AI and funds management: How to supercharge your workflows using today's tools

Navigating the AI landscape can be challenging amid an overwhelming array of buzzwords and a constant influx of new platforms. In 2023 alone, US$630 million was invested in AI-powered PropTech—a figure set to rise further as advancements in AI continue to drive innovation in the sector, with AI and Generative AI emerging as two of the top three technologies poised to transform real estate.

In today’s rapidly evolving financial environment, leveraging artificial intelligence in funds management is less about futuristic hype and more about meeting practical challenges. In this blog post, we explore actionable AI solutions that streamline data analysis, enhance risk management, and automate reporting, enabling you to make informed, timely decisions.

The Rise of AI in Funds Management

The investment management industry has always relied on data—from market trends and economic indicators to company financials. However, the explosion of data and the need for rapid decision-making have driven firms to seek smarter, faster ways to process information. AI fills this gap by enabling:

• Research & Market Intelligence: AI can quickly sift through vast amounts of information to uncover hidden trends and correlations, empowering fund managers to make data-driven decisions based on real-time insights.
• Property Analysis & Due Diligence: By consolidating and automating the evaluation of property records, transaction histories, and market trends, AI streamlines the due diligence process and reduces the risk of manual errors.
• Risk Management & Predictive Forecasting: AI-powered tools simulate various economic scenarios to predict potential risks and market disruptions, allowing for proactive risk mitigation and more informed investment strategies.
• Automated Reporting and Content Generation: Natural language processing solutions automatically generate detailed, data-driven reports, freeing analysts to focus on high-level strategy while ensuring timely and consistent communication with stakeholders.

Top AI Tools for Fund Management

1. Research & Market Intelligence

The Challenge: Conducting thorough research means sifting through endless financial reports, market news, and industry studies. Manually combing through this data is not only time-consuming but can also lead to missed opportunities and delayed decisions when market conditions shift rapidly.

How AI Helps: AI-powered research platforms automatically aggregate and analyse data from multiple sources, distilling complex information into actionable insights. By automating these laborious tasks, your team can concentrate on strategic analysis instead of data gathering.

Key Tools & Real World Examples:

AlphaSense

• What It Is: AlphaSense is an AI-powered research platform that integrates financial data, documents, and analysis tools into one seamless interface.
• Benefits: It dramatically reduces manual research time, delivers real-time market intelligence, and offers customisable dashboards to track industry trends.
• Actionable Example: Wendel—a leading private equity firm managing over $10 billion in assets—needed to stay abreast of industry trends across multiple segments. By implementing AlphaSense, they cut the time spent sourcing market information by at least 50%. The platform enabled their team to conduct complex, industry-wide searches across broker research, event transcripts, and media coverage. Features like the Company Topics explorer provided a real-time view of market sentiment, while Table Extraction to Excel saved valuable hours previously spent on data reformatting.

Archistar: AI Research Platform

• What It Is: The Archistar AI Research Platform is a state-of-the-art tool that harnesses advanced geospatial analytics, satellite imagery, and machine learning to deliver comprehensive insights into commercial real estate markets. Tailored for fund managers, it provides detailed property mapping, planning data, and predictive analytics to support more informed investment decisions.
• Benefits: The Archistar AI Research Platform automates vast data collection and analysis, delivers interactive maps and visual insights for assessing property locations, zoning restrictions, and redevelopment potential, employs predictive analytics to forecast market trends and pinpoint high-potential opportunities, and streamlines property evaluations so fund managers can focus on strategic decision-making.
• Actionable Example: A commercial real estate fund manager can use Archistar AI Research Platform to scan a major urban area for under-utilised industrial sites; by leveraging its interactive mapping and predictive insights, the manager identifies several properties with favourable planning permissions and redevelopment potential, thereby reducing due diligence time and supporting more informed investment decisions.

2. Property Analysis & Due Diligence

The Challenge: Accurate property research and due diligence require a deep dive into data—from ownership records and transaction histories to current market trends. This process is often labour-intensive and prone to errors, which can lead to suboptimal investment choices.

How AI Helps: AI tools consolidate disparate data sources into a unified view, enabling quicker and more precise fund assessments. They reduce manual data entry and cross-referencing errors, thus enhancing both the speed and reliability of due diligence.

Key Tools & Real World Examples:

Arc: The AI platform for private credit

• What it is: Arc Intelligence is an AI platform designed specifically for the private credit industry, launched in late 2024. It automates complex financial tasks, with its first product being the AI Private Credit Analyst. The platform offers significant benefits to credit investors, including rapid data room processing, automated financial analysis, and generation of detailed credit memos. This automation transforms processes that typically take deal teams several days into tasks completed in minutes, with a reported 99% accuracy rate.
• Benefits: A key benefit of Arc Intelligence is its integration with Arc Capital Markets, a $4 billion private credit marketplace processing billions in loan volume. This integration creates a strong feedback loop, improving model accuracy and marketplace efficiency.
• Actionable Example: Arc has the ability to analyse unstructured financial data. For instance, when tasked with determining a company's total cash balance, the system can analyze multiple documents with different "as of" dates, identify the most recent period, and precisely extract the numerical value. This level of detailed analysis allows credit teams to make faster, more informed investment decisions, significantly streamlining the due diligence process

3. Risk Management & Predictive Forecasting

The Challenge: Identifying risks before they materialise is crucial, yet forecasting market downturns and performance fluctuations remains extremely challenging. Many managers still rely on intuition or outdated models, which can fail to capture the full complexity of a dynamic market.

