Yet, many insurers still rely on manual processes involving a combination of spreadsheets and text editors. These approaches can be labour-intensive and prone to errors. Tight deadlines, evolving regulations like IFRS 17 and the Solvency II review, and the need for simultaneous regulatory and internal reporting only add to these challenges. I believe that automation offers insurers the opportunity to reimagine financial reporting, shifting their focus from inefficiency to insight.
Process automation can significantly reduce reporting runtimes which frees up time for highly skilled experts for value-adding analysis. Open-source tools such as Python provide a cost-effective way to automate processes. Python enables a holistic view and optimisation of the reporting workflow from data imports, transformations to calculations and extracting the outputs in HTML, PDF or MS Word formats. The option to produce outputs in Word format means that hybrid solutions are also possible whereby senior actuaries can edit the auto-generated reports in a flexible manner.
I believe that embracing technological change requires a shift in mindset.
When using a Python automation toolkit, the results of any calculations such as tables and charts feed into reports seamlessly and the need for manual copying is eliminated. Key parameters such as reporting dates can be set and used consistently across the report. Any calculations requiring significant computing power can be accelerated using parallel computing. Furthermore, reports do not have to be built from scratch as users can utilize various built-in templates.
Advanced technologies such as GenAI can help to automate the reporting process. However, the risks inherent in this approach must be carefully managed, especially in light of the requirements of the EU AI Act implemented on 1 August 2024.
While Python offers substantial benefits, adopting it comes with challenges such as a steep learning curve, setup complexity, and a perceived lack of user-friendliness compared to traditional tools like MS Office. Potential solutions to overcome such barriers include adopting low-code or no-code applications, making Python more accessible for non-technical users. Companies could also implement comprehensive training programmes or consider outsourcing to bridge the skills gap.
I believe that embracing technological change requires a shift in mindset. Gaining buy-in from reporting teams is essential to ensure the success of any transformation project. As the insurance industry continues to evolve, automation will no longer be optional but essential. With advancements in Python and emerging technologies like GenAI, financial reporting can become faster, smarter, and more accurate. Now is the time to lead this transformation!
This blog is written in a personal capacity.
