From Spreadsheets to Automation: The Power of KidbrookeONE in Financial Analytics

Optimising Financial Analytics: Embracing Automation for Improved Decision-Making 

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As the financial industry undergoes continuous transformation, the ability to streamline financial analytics processes becomes more critical than ever. According to research by Forbes, 75% of financial services workers believe that up to a quarter of their routine information-based tasks could be automated by AI. For financial institutions, particularly those in wealth and investment management, shifting from traditional spreadsheets to advanced automation platforms can unlock significant cost savings and drive more efficient decision-making. In today’s blog, we explore specific challenges that firms face in regard to their analytical processes and offer the classification of analytical tools that can transform how firms manage and analyse their market, ESG and product data. 

Data Challenges in The Financial Industry 

Many financial institutions still rely on traditional, manual processes, which presents numerous operational challenges. First, these issues relate to navigating complex and abstract requirements, exacerbated by balancing competing internal priorities and consolidating multiple data sources for analysis and decision-making. Defining these requirements upfront is challenging, often leading to increased costs, delays, and other operational inefficiencies. Gathering detailed requirements from multiple stakeholders, organising the proofs-of-concept phases before embarking on larger projects, and designing or acquiring flexible solutions that adapt to evolving needs is vital for ensuring the effectiveness and compliance of the analytical processes. 

Another issue is that subject matter experts must iterate over possible solutions to fully understand the problem and validate solutions, which is not always possible due to insufficient expertise and an inefficient inter-organisational dialogue. This process involves developing, evaluating and validating multiple iterations, gathering feedback, and making incremental improvements, which can be time-consuming and resource-intensive.  

Third, financial experts still rely on manual tools like spreadsheets for financial analytics, but these are not ideal for large-scale industrial applications. While Excel-based solutions offer flexibility and ease of use, they fall short in handling complex, high-volume data processes and ensuring data consistency across multiple users. They are prone to inefficiencies and risks, lack version control, and are difficult to scale or integrate with other systems.  

For example, one of our clients, a leading Swedish life insurance and pensions broker, faced these challenges in managing product universes and model portfolios across multiple platforms. They used large spreadsheets for data aggregation and metric calculation, which was prone to crashing and very time-consuming. 

Modern financial analytics require robust, automated solutions that can handle large datasets, provide real-time insights, and support seamless integration, which traditional tools like spreadsheets cannot adequately deliver.   

Overcoming the challenges 

To overcome these challenges, financial institutions need to consider balancing the flexibility of manual, spreadsheet-based analytics with the need for more robust, scalable solutions. This can be facilitated by: 

Acknowledging the Need for Initial Flexibility: Allowing subject matter experts to use flexible, manual tools during the early stages of the projects to explore and define solutions is crucial. This initial phase enables detailed problem exploration, helping to clarify complex requirements and identify potential pitfalls early on. Embracing spreadsheets in the initial stages allows experts to iterate quickly and develop a deeper understanding of the challenge and the solution before committing to large-scale development.  

Planning for Industrialisation: Once the exploratory spreadsheets have been developed, it's essential to invest time and resources to convert these into industrialised processes. By doing so, firms can ensure that these solutions are robust enough to handle large volumes of data, meet compliance standards, and integrate seamlessly with other systems. What’s more, proper planning for industrialisation helps bridge the gap between proof of concept and full-scale implementation, ensuring solutions are efficient and resilient. 

Mixing Business and Tech Expertise: Ensuring your teams have a blend of domain and technical expertise is vital for developing and maintaining effective financial analytics solutions. This cross-functional collaboration ensures that business needs are accurately translated into technical requirements and that the solutions developed are aligned with business goals. Combining insights from both business and technology perspectives enables more innovative and comprehensive solutions, enhancing the overall effectiveness of the analytics process. 

We've witnessed these challenges firsthand at Kidbrooke through working with our clients. To address them, we developed KidbrookeONE, a fully integrated platform designed to handle the entire lifecycle of financial analytics and optimise workflows, mitigate risks, and deliver more efficient outcomes. KidbrookeONE supports the industrialisation of processes, ensuring your analytical solutions are scalable, robust, and adaptable to ongoing changes.  

Descriptive, Predictive and Prescriptive Analytics 

However, which analytical processes do financial institutions aim to enhance, and how can they industrialise their capabilities and workflows? 

Descriptive analytics allows firms to summarise the performance of portfolios, funds, and other financial instruments. This involves aggregating internal and external data to create a clear picture of all the relevant factors in the decision-making – from customer and market data to the ESG performance of the underlying investment products. 

For instance, insurers, banks and wealth managers utilise descriptive analytics to answer questions like, "What is the value of my current portfolio?" or "How has the portfolio developed over time?" This capability extends to evaluating the impact of investments on the ESG (Environmental, Social, and Governance), providing a comprehensive view of both performance and ethical considerations. 

By automating these descriptive processes, KidbrookeONE helps financial institutions move away from manual spreadsheet-based analysis, reducing operational risks and improving the accuracy of the information shared with stakeholders. 

Moving beyond the present, predictive analytics is crucial for forecasting and planning. KidbrookeONE’s predictive capabilities are built in a robust economic scenario generator that models potential future outcomes for various financial products. This allows wealth managers to simulate different economic scenarios, such as market shocks or portfolio adjustments, and understand their potential impacts probabilistically. 

For example, predictive analytics can help answer questions like, "What might the future value of the portfolio be?" or "What happens if I change my portfolio composition?" These insights are invaluable for strategic planning and client advice, enabling firms to make informed decisions and provide more personalised advice. 

The predictive layer is essential for creating engaging financial journeys for clients, from onboarding experiences to ongoing wealth management. By integrating predictive analytics, KidbrookeONE enables financial institutions to build forecasts that are not only accurate but also adaptable to shifting market trends and client needs. 

The final step in the analytics journey is prescriptive analytics, which uses available data to suggest optimal actions. KidbrookeONE leverages advanced models to provide recommendations that help financial institutions and their clients make informed decisions. This includes utility-based portfolio optimisation, which can suggest the best investment strategies based on individual client risk profiles and market conditions. 

Prescriptive analytics answers questions like, "How can I improve my portfolio to better suit my needs?" or "Is it better to invest in Fund A or Fund B?"  

Financial advisors are empowered to deliver higher-quality, personalised advice by offering such actionable insights, enhancing the overall client experience. 

Long-term Success with Advanced Financial Analytics   

Whether you’re looking to describe current portfolio values, predict future outcomes, or suggest actions based on your clients’ data, KidbrookeONE provides a unified approach to analytics and data management. This enhances operational efficiency and ensures consistency across various financial processes. 

As financial markets become more complex and client expectations continue to rise, adopting advanced analytics platforms like KidbrookeONE is essential for staying competitive and achieving long-term success. 

Feel free to reach out for more information or schedule a KidbrookeONE demo.