In today’s globally connected world, financial services businesses operate amid uncertainty, with capital markets forming a complex ecosystem of interdependencies that demand agility and resilience for survival and success. Constant change, driven by various disruptive forces, shapes the business, regulatory and geopolitical landscape, profoundly impacting investment decisions. Asset and wealth managers must navigate this volatile environment in real-time, relying on comprehensive insights to inform decisions.

As we progress through 2024, the operating environment faces strong headwinds, including high interest rates and varying growth patterns across economies. The International Monetary Fund forecasts a 3 percent growth for the global economy this year.1 The upcoming US presidential election and potential regulatory changes under the new administration add further complexity, requiring investment strategies to be adaptable to complex choices and trade-offs.

The outlook for investment managers in 2024 appears grim, with only 10 percent of respondents in a Deloitte survey2 anticipating significantly better revenue prospects this year. While technology and automation have empowered investment managers in the past decade, their impact has been waning, leading organizations to explore Artificial Intelligence (AI)-enabled initiatives that promise to significantly boost productivity and operational efficiencies.

Advanced AI in Investment Operations (InvOps)

The challenges around InvOps encompass a number of processes and activities (see Figure 1) required to manage and support investment portfolios. By emulating human intelligence, AI is transforming investment management, revolutionizing data analysis, risk assessment and decision-making processes with advanced algorithms and machine learning models. These enable quick and accurate evaluation of vast financial datasets, providing valuable insights for investment professionals.

Automation of routine tasks further enhances efficiency, allowing investment managers to focus on strategic planning, portfolio optimization and navigating complex market dynamics with agility and precision.

An Overview of AI Use Cases in Core Areas of InvOps

Figure 1: An Overview of AI Use Cases in Core Areas of InvOps

Onboarding New Customers

Given that investment management firms operate in a highly regulated environment, onboarding new clients requires enhanced due diligence to verify identities, assess risk profiles and comply with Anti-Money Laundering (AML) and Know Your Customer (KYC) mandates. This process can be complex and time-consuming, demanding extensive documentation in the form of tax records, bank statements and identification papers. Staying up-to-date with ever-changing regulations can be challenging, yet non-compliance can lead to severe legal and financial consequences.

AI can significantly improve efficiency in new customer onboarding by automating processes, lowering errors and boosting the overall customer experience. Specific areas include:

  • Document Processing and Verification: AI-powered Optical Character Recognition (OCR) tools can extract information from various formats and sources to expedite the process. Algorithms based on Natural Language Processing (NLP) verify this information with speed and accuracy for efficient compliance.
  • Customer Identity Verification: AI-enabled systems can dramatically transform facial recognition to verify customer identity during video calls or through uploaded photos. This provides another layer of authentication for biometric methods such as fingerprints or voice recognition.
  • Risk Assessment and Compliance: AI enablement simplifies tedious processes such as KYC. Trained algorithms can also intelligently monitor AML-related activities to help detect and prevent data breaches by flagging unusual patterns.

Risk Management and AI

Effective risk management is pivotal in safeguarding investments and optimizing portfolio performance. This involves identifying potential threats and uncertainties stemming from market conditions, credit and liquidity, as well as operational risks influenced by market trends, economic indicators and external factors. It’s crucial to quantify the potential impact of identified risks on the portfolio. Common risk mitigation strategies include diversification, hedging and strategic asset allocation.

Furthermore, risk management necessitates adherence to industry regulations and legal requirements. For instance, recent emphasis on Environmental, Social and Governance (ESG) concerns has led to numerous regulatory mandates. With this added layer of complexity to risk assessment, portfolio managers are increasingly turning to AI to analyze new sustainability datasets and extract value.

AI has emerged as a transformative force in investment risk management. By swiftly processing vast amounts of data, advanced AI-based algorithms enable better credit decisions and reduce operational, governance and compliance costs. Specifically, AI assists risk management in the following ways

Market Volatility Management

AI algorithms provide real-time analysis of market volatility, automatically triggering risk management measures, such as adjusting exposure and hedging positions, to mitigate potential losses during turbulent market periods.

Liquidity Management

AI analyzes market conditions to facilitate smooth asset transactions and enhance liquidity management strategies. Investment managers can make trading decisions without significantly impacting prices.

Margin Maintenance

AI-powered systems continuously monitor market conditions and portfolio positions in real-time, enabling quick adjustments to margin requirements as market conditions evolve. This ensures that margin levels remain appropriate and compliant with regulations.

Regulatory Compliance

AI streamlines regulatory compliance by identifying discrepancies and potential violations, thus minimizing the risk of penalties. Its ability to process vast data ensures seamless adherence to regulatory requirements, thereby lowering operational costs.

Scenario Analysis

AI facilitates scenario analysis by simulating various market conditions and assessing their impact on margin requirements. This empowers investors and risk managers to anticipate and understand how changes in asset prices, interest rates and other factors might affect margin obligations.

