Paul Nixon, Head of Behavioural Finance at Momentum Investments Group

We’ve seen firsthand how the financial advice landscape is being transformed by the rise of Artificial Intelligence (AI) and machine learning. These technologies offer a powerful new tool for advisers to better understand their clients’ behaviour and provide more personalised, informed advice.

In an evolving financial world, where the financial decisions of clients are influenced by so many factors, it is crucial to stay ahead by embracing these advancements. But to understand the technology, you first need to grasp the impact of behaviour and the tax it imposes on us.

What is behaviour tax?

The concept of ‘behaviour tax’ is central to understanding the impact of emotional and cognitive biases on investment returns. Essentially, behaviour tax refers to the cost incurred by investors who make decisions based on short-term emotions rather than long-term strategies. This tax is not a literal levy but a figurative one, representing the loss in returns due to ill-timed investment decisions, like panic-selling during market downturns or chasing past performance.

Our role is to help financial advisers minimise this behaviour tax for their clients by guiding them through the emotional rollercoaster of investing and financial management. But how do we do this effectively? This is where machine learning comes into play, offering insights into client behaviour patterns and helping us predict and mitigate potential pitfalls.

The power of machine learning

If you’re familiar with AI, you’ve likely heard about machine learning as well. Machine learning is a branch of AI that enables systems to learn from data, identify patterns, and make decisions with minimal human intervention.

Machine learning can be divided into two main types: unsupervised and supervised learning. Unsupervised machine learning helps identify behavioural structures by clustering data into meaningful patterns. By unravelling and understanding these patterns, we can help advisers tailor their advice to suit each client’s unique psychological profile.

On the other hand, supervised machine learning algorithms are designed to predict future behaviour. These algorithms can analyse historical data to forecast how a client might react to specific market conditions. For example, they can predict when a client is likely to switch investments based on factors like portfolio value, age, or past switching behaviour.

This predictive capability allows advisers to intervene before clients make decisions that could negatively impact their returns.

To make these predictions, we first have to immerse ourselves in the underlying investment behaviours.

Understanding investor archetypes

One of the significant contributions of machine learning in financial advice is the identification of investor archetypes. Through analysis of data from platforms like Momentum Wealth, we’ve identified four distinct behavioural patterns among investors:

1. Market timers: These investors are highly active, frequently switching investments in response to market fluctuations. Unfortunately, this behaviour often results in significant value destruction as they chase past performance rather than sticking to a long-term strategy.

2. Assertive investors: Assertive investors are characterised by their tendency to up-risk their portfolios based on past performance, leading to high behaviour taxes, especially during volatile periods.

3. Anxious investors: These individuals are risk-takers but become quickly unsettled by paper losses, prompting them to move investments to safer, lower-performing options, thus derailing their long-term goals.

4. Avoiders: Typically conservative, avoiders tend to stick with low-risk investments, missing out on potential gains from more aggressive strategies. Their behaviour tax comes in the form of opportunity cost.

By recognising these archetypes, we can help financial advisers offer more targeted advice, steering clients away from behaviours that lead to poor outcomes.

AI as an adviser’s ally

AI is not here to replace financial advisers; it’s here to enhance their expertise by augmenting their capabilities. With tools like AI-driven longevity predictions, advisers can better advise clients on whether to opt for life annuities or living annuities based on health data and behavioural patterns. This frees up their time to focus on what truly matters to their clients — addressing challenges and reaching their financial outcomes.

By understanding the structure of behaviour through unsupervised machine learning, and predicting future actions with supervised algorithms, we can help advisers proactively manage client relationships, reducing the likelihood of impulsive decisions that lead to financial loss or even ruin.

The integration of AI and machine learning into financial advice is not a future possibility; it’s a present reality. Advisers who fail to adopt these technologies risk being left behind in a rapidly evolving industry. As clients become more informed and demand more personalised, behaviourally informed advice, our ability to leverage AI will be a key differentiator.

By reducing the behaviour tax, improving client outcomes, and deepening our understanding of client psychology, AI empowers advisers to offer advice that is not only more accurate but also more human. The future of financial advice is here, it’s human and machine and it’s time to get on board.

ENDS