How AI Helps: AI-powered risk management tools use historical data and advanced predictive models to simulate a variety of economic scenarios, offering early-warning signals for potential risks. This empowers managers to adjust strategies proactively and mitigate losses before they occur.

Key Tools & Real World Examples:

Blooma AI

• What It Is: Blooma is an AI-powered digital underwriting platform designed for commercial real estate lending. It leverages machine learning to rapidly process offering memorandums, extract relevant comps, analyse borrower documents, and assess asset potential—from loan origination to ongoing portfolio management.
• Benefits: Blooma automates data processing to drastically reduce underwriting turnaround times, enhances risk assessment through comprehensive analysis of financial statements and property data, and offers real-time monitoring with instant alerts—all while seamlessly integrating with existing Loan Origination Systems.
• Actionable Example: A private debt fund specialising in bespoke CRE financing—including bridge loans and ground-up construction—faced significant operational bottlenecks due to manual underwriting workflows, inconsistent borrower evaluations, and fragmented cash flow analysis. By implementing Blooma, they integrated borrower risk assessment dashboards, standardised financial templates, and real-time cash flow analysis.

4. Automated Reporting & Content Generation

The Challenge: Producing comprehensive, data-driven reports is essential—but manually compiling these reports is both laborious and time-consuming. This repetitive task not only consumes valuable resources but also increases the likelihood of errors, which can delay the delivery of crucial insights.

How AI Helps: AI-driven content automation tools transform complex datasets into clear, concise narratives, ensuring stakeholders receive timely and actionable reports. By automating the reporting process, these tools free up analysts to focus on strategic insights and high-level decision-making.

Key Tools & Real World Examples:

ChatGPT

• What It Is: ChatGPT is a state-of-the-art natural language processing tool developed by OpenAI that generates human-like text from complex data inputs. When integrated with data systems, ChatGPT can convert raw data into coherent narratives and detailed reports.
• Benefits: ChatGPT automates report generation, reducing manual effort while ensuring consistency and accuracy, and it offers the flexibility to generate custom reports tailored to various stakeholder needs.
• Actionable Example: At Caruso, one of our fund managers uses ChatGPT to boost strategic decision-making. They rely on it to draft investor reports, create branded content, and summarise lengthy documents from property managers, legal advisors, and other key stakeholders. This streamlined approach improves communication with investors while reducing manual workload and ensuring clear, consistent messaging.

Yseop

• What It Is: An AI-powered natural language generation tool that automates the creation of insightful, data-driven content.
• Benefits: Enhances efficiency by turning raw data into polished, ready-to-share reports that communicate key metrics effectively.
• Actionable Example: A private credit team could use Yseop to generate daily summaries of loan performance and risk indicators, ensuring that senior management can make swift, informed decisions.

Implementing AI: A Four-Step Framework

The transition to AI-powered fund management demands a methodical approach that balances innovation with operational stability. While the potential benefits are significant—from enhanced data analysis to automated reporting—success hinges on careful planning and systematic implementation. Here's a proven framework for integrating AI solutions:

1. Assess Current Operations

• Map workflows to identify time-intensive processes and quantify error rates in key areas
• Focus on data aggregation, risk analysis, and reporting bottlenecks through process audits
• Document specific pain points to build a clear business case for AI implementation

2. Select Appropriate Tools

Evaluate solutions based on:

• Core functionality alignment with identified operational needs and efficiencies
• Integration capabilities with your existing technology stack and data architecture
• Scalability to accommodate growing data volumes and market expansion
• Vendor stability and comprehensive support infrastructure

3. Deploy Strategically

• Begin with focused pilot programme to validate benefits and capture learnings
• Provide targeted training and documentation for key system users
• Establish clear support channels between team and vendors
• Set measurable success metrics aligned with business objectives

4. Review & Refine

• Track KPIs against baseline metrics to quantify improvements
• Gather structured user feedback to identify optimization opportunities
• Adjust implementation based on performance data and user insights
• Scale proven solutions across the organisation systematically

Conclusion

Fund managers are increasingly deploying artificial intelligence to enhance their core operations—from investment research and due diligence to risk management and investor reporting. These tools are delivering measurable improvements across commercial real estate, private credit, and private equity portfolios, reducing manual workloads while improving the speed and quality of decision-making.

Success comes from systematic integration: evaluating operational needs, selecting appropriate tools, and implementing them strategically. In an industry where information advantage defines performance, the effective use of AI often marks the difference between market leaders and followers.