AI for Seamless Trade Settlement and Reconciliation

Trade settlement, ensuring timely and accurate completion of trades, and reconciliation, aligning trade details across systems, are critical for investment integrity and operational efficiency. Any discrepancies or delays can lead to failed trades, misaligned portfolio positions and erroneous decision-making. This includes corporate actions initiated by publicly traded companies that can impact the ownership or value of its securities.

These functions are fundamental not only for trade completion but also for risk management, compliance and regulatory adherence. AI revolutionizes trade settlement and reconciliation by automating tasks, validating data, handling exceptions and driving continuous improvement for fund managers.

  • Automation and Efficiency: AI-driven systems automate trade settlement and reconciliation tasks, reducing errors and accelerating processes through historical data-driven machine learning.
  • Data Validation and Enrichment: AI enhances trade data accuracy by validating and enriching it with relevant information, leveraging NLP to interpret unstructured data from sources like trade confirmations and e-mails.
  • Handling Exceptions: AI assists in locating and resolving exceptions or discrepancies during trade settlement, using historical data to recommend effective solutions and avoid delays.
  • Straight-through Processing (STP): AI-powered systems boost STP rates by automating the entire trade lifecycle, minimizing manual intervention and errors across the board from trade execution to settlement.
  • Continuous Improvement: AI analyzes trade settlement outcomes, identifying areas for improvement and enabling investment firms to refine operations over time, enhancing efficiency and reducing errors continuously.

Performance Measurement and Management

Performance measurement and management offers quantitative insights into investment strategies, aiding decision-making. Accurate evaluations help investors assess portfolio effectiveness, pinpoint areas for improvement and align investment objectives with financial goals. These metrics also serve as vital benchmarks for evaluating investment professionals, fostering industry transparency and accountability.

Investment funds often hold diverse financial instruments, requiring specialized accounting and valuation methods. This complexity poses challenges in accurately recording and reporting their values, especially with Net Asset Value (NAV) calculation being crucial for determining investor holdings. NAV errors can lead to incorrect investor payouts and legal ramifications

AI can transform performance measurement and reporting in InvOps, enhancing efficiency, accuracy and depth.

Automated Data Collection and Integration

AI automates data gathering and integration from various sources, ensuring accurate and current data for performance evaluations.

Complex Performance Calculations

AI swiftly handles intricate calculations across asset classes and benchmarks, including Time-weighted Return (TWR), Money-weighted Return (MWR) and risk-adjusted metrics such as Sharpe and Sortino ratios.

Pattern Recognition and Anomaly Detection

AI identifies patterns and anomalies in investment data, detecting errors, fraudulent activities and improvement opportunities.

Customized Reporting and Visualization

AI-driven tools generate tailored reports with visualizations, summaries and key performance indicators for internal teams and clients, facilitating a clearer understanding of investment performance.

Real-time Reporting

AI enables real-time data processing and reporting, empowering professionals with timely insights for swift decision-making in fast-paced markets.

AI as an Enabler in Fund Accounting

Fund accounting encompasses numerous manual tasks, reconciliations and calculations that are time-consuming and prone to errors. As investment product complexity and asset volumes grow, accounting becomes increasingly intricate. Investment firms must balance operational efficiency with cost control, often involving investing in technology, automation and collaboration with experienced third-party service providers.

AI is rapidly advancing in fund accounting and investment operations, offering significant benefits in efficiency, accuracy and decision-making. Key areas of impact include:

Automated Data Processing and Cleansing

AI streamlines data collection and cleansing by automatically identifying errors or inconsistencies and enhancing overall data quality.

Transaction Matching

AI algorithms excel in swiftly and accurately matching transactions, reducing the risk of errors and exceptions compared to manual processes.

Risk Management

AI identifies risk by analyzing historical data and market trends, providing early warnings for anomalies or events affecting investment portfolios.

Winning with an AI-led InvOps Strategy

Embracing an AI-led InvOps strategy is imperative for investment managers navigating today's complex landscape of regulatory changes, economic uncertainty and geopolitical volatility. Beyond access to information, success hinges on the ability to harness data, leverage predictive insights and proactively position in the market to deliver the alpha.

Companies that outperform peers prioritize growth and innovation, fostering an environment for creativity and forward-thinking, and investing in cutting-edge technologies like Generative AI (Gen AI). The advent of Gen AI is re-shaping InvOps, offering unparalleled advantages in data comprehension, algorithmic sophistication, research efficiency and knowledge management.

Its integration marks a new era of precision and strategic foresight in dynamic financial markets, positioning organizations for success in the ever-evolving landscape. Early adopters of Gen AI-led initiatives are well-positioned to innovate rapidly, with many top innovators already implementing advanced Gen AI strategies.3

Advanced AI capabilities, including Gen AI, are revolutionizing InvOps, enabling investment managers to predict outcomes and exponentially enhance their ability by tapping into collective intelligence. When coupled with insights and experience, these capabilities become winning strategies, transforming investment operations and empowering informed decision-making.

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