Asset managers can streamline operations and unleash productivity with AI

Asset managers continue to face an unfavorable macroeconomic environment due to market volatility persisting through the first half of 2023. This has created limited exit opportunities in the public markets as well as difficulty in fundraising. Due to these challenges, asset managers are likely to remain focused on improving operational efficiency and their existing investment portfolios to increase returns for investors. Leveraging artificial intelligence, and specifically generative AI, to streamline operations could be the key to not only surviving but thriving in this landscape.

Compared to periods of low interest rates, the current environment has created a larger disconnect between sellers and buyers on the ever-present question of value. This has significantly slowed deal pace as compared to the peak in 2021.

Generative AI can optimize various functions of an asset manager’s business. Through its ability to swiftly process, summarize and analyze vast swaths of data, this powerful technology can help optimize investment portfolios by identifying patterns in that data while considering risk tolerance, historical performance, market trends, and portfolio screening and scoring. Large hedge funds already use AI for financial and sentiment analysis, and large private equity shops are tapping it to supplement research, deal sourcing and vetting.

Additional uses for AI and generative AI include:

• Document management and contracting: AI can help automate the extraction and analysis of key information from lengthy contracts, translate contracts from different languages, and help catch errors or inconsistencies.
• Risk management: AI algorithms can assess market conditions, news sentiment and macroeconomic factors, which can greatly improve an asset manager's risk management function.
• Compliance monitoring: AI-powered systems can monitor, analyze and interpret regulations. In addition to flagging potential violations, the technology can be used defensively to flag anomalies.
• Performance reporting: AI can automate the generation of investor relations documents, such as monthly or quarterly investor performance reports, and handle additional functions such as responding to basic inquiries and conducting research on potential investors.
• Back-office operations: By leveraging robotic process automation, asset managers can reduce manual efforts, minimize errors and streamline operational workflows.

By cutting the time spent on these more routine tasks, AI can help organizations focus on growing investor relationships in a more meaningful and valuable way. In a 2023 Microsoft survey of 31,000 people across 31 countries in various industries, 64% of respondents said they don’t have the time or energy to complete their jobs. This may be due in part to the fact that 60% of the average worker’s day is spent communicating and coordinating while only 40% is spent on their “day jobs,” according to the survey.

While generative AI can streamline operations, it also brings risks. Organizations need to ensure the data they use to inform their AI algorithms is unbiased, as poor data quality will lead to flawed insights. Additionally, the output from deep learning models can be difficult to interpret. This could be problematic for asset managers who prize transparency and accountability in the investment selection process.

Because AI implementation requires vast amounts of data, including sensitive information, asset managers also need to bolster their cybersecurity and data privacy protocols to mitigate the risk of data breaches and cyberattacks.

Finally, given that AI is a relatively nascent space, regulation will likely be forthcoming and asset managers will need to ensure compliance as laws go into effect.

Large asset managers have already begun exploring AI for operations. To remain relevant, middle market asset managers should look for ways to leverage the technology, including:

• Develop AI expertise: Reskill and upskill the workforce, promote a culture of innovation and collaboration, and partner with external parties to address any skills gaps.
• Establish or refine data strategy: Tailor data requirements, sources, quality standards and governance models to business needs, and ensure the organization’s risk management framework addresses the specific risks related to AI use.
• Invest in scalable tech infrastructure: Evaluate AI options and adopt the tools that best fit organizational needs, ensure a seamless integration of AI tech with existing systems and processes, and above all, strive for the adoption of ethical and explainable AI.