Robotics Process Automation (RPA) is the application of robotics technology to manipulate existing application software which completes a business process. In recent years, this technique has captivated large financial services players given its potential to help drive down labor costs, reduce human errors and improve the customer experience. In institutional investing, many players seem to view RPA as a way to also deal with stringent regulatory requirements, lower projected future returns and growing cost pressures.

The challenge with this? The traditional RPA approach used by large financial services players usually doesn’t easily work in institutional investing. This is mostly because a large share of the total spend is related to front-office costs that RPA does not address, such as staff compensation, investment management fees and deal costs.

To overcome these challenges, the institutional investors who are most successful with RPA typically base their programs on three pillars:

1. Cross-departmental collaboration. High-performing RPA programs require a support team to roll-out and upkeep robots—and reprogram them when a process changes. In large banks and insurers, individual departments are usually large enough to justify creating their own support teams. However, institutional investors typically have departments which are smaller—and therefore inter-departmental collaboration is key.

2. Effective process selection. Institutional investors usually have narrower operations than banks or insurers. And only a handful of processes such as reconciliations, valuation, portfolio company information gathering and financial reporting typically account for most of the RPA opportunity. Consequently, a successful RPA program in institutional investing will over-index in process screening and opportunity validation capabilities versus at-scale deployment.

3. Business-led, integrated approach. Bank and insurance executives often take a two-step approach to their RPA journey by first selecting a tool, and then diligently deploying it across their organization. While this approach can be effective, institutional investing processes tend to be more complex than the average financial services process. Institutional investors who are most successful with RPA tend to identify business problems to address first, and then apply a broader scope of intelligent automation tools (e.g., RPA, AI, machine learning) to address those challenges.

Despite requiring a more tailored approach, we believe RPA has an important role to play in institutional investing operations. Not only could it help manage cost pressures, it could also help reduce errors, improve compliance, and simplify overall business complexity.

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