The cost-of-living crisis is creating a double-edged sword for financial advisors. While an influx of clients seeking guidance might seem positive, unfortunately, it coincides with advisers already facing significant time constraints. This pressure to serve more clients while juggling limited resources could prove challenging for long-term sustainability and growth.
AI technology can relieve some of that pressure by freeing up their time. In particular, AI can automate several daily tasks that a financial adviser would undertake. Let’s unpack this further.
Since ChatGPT’s launch in November 2022, many employers have tried to replace entire job functions using AI. The National Eating Disorder Association (NEDA) intended to replace 6 employees and 200 volunteers, ending in disastrous results. Major Hollywood studios have also refused to rule out replacing writers with generative AI – spawning strikes across 2023.
Does generative AI’s potential threaten the role of financial adviser? The typical financial adviser has years of financial planning expertise, which generative AI currently struggles to replicate. Simply put, AI can’t replicate the soft skills necessary for financial advising (e.g. maintaining a client-centric approach, networking to increase their client base, etc.).
Therefore, the focus should be on removing the repetitive administrative tasks from the calendars of financial advisers. In particular, instead of replicating what financial advisers excel at, eliminate the repetitive admin tasks that occupy hours of their time each week. A Square Mile Investment Consulting and Research study found that administrative tasks are financial advisers’ greatest time burden. The Financial Times article covering these results notes that the admin burden might compromise the quality of client relationships.
Stripping away some of the admin tasks will create new possibilities for improving client relationships – as in producing faster communication, more informed advice and more. In addition, taking a ‘process-by-process’ rather than an end-to-end approach means that humans – rather than algorithms – get the final sign-off on client communications. The result? Financial advisers and AI algorithms can engage in what they excel at– sophisticated strategising and communication and repetitive, low-skilled, back-office tasks, respectively.
Now, let’s turn to which admin tasks AI can successfully automate. Whilst its implