Advanced analytics in finance

Common obstacles in finance analytics

Between rising interest rates, geopolitical conflicts, stringent regulations, and the push toward environmental, social, and governance (ESG) reporting, the investment management industry is constantly in flux. These shifts compel firms to innovate and invest in more advanced technologies, particularly when it comes to artificial intelligence (AI) and data analytics. As a result, industry leaders are often expected to deliver forward-looking insights designed to navigate these changes more effectively.

However, several challenges hinder firms from optimizing their finance operations and harnessing the full potential of their data.

• Resistance to change and securing funding: Resources may resist automation due to fear of job loss, and senior managers may be uncomfortable relying on data-driven decisions. As a result, a well-designed change management plan and a clear articulation of the business case are essential.
• Recruiting and retaining next-gen skills: There is a growing need for employees who are skilled in tools like Python, machine learning, and Alteryx, but there isn’t necessarily a growing supply to match.
• Fragmented data: Inconsistent data labeling and multiple versions of products across departments often spark reconciliation challenges and unreliable deliverables.
• Dependency on legacy systems and processes: A firm tether to existing systems, no matter how outdated they are, means many firms rely on manually intensive data processes, therefore minimizing the amount of time spent on predictive and insightful work.

Turning new capabilities into potential solutions

To address these challenges, firms are better off investing in modern technology and more capabilities. Predictive analytics tools, like Deloitte’s PrecisionView, are designed to help clients improve finance processes, adopt modern practices, increase forecasting accuracy, and deliver actionable insights.

Case study: Automated costing allocation

For a top 10 investment manager, consistent struggles with manual spreadsheets for cost allocation led to errors and compromised product pricing. The team reduced manual errors and data transformation time by using a central info hub to store firmwide data and link it directly to the costing tool.

The costing tool also improved transparency in servicing, distribution, and production of investment products, while simultaneously facilitating regulatory reporting and product portfolio management. This meant that leadership was able to focus resources on high-growth product lines and undertake cost rationalization measures.

Case study: Forecasting using predictive modeling

Historically, many investment management firms have struggled with basic data for forecasting and scenario analysis, relying on manual spreadsheet models with limited granularity. Equipped with Deloitte’s PrecisionView tool, leadership was able to focus on leveraging systematic data aggregation and predictive modeling using historical driver data, regression analysis, data mining techniques, and machine learning.

On top of that, forecasted key performance indicators helped the client plan business strategy and stress test viability. This allowed them to understand the impact of various drivers on revenue and fund flows and adjust ETF product pricing accordingly.

Looking forward

These use cases illustrate the concrete benefits of predictive analytics and automation, including cost reduction and enhanced decision-making. For teams looking to develop a competitive edge, it is essential that they prioritize initiatives based on business impact, budget, and resource availability. 

Download our new report to take a closer look at why the investment management industry is experiencing these significant shifts, and how industry leaders can harness the power of analytics and AI to ensure their organizations are set up for success.