Financial models are more than a collection of numbers and formulas. They’re drivers of decisions, change and our future. That’s why we strive to stay at the forefront of financial modelling innovation, regularly exploring new technologies to enhance how we deliver services to our clients.
AI has yet to really make its presence felt in financial modelling. But soon, it will. When it does, it will reshape traditional approaches and open up new possibilities (and pitfalls) for financial analysts and decision-makers alike.
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How might AI change financial modelling?
Artificial Intelligence (AI) has emerged as a game-changing force in many industries. From machine learning algorithms that can process vast datasets, to natural language processing tools that can extract insights from unstructured financial reports, AI is poised to revolutionise how we build, interpret, and use financial models.
AI in financial modelling is likely to enable us to streamline complex concepts and simplify data-heavy processes. It will make financial modelling more efficient and cost-effective, which means that organisations of all sizes will be able to benefit from advantages currently only available to larger corporates.
Improved workflow efficiency
The manual workload involved in collecting, collating and checking financial data for errors is enormous. AI will be able to alleviate this by rapidly processing vast amounts of financial data, including market trends, company financials, and economic indicators.
Not only this, but AI models will be able to automatically update and refine themselves as new data becomes available, ensuring financial models remain current and relevant without constant manual intervention. Automating routine aspects of financial modelling will free up time for financial modelling consultants to focus on higher-level strategy and interpretation.
Enhanced accuracy and precision
AI can identify patterns and correlations that might be missed by human analysts, enhancing the usefulness and applicability of financial models. AI models are likely to be able to forecast financial outcomes with greater accuracy by considering a more comprehensive range of variables and complex interrelationships, reducing human error and bias, and enhancing the overall accuracy of financial models.
Whilst this is a tempting proposition, it’s crucial to understand that even the most highly-trained AI financial modelling will still need to be checked and validated by an actual human to ensure accuracy. Only a financial analyst can understand the nuances of what the financial model is trying to achieve and, therefore, how to spot errors in the final product.
Improved risk management
AI will likely be crucial in financial model audits and internal model reviews. Currently, model audit firms use a combination of software checks and human review. This will likely continue, but the range of checks that AI model audit tools can perform will be considerably greater and more useful than current software tools. One example might be document review, where humans currently review contracts and project documentation and compare them against what’s in a financial model. AI tools will likely do some of this in the future.
Model build
AI-based financial model-building tools are already in development. One example is Openbox, a cutting-edge tool designed to automate the process. It leverages AI technology to allow users to describe their project in plain language, and then it constructs a full, Excel-based financial model based on those inputs. This significantly reduces the time and effort typically required for model creation, enabling the completion of complex financial models in minutes instead of days.
Openbox is particularly useful for financial professionals who need to build models frequently, as it simplifies the process through a conversational interface. Users can speak or type their requirements into the system, and the AI interprets these instructions to generate a customised model structure.
These tools are likely to become increasingly capable of building all kinds of financial models and change the way we approach financial model building.
Scalability
AI systems can process and analyse vast amounts of financial data that would be impractical or impossible for human analysts to manage manually. This capability allows for more comprehensive models incorporating a wider range of variables and data points, permitting financial analysts to apply sophisticated modelling techniques to a broader range of situations without a proportional increase in time or resources.
Advanced predictive analytics
AI models can process streaming data and update predictions in real-time, allowing for dynamic financial forecasting. This is particularly valuable in fast-moving markets or during periods of economic volatility. Through natural language processing (NLP), AI can analyse unstructured data sources like news articles, social media, and earnings call transcripts. This allows for sentiment analysis and other qualitative factors in predictive models. Financial analysts can develop more sophisticated and timely predictive models by tapping into these capabilities, leading to better-informed decision-making and strategy development.
Are there any risks to consider?
Robots are only as accurate as the data and information you feed them. If someone tries to use AI for financial modelling with little experience with collating financial data, then the model they build could be fraught with error, and that could cost dearly.
Data quality and bias
AI models are only as good as the data they’re trained on, and in the financial world, this data can often be incomplete, outdated, or skewed. If historical financial data reflects past biases or market anomalies, AI models may perpetuate these biases in their predictions, leading to flawed financial forecasts. It’s crucial for financial analysts to critically assess and clean their data inputs, regularly audit AI models for bias, and supplement AI insights with human expertise and diverse perspectives.
Model opacity and explainability
A significant challenge in implementing AI for financial modelling is the issue of model opacity and explainability. Many AI models, particularly those based on deep learning, can be highly complex and difficult to interpret. This “black box” nature means that understanding how the model arrives at a specific decision or prediction can be challenging, leading to potential issues in transparency and accountability. Financial modellers must often explain and justify their decision-making processes to regulators. The opacity of AI models can make this difficult, potentially leading to compliance risks. As a result, there’s a growing emphasis on developing more interpretable AI models and implementing techniques to enhance model explainability in financial modelling applications.
Overfitting and model generalisation
AI models, especially those trained on large datasets, can sometimes become too tailored to the specific data they were trained on, a phenomenon known as ’overfitting’. This can lead to models that perform well on historical data but poorly in real-world applications. AI models may struggle to generalise to new, unseen data, particularly in volatile or rapidly changing financial markets, leading to unreliable predictions. An overfit model might, for instance, fail to adapt to sudden market shifts or economic disruptions, potentially resulting in significant financial losses.
Security and privacy risks
Financial models often require access to sensitive and confidential data. Using AI introduces risks related to data breaches, cyberattacks, and unauthorised access, which could compromise the security of this information. The extensive data collection required for training AI models can raise privacy concerns, particularly if personal or sensitive information is involved. Ensuring compliance with data protection regulations (e.g., GDPR) is critical and a vitally important aspect when considering the role of AI in financial modelling.
Summary
Whilst the role of AI in financial modelling will become ever more prominent, we will need to bear in mind the risks and flaws outlined above. Yes, AI can help streamline processes and speed up the more routine tasks involved in financial modelling. Still, it shouldn’t and won’t replace the insights and broader scope of knowledge that trained professionals have spent years perfecting. It will, however, allow financial analysts more scope to focus on strategic decision-making and forecasting whilst leaving AI to the heavy lifting of data processing and analysis.
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