Transforming Financial Analytics: The Shift from Manual Processes to Advanced Automation

The integration of advanced automation in financial analytics has become increasingly crucial. Studies suggest that up to 25% of routine tasks in financial services could be automated with AI, signalling a major shift in how financial institutions operate.

For firms in wealth and investment management, moving from traditional spreadsheets to sophisticated automation platforms can lead to significant cost savings and improved decision-making efficiency. Recent insights into the challenges faced by these firms highlight how advanced analytics tools can revolutionise market, ESG, and product data management.

Many financial institutions still rely on outdated, manual processes that involve managing complex and often ambiguous requirements, juggling various internal priorities, and integrating disparate data sources. This process of defining requirements can be costly and time-consuming. To improve their analytics processes, institutions must carefully collect detailed requirements, manage proofs-of-concept efficiently, and develop or acquire flexible solutions that can adapt to changing needs.

Manual tools like spreadsheets present challenges, especially when scaling for large-scale applications. While they offer flexibility and ease of use, spreadsheets struggle with managing large data sets and maintaining data consistency among multiple users, leading to inefficiencies and potential risks. For example, a Swedish life insurance and pensions broker faced significant issues with spreadsheets, encountering frequent crashes and inefficiencies. This underlines the need for robust, automated solutions that can handle large volumes of data, offer real-time insights, and integrate seamlessly—capabilities that spreadsheets cannot adequately provide.

To overcome these challenges, financial institutions should blend the flexibility of manual tools with the robustness of scalable solutions. Initially, manual tools can be used for in-depth problem exploration and requirement clarification. Following this exploratory phase, it is essential to invest in transforming these manual processes into industrialised systems that can manage extensive data volumes, comply with standards, and integrate smoothly with other systems.

Moreover, fostering a combination of business and technical expertise within teams is crucial. This collaboration ensures that business needs are effectively translated into technical requirements and that the solutions developed align with organisational goals.

An integrated platform exemplifies this approach by optimising the entire lifecycle of financial analytics. It enhances workflows, mitigates risks, and ensures more efficient outcomes. Beyond basic descriptive analytics, such platforms provide predictive capabilities through an economic scenario generator, enabling wealth managers to model future scenarios and their impacts.