Insurance Accounting Advisory Quarterly

Insurance companies are navigating a digital future that is far more connected than the past. Insurance leaders understand the need to modernise their finance function to help achieve growth and strategic objectives.

In doing so, this can enable organisations to develop a stronger, more efficient infrastructure and better equip finance colleagues to deliver value through strategic direction and operational excellence across the value chain.

The urgency of transformation is clear. As insurers start to move into new areas and develop new business models, leaders within the organisations are looking to the finance function for insight and advice on how to create and protect value, along with the continued expectation to act as a reliable facilitator of regulatory change and compliance.

Yet many insurance finance functions are still working with legacy platforms accompanied by manual, complex processes that require intervention. This has left finance teams and systems under strain, battling to meet their reporting deadlines while looking at ways to add value to the wider business.

The finance landscape is changing rapidly

Many insurance CFOs and finance leaders have been advocating for transformation for some time. Some believed that the shift to meet the Solvency II reporting requirements and most recently IFRS 17 requirements might provide the wider opportunity to modernise finance technologies, processes and models. However, organisations quickly found that the effort required to translate and integrate these into existing IT systems and processes had been underestimated. And for many, focus quickly narrowed to achieving compliance by meeting reporting deadlines and getting back to business as usual. The initial vision of a transformed function was lost.

Undertaking a transformation project can be challenging and complex. But insurance organisations now better understand the requirements and areas of risk. Many CFOs understand the power of data and advanced technologies such as AI and are working to align their transformation journey with the organisation’s overall business strategy to get the investment and support needed.

Setting a vision for change

Articulating a vision for a transformed finance function is critical. It can assist with driving and guiding the journey, to help ensure the organisation remains aligned and engaged with the scope and governance of the project.

At the same time, setting a vision allows the function to get their stakeholders (both leadership and those directly impacted) on board with the case for change.

Finance must evolve its role again, and needs to feel different to today

Insurers need finance to be agile and quick to react to business change
Finance needs to play an influential role, partnering with business leaders and functions to improve decision making, by understanding results drivers
In future, finance needs to lead on data driven decision making across the organisation, and work across traditional boundaries
Insurers need their finance teams to focus on outcomes not processes and tasks
Finance needs to be a role model for insurance digitisation and being a cost-efficient function
Finance needs to re-imagine itself around an integrated set of capabilities, evolving these over time to move higher up the value chain for the business

AI is moving fast…

Generative AI (Gen AI) is currently causing a buzz across the insurance industry. While artificial intelligence and machine learning have been successfully applied to specific problems for some time, the ability of Gen AI to generate new content and its accessible user interface makes it hugely relevant to a broad spectrum of use cases across the finance function.

AI might not be new, but it is moving very fast, and CFOs need to think differently and quickly understand how to leverage it. The Central Bank also recognise this pace of change and recently held an industry workshop on the Responsible Use of Big Data & Related Technologies, as discussed in their September 2024 Insurance Quarterly publication, with key themes related to governance & risk management, data usage & management and ethical and fair usage in avoiding consumer detriment.

A marriage of Gen AI capabilities and finance can create better speed and efficiency by eliminating redundant or manual activities, allowing finance professionals to focus on higher value tasks. But like its predecessors, Gen AI is only as good as the underlying data and well-engineered prompts and will only be effective when it is embedded in the right way within the finance function.

Natural language generation capabilities are already being used by finance functions across other industries to create reports, which are standardized and to describe basic analytics such as how results compare against previous data. AI is also being trained to identify patterns and trends in financial datasets that may indicate errors or anomalies.

Some finance functions are also using AI to raise reporting queries, using a conversational prompt which is more intuitive than building reports and requires much less time. Similarly, it is being used by business and finance professionals to extract key information, generate summaries, and conduct analysis of large documents such as accounting policies, contracts, policy documents and financial reports.

Where leading finance teams are adopting AI

Forecasting & budgeting

Integrating predictive models, creating scenarios, and generating insights on potential financial outcomes. Insurance companies are incorporating R/ Python scripts into business planning tools to facilitate the handling of larger data sets and more complex calculations, thereby enhancing the capability for comprehensive business planning.
Generating commentary

Reducing the time and effort needed to create recurring materials required for financial reporting, business reviews, management reports and board meetings.
Collecting market intelligence

Powerful research tool able to find and synthesise public data to generate insights on markets, competitors, and customers.
Generating strategic insights

Partnering with other functions to provide insights across the business. Use finance’s position to inform strategic decisions and solve problems with pricing, performance, and benchmarking metrics.
Detect anomalies

Gen AI shows promise as a tool for detecting errors. It can compare new data with past patterns to identify anomalies.
Financial operations

Automation of manual tasks such as data entry, reconciliations and invoice matching, coding, analysis.
Data visualisation

AI facilitates interactive and intelligent data visualisation, enabling users to gain insights, receive smart recommendations, and dynamically modify visualisations based on their data and preferences.
Addressing legacy Excel

AI facilitates accelerated analysis of existing Excel files providing model documentation, visual flow maps and definitions of calculations used which helps to reduce risk and also accelerate migration activities. Insurance companies are also using Gen AI to accelerate the coding conversion of legacy Excel spreadsheets and models to more robust formats such as R/ Python scripts which further enables automation, workflow capabilities, speed & ability to handle larger data sets.
Modelling design acceleration

Gen AI can be used to interpret model specifications or policy documents and automatically convert them into models (e.g. Excel/ Python/ R).

