How Generative AI Can Transform the Finance Function in Insurance

The insurance industry has long been a relatively advanced user of automation to improve operational efficiency, from underwriting to claims processing. However, automation in these areas often faces limitations, such as high levels of manual intervention and fragmented data systems. Generative AI (GenAI), driven by machine learning and advanced data analytics, offers further opportunities to revolutionise insurance—particularly within the finance function. Finance professionals are crucial in actuarial and risk modelling, financial reporting, business partnering and portfolio management, and regulatory compliance. By leveraging GenAI, insurers can transform these processes, enhance decision-making, and significantly improve operational performance and productivity. For instance, GenAI can automate complex tasks like reserving and financial hedging, freeing up finance professionals to focus on strategic decision-making. It can also provide more accurate and timely financial forecasts, improving financial planning and budgeting. These are just a few examples of how GenAI can drive transformation in the finance function.

However, adopting GenAI at scale in finance presents complex challenges. Insurers need to manage multiple accounting standards, integrate fragmented data systems across regions into platforms that can act as springboards, and simplify intricate actuarial models. While these challenges are considerable, they also present opportunities for insurers to build a more integrated and scalable finance function. Successfully implementing GenAI hinges on evaluating where it can offer the most substantial gains, such as improving reserving accuracy, optimising financial hedging, and enhancing investor relations. Estimates suggest that GenAI could deliver efficiency improvements of 10% to 20% across the finance function, creating a foundation for future innovation and supporting profitable growth.

Key Areas Where GenAI Can Drive Transformation

1. Managing Specialised Processes: Tasks like reserving and financial hedging are complex and data-intensive in finance. GenAI can automate parts of the reserving process by analysing historical data and simulating future claims, helping actuaries provide more accurate loss reserve estimates. This ensures more robust capital allocation and better risk management. Similarly, GenAI can analyse financial markets and policyholder data to support strategic hedging, improving investment decisions and safeguarding against market fluctuations.
2. Enhancing Financial Reporting and Compliance: GenAI can streamline regulatory reporting and tax compliance by automating data analysis and reporting tasks. It can continuously monitor changes in tax regulations and accounting standards, such as IFRS 17 while helping finance teams adapt quickly to these requirements. This automation frees up valuable resources, reduces manual errors, and ensures that compliance reporting is accurate and timely.
3. Empowering Decision-Makers: GenAI can analyse large datasets in financial planning and budgeting to create precise financial forecasts and what-if scenarios. This is crucial for insurers, as even minor changes in economic variables can have significant implications for balance sheets and P&L statements. By integrating insights from various departments, such as actuarial teams and investment portfolios, GenAI aligns financial strategies with operational imperatives. It also enables the identification of hidden patterns in data, offering more profound insights into market behaviours and trends that finance professionals can use to refine strategies and make data-driven decisions.

Streamlining Operations and Data Management

In addition to enhancing specialised processes, GenAI can drastically improve operational efficiency by automating routine tasks such as transaction processing, cash flow management, and financial reporting. For instance, GenAI can automate data validation and cleansing, improving data quality controls in finance operations. This is essential for maintaining the accuracy of financial statements and ensuring compliance with regulatory requirements like Solvency II.

By automating these manual tasks, insurers can reduce operational risks, enhance decision-making, and optimise cash flow management, leading to more efficient capital allocation. As insurers typically manage substantial cash, improving these processes creates a competitive edge and drives financial performance. Moreover, improving operational efficiency supports better business partnering and strategic planning, linking GenAI's capabilities to long-term economic strategy.

Overcoming Adoption Challenges

For multinational insurers to scale GenAI within their finance functions, they must address several barriers:

1. Accounting Standards: GenAI must adapt to various global and local accounting standards, including IFRS 17, IFRS 9, and regional GAAP requirements, as well as capital regulations like Solvency II. Ensuring flexibility and accuracy in navigating these standards is crucial for successful implementation.
2. Data Fragmentation: Many insurers face the challenge of fragmented data across regions and systems. To fully harness GenAI, data must be centralised and accessible in a standard format, which often requires significant organisational restructuring.
3. Actuarial Complexity: Actuarial models involve sophisticated calculations and assumptions about future risk, making them difficult to automate. Insurers must invest in engineering efforts to codify these models into algorithms that GenAI can process and analyse effectively.

