Moody’s: AI Set to Revolutionise Financial Analysis
As artificial intelligence reshapes the investment landscape, institutional investors who harness its power stand to gain a significant edge.
In its latest report, Moody’s looks at ways AI is set to revolutionise the financial analysis.
AI: More than a trend in financial analysis
The integration of AI into financial analysis is not merely a trend, but a seismic shift that is reshaping the entire industry.
From automating routine tasks to uncovering hidden patterns in vast datasets, AI is enhancing the accuracy of forecasts, improving risk management and optimising investment portfolios.
However, the adoption of AI in financial analysis is not without its challenges. It requires significant capital investment, highly skilled teams, and close collaboration among multiple stakeholders.
This complexity is creating a divide between tech-savvy investors who can effectively leverage AI and those who lag behind.
“Effective AI strategies will prioritise applications with proven track records,” says John Smith, Chief Investment Officer at Global Asset Management.
“When asset managers use AI to make predictions, they face the challenge of models becoming less reliable as they approach investment decision-making because patterns observed in financial markets change quickly.”
One of the most significant impacts of AI on financial analysis is the potential for improved efficiency. Large language models, such as OpenAI's GPT and Anthropic's Claude, are delivering substantial productivity gains.
These models can process vast amounts of text data, including annual reports, debt documentation and broker research, much faster than humans.
“Large language models can automate the creation of documents like earnings reports or market commentaries and generate investment ideas,” explains Sarah Johnson, Head of AI Integration at Tech Investments Ltd.
“Moreover, they can assist in writing code, enabling investors to design small applications tailored to their needs.”
While large language models are becoming increasingly accessible, their widespread availability means that using these models alone will not provide higher returns.
Achieving outperformance requires implementing more traditional AI models, which are more challenging to deploy as they need in-house training and regular maintenance.
The combination of traditional and large language models is where tech-savvy investors are likely to set themselves apart.
“Investors who can overcome the challenges to combine traditional and large language models will be able to set themselves apart, increasing the likelihood of peer outperformance,” Sarah adds.
These models can process vast amounts of text data, including annual reports, debt documentation and broker research, much faster than humans.
“Large language models can automate the creation of documents like earnings reports or market commentaries and generate investment ideas,” explains Sarah Johnson, Head of AI Integration at Tech Investments Ltd.
“Moreover, they can assist in writing code, enabling investors to design small applications tailored to their needs.”
While large language models are becoming increasingly accessible, their widespread availability means that using these models alone will not provide higher returns.
Achieving outperformance requires implementing more traditional AI models, which are more challenging to deploy as they need in-house training and regular maintenance.
The combination of traditional and large language models is where tech-savvy investors are likely to set themselves apart.
“Investors who can overcome the challenges to combine traditional and large language models will be able to set themselves apart, increasing the likelihood of peer outperformance,” Sarah adds.
AI: Leveraging alternative data
One area where AI is making significant inroads is in the use of alternative data. As conventional data sources represent only a fraction of available information, more investors are turning to alternative data from sources such as social media, online retail websites and satellite imagery to gain an edge.
“Alternative data often lacks structure, making it challenging to analyse using conventional methods," notes Michael Brown, Data Scientist at Quant Solutions.
“However, advancements in AI algorithms, coupled with lower computational and data storage costs, now enable the conversion of alternative data into interpretable signals for investors.”
The integration of alternative data can significantly enhance the investment process, but it's not without its challenges. Selecting the right dataset is difficult due to the plethora of options available, and its value depends on factors such as asset class, time horizon and investment strategy.
As AI transforms financial analysis, it's also expanding the risk assessment landscape. The efficiency gains from AI are reducing the cost of risk assessment, enabling analysis of smaller borrowers previously overlooked due to profitability constraints. This trend could further fuel the growth of private markets, notably private credit.
“AI could streamline the analysis of financials and legal documents, which are less standardised than in public markets, and facilitate investment valuation,” explains Emma Thompson, Head of Private Credit at Capital Investments.
Moreover, AI and alternative data are allowing investors to measure risk beyond the level of the organisation, drilling down to individual assets. This granular approach to risk assessment is particularly valuable for accurately assessing certain risks, such as climate-related hazards or political upheavals.
“The next frontier in risk assessment will be to map relationships between assets from different companies to reveal an interconnected view of potential vulnerabilities,” Thompson concludes.
“This approach would help to uncover hidden risks and dependencies across industries and geographies, providing a more comprehensive understanding of risk in an increasingly complex global economy.”
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