferences, enabling highly targeted marketing campaigns. This approach improves customer engagement and results in higher conversion rates by delivering personalised insurance solutions that align with individual needs. The integration of AI fosters a better understanding of market dynamics, allowing BGL Group to market to and acquire specific customers.

Regulatory compliance and reporting

AI is helping insurance companies navigate the complex landscape of regulatory compliance, while reducing manual effort and human error. Through automated monitoring and reporting systems, insurers can now more efficiently track regulatory changes, identify potential compliance issues and generate accurate reports for regulatory bodies.

SAS offers an AI tool, SAS Viya, to insurance companies for regulatory compliance and reporting. UTWIN, a fintech startup in France specialising in borrower insurance, has utilised SAS Viya to provide advanced analytics and real-time monitoring capabilities for its compliance reporting. The platform enables insurers to efficiently manage risks, detect anomalies and ensure adherence to anti-money laundering regulations.

Aon employs AI-driven analytics to streamline compliance processes, particularly in managing complex regulatory requirements across various jurisdictions. By utilising ML algorithms, Aon can automate data collection, monitor compliance risks and generate real-time reports, ensuring that clients adhere to evolving regulations efficiently.

Operational efficiency

AI technologies are also being used to streamline back-office operations across the insurance industry, automating routine tasks and reducing processing times. Insurers are leveraging AI to enhance workflow efficiency and reduce operational costs while maintaining accuracy.

Zurich Insurance employs AI to automate basic, repetitive and routine tasks. By utilising intelligent systems, Zurich reduces manual processing times for claims and administrative tasks, allowing staff to focus on more complex issues. This automation improves operational efficiency, reduces errors and enhances overall productivity within the organisation.

Progressive Insurance has implemented AI ML algorithms for claims processing and underwriting. Progressive Insurance has used AI to analyse vast amounts of data quickly, enabling faster decision making and more accurate risk assessments by human operators. This automation reduces processing times for claims and optimises resource allocation, ultimately leading to improved operational efficiency across the organisation.

As AI technology is consistently evolving, its impact on the insurance industry continues to be significant. AI is helping insurers operate more efficiently while delivering better customer experiences.

While challenges around ethics, privacy and regulatory compliance remain important considerations, the projected reduction in operational expenses makes AI investment a strategic imperative for insurers looking to stay competitive in an evolving market.

Nick Abrahams is global co-leader of the digital transformation practice at Norton Rose Fulbright. He can be contacted on +61 408 673 648 or by email: nick.abrahams@nortonrosefulbright.com.

Nick Abrahams

Norton Rose Fulbright