Financial Services

The Do’s and Don’ts of Incorporating AI into Your Financial Forecasting Model

Artificial intelligence (AI) has revolutionised the way businesses operate, especially in the realm of financial services. Traditional forecasting models have their limitations. True, they consider historical data and project them into the future, but the datasets are relatively small and siloed, and that can lead to inaccurate predictions. AI models can consider many more variables and subsequently, the AI-driven predictive analytics tools make more accurate and adaptable forecasts. Incorporating AI into financial forecasting models has already proven to enhance accuracy, efficiency and decision-making.

However, to reap the benefits of incorporating AI, it is essential to navigate this integration carefully. When AI forecasting models are implemented correctly, errors can be controlled to less than 5 per cent.

Incorporating AI into financial forecasting model: The Do’s and Don’ts

Here are the do’s and don’ts of incorporating AI into financial forecasting models.

The Do’s:

1. Understand your data: Before implementing AI, ensure the data is clean, relevant and sufficient. AI relies heavily on data quality for accurate predictions. And any data is only as good as its source and the methods of collection. Further, if data quality degrades over time, the AI model’s performance declines too. It is important to retrain the model constantly by using as recent data as possible.
2. Go granular: While many organisations implement a top-down model while forecasting, others use a bottom-up approach. A top-down approach involves focusing on the financial projections of high-performing organisations while a bottom-up approach begins at the bottom. Going into the details helps identify problem areas more easily whenever a disruption occurs.
3. Define clear objectives: Clearly define what the organisation wants to achieve with AI in financial forecasting. Whether it is improving accuracy, reducing errors or enhancing speed, setting clear objectives is crucial. This can be best achieved if the key stakeholders and data engineers agree on the objectives before the model is built. Further, a well-defined strategy map would enable the relevant personnel to interpret the forecast metrics easily.
4. Invest in the right talent: Hire or train staff with the necessary skills to understand and leverage AI technologies effectively. This includes data scientists, AI engineers and domain experts.
5. Start small: Begin by integrating AI into a specific aspect of the organisation’s financial forecasting model. This allows for easier implementation and evaluation.
6. Regularly evaluate and improve: It is important to continuously assess the performance of the AI system and make necessary improvements based on feedback and new data.
7. Maintain a benchmark to detect anomalies: AI models, more often than not, cannot detect disruptive events such as a market crash, or an earthquake or a pandemic, and forecasts during such events can be inaccurate since AI would assume the anomaly happened under normal circumstances. By maintaining a performance benchmark, the team can be alerted whenever the forecast exceeds or even hits the benchmark. During such an event, the data team can make the necessary adjustments to the model.

The Don’ts:

When AI models are interpreted incorrectly, the forecasts they make can harm the organisation’s performance.

1. Don’t neglect human oversight: While AI can automate many processes, human oversight is still crucial. Always have experts review AI-generated forecasts to ensure accuracy and relevance. Internal financial analysts would know immediately if an AI-driven forecast is reasonable or not.
2. Don’t use boxed products: When organisations build their own AI-driven forecast models, it is easier for data teams to understand and troubleshoot the models whenever there is a problem. However, pre-packaged products may not produce accurate results since they could be based on predetermined conditions.
3. Don’t ignore ethical considerations: AI in financial forecasting raises ethical concerns, such as data privacy and bias. These issues need to be addressed proactively to maintain trust and compliance.
4. Don’t overlook security: Protect organisational AI systems from cyber threats. Implement robust security measures to safeguard sensitive financial data.
5. Don’t rush implementation: Rushing the integration of AI into financial forecasting models can lead to errors and inefficiencies. Spend time on planning and executing the integration properly.
6. Don’t expect accurate forecasts from day one: AI forecasts are expected to be about 70-80 per cent accurate at first, not 100 per cent. Over time, the model improves with real-time data and fine-tuning. Don’t discard the model if the results are less than perfect at first.  
7. Don’t disregard regulatory compliance: Ensure the AI system complies with relevant financial regulations and standards. Failure to do so can result in legal issues and financial penalties.

Benefits of AI forecasting over traditional models

Incorporating AI into financial forecasting models can yield significant benefits when done correctly. The key differentiators between the two methods of financial forecasting include:

Volume and type of data: AI and machine learning (ML) models are not limited by the quality, type or volume of data they can receive and analyse. AI and ML models work as efficiently as the data that they are fed with. When designed and trained effectively, AI forecasting models can consider internal and external performance variables, such as stock market conditions, macroeconomic conditions and even weather effects. Traditional models, however, are limited by the quantity and type of data they can work with.

Adaptability: AI-driven models are self-learning so each time new data is fed, the model adjusts and re-forecasts. AI models can identify correlations that are often not obvious to human analysts. Traditional models, on the other hand, must be recalibrated manually whenever circumstances change, limiting their speed and accuracy.      

Automation: AI-driven systems automatically aggregate data from historical, external and internal sources enabling the models to be trained with a rich storehouse of data. The models are continuously fed with real-time data from existing and new data points. And they can detect even subtle differences and similarities across data sets.

To realise and benefit from AI’s transformative benefits, organisations must focus on its careful incorporation and governance. The success of AI models depends on transparency, ethical practices and sound strategies. Organisations that implement AI judiciously in their financial forecasting models are sure to gain a distinct competitive edge over their competitors. 

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