While these challenges are significant, insurers that adopt a disciplined approach and focus on high-impact areas can build a more agile and efficient finance function.

Building a GenAI-Driven Finance Function

To implement GenAI successfully, insurers should take the following steps:

1. Estimate the Potential Impact: Quantify the potential efficiency gains and improvements that GenAI can bring to finance functions. Demonstrating these gains can help mobilise the organisation and drive investment, igniting a sense of motivation and eagerness to explore the possibilities of GenAI.
2. Identify High-Impact Use Cases: Prioritise use cases with high returns, such as reserving or financial reporting automation, and gradually expand the scope to more complex processes. For instance, in the area of reserving, GenAI can automate parts of the process by analysing historical data and simulating future claims, helping actuaries provide more accurate loss reserve estimates. In financial reporting, GenAI can automate the generation of compliant reports, reducing the time and effort required for manual data analysis and reporting. Starting with tasks that are easier to automate, such as transaction processing, can provide quick wins and build momentum for broader GenAI adoption.
3. Establish Controls and Safeguards: Implement strong data governance, risk controls, and review processes to ensure the accuracy of GenAI-generated outputs, particularly in heavily regulated environments. Set clear KPIs to measure the success of AI implementation and demonstrate tangible value.
4. Foster Responsible AI Use: Develop a clear framework for responsible AI use, integrating ethical principles and risk mitigation into governance practices. Ensuring that AI applications are used safely and responsibly is essential in maintaining regulatory compliance and trust.

Examples of AI Use Cases Specific to Finance Function in Insurance

1. Actuarial Modelling and Reserving: GenAI can enhance the actuarial reserving process by using machine learning to improve the accuracy of loss reserves. By analysing historical data and simulating future claims scenarios, GenAI helps insurers forecast liabilities more effectively, improving capital allocation and risk management. This reduces manual errors, provides real-time insights, and strengthens compliance with regulatory frameworks like IFRS 17.
2. Financial Hedging and Capital Management: GenAI can support financial hedging by analysing financial market trends, policyholder data, and economic factors to predict risks and identify hedging opportunities. This enables insurers to optimise investment portfolios and more effectively manage exposure to market fluctuations. AI-driven insights help fine-tune capital strategies and ensure financial stability in volatile markets.
3. Financial Reporting and Investor Relations: Generative AI can streamline the financial reporting process by automating the generation of compliant reports, analysing complex financial data, and creating clear investor presentations. By automating data extraction and applying accounting standards such as IFRS, insurers can reduce manual reporting errors and ensure that investor materials are accurate, up-to-date, and reliable.
4. Regulatory Compliance and Tax Reporting: AI-driven solutions can monitor regulatory changes, automate tax reporting, and help insurers adhere to complex compliance frameworks. This includes continuous monitoring of evolving regulations like IFRS 9 and Solvency II, ensuring that finance teams can meet their obligations with reduced manual intervention. GenAI can also improve collaboration between regulatory teams by using AI chatbots to streamline communication and training on compliance topics.
5. Financial Planning and Scenario Analysis: GenAI enables precise financial forecasting and budgeting by analysing historical financial data and market conditions. The technology can also perform complex sensitivity analyses, providing insights into the impact of interest rate changes, claims frequency, and macroeconomic shifts. This is particularly important in insurance, where small fluctuations in key variables can significantly affect financial outcomes.
6. Operational Efficiency and Transaction Processing: GenAI can automate transaction processing, reconciliation, and other routine finance tasks. By learning from historical data, the AI can accurately process transactions, reducing operational errors and accelerating processes. For insurers, this improves day-to-day finance operations and helps maintain clean and reliable financial records.

The final takeaway

Generative AI presents a significant opportunity for insurers to enhance their finance function, improve operational efficiency, and gain a competitive edge in the market. While the path to adoption may be complex, overcoming challenges related to data fragmentation, regulatory compliance, and actuarial complexity will enable insurers to realise GenAI's transformative potential. Insurers that take a strategic and disciplined approach to implementing GenAI will enhance their operational performance and set a new standard for innovation in financial management and risk analysis.

If you'd like to learn more about how GenAI can revolutionise your finance function or explore specific use cases tailored to your business, please feel free to reach out. We can also facilitate tailored sessions to bring these ideas to life and help you discover GenAI's potential in your organisation.