How are insurers approaching AI transformation?

Insurance organisations are approaching AI transformation strategically and with cautious optimism. Many have seen early success with a handful of integrated AI solutions, where use of the technology has typically been developed to tackle a specific problem, such as quality assurance.

Others are developing an understanding of the wider capabilities through integrated platforms, such as Microsoft Copilot, learning to quickly create human-like text, images, audio, and videos.

Historically insurance companies have been conservative when adopting new technologies and while insurance companies and CFOs understand the potential advantages of scaling initiatives, there is a hesitation to introduce AI more widely across the workforce, partly due to the speed of evolution and associated risks.

There are also growing concerns around data quality along with ethics and biases (particularly when using legacy datasets), in addition to regulatory compliance across jurisdictions. The inability to respond to these could result in significant risk to reputation and rising pressure from shareholders.

Ultimately, AI can help insurance companies improve the accuracy and speed of their financial reporting processes, which can help them make better-informed decisions and improve their overall financial performance.

In order to harness the real power of Gen AI, however, organisations need a modern technology stack, access to quality data and an ability to integrate new technologies.

By the nature of it, an AI transformation program is heavily data-ready centric. To get the best results requires focus on the performance insights & data layer of the Finance Target Operating Model. For most organisations, there’s much to do around data to be able to exploit it to the full.

Therefore, it is important to ensure that you have the right combination of data skills, together with the experience of how to maximise the capability of data within an AI enabled finance function.

Consider the types of skills and capabilities that will be required. In part, this is about upskilling colleagues to add value within the new operating model, in addition to identifying new talent that understands the benefits that the new technology can deliver to the organisation, as well as people who can bridge the gap between finance technical skills and data/technology capabilities.

Finance leaders should also take the time to set the foundations of good transformation. Don’t underestimate the importance of good change management supported by open and continuous communication.

A key determining factor of success in transformation is the ability to influence and bring senior stakeholders, alongside the wider workforce, on the journey. Executive support and buy-in will be critical to securing budget, resources, and collaboration from the business.

And the more engaged finance professionals are in the project, the more value the organisation will get out of the investment in the long run.

Questions to consider before undertaking finance transformation

How can you streamline and automate finance processes to reduce manual effort, increase accuracy and improve speed?

Assess current processes to identify areas that can benefit from AI (repetitive, time consuming, and prone to errors) and assess the availability of data to enable it.
How are new technologies such as Cloud ERP, AI and data analytics impacting the finance function, and would adopting these help to improve finance operations?

Identify inherent AI capabilities and opportunities in existing technologies and research and select AI tools that align with your objectives and technical capabilities.
Is finance talent being developed and retained through training, career development and performance management?

Identify potential gaps in AI skillsets (data literacy, AI ethics & governance, digital fluency, etc.).
Are financial controls and governance frameworks sufficient to ensure ongoing compliance with regulations and mitigate risk, particularly related to new technology & AI?

Establish a framework that outlines the principles, policies, and processes for governing AI initiatives within the finance organisation and gather an inventory of controls that could be impacted by AI and assess potential risks. Identify and understand regulations and compliance obligations in the context of AI (industry, government, etc.) The EIOPA AI Act needs to be a key consideration in this, with particular attention paid to the insurance-specific use cases that have been classified as high-risk within the Act.
Does the current finance function operating model align with the needs of the business and other stakeholders?

Assess current state to identify areas where AI could enhance efficiency and productivity.
Does the finance function have strong relationships with the business to strengthen decision-making and drive business performance?

Restructure roles and responsibilities within the finance organisation, redefining job functions to align with the new service delivery model allowing finance professionals to focus on higher value tasks.
What lessons has your organisation learned from either previous transformation efforts, or from wider industry examples of embedding transformation into the organisation strategy?

Evaluate current data management capabilities to understand gaps and limitations in handling AI-related data (e.g. quality, accessibility, integration, and security) and collaborate with IT to assess current data architecture and infrastructure.

For more, get in touch

At KPMG we believe transformation starts with people. Our global network of experienced insurance professionals provides clients with deep industry knowledge, actionable insights and implementation expertise, helping them to realise the full potential of their people and technology, and working together to achieve successful transformation.

Because when people and technology are in harmony great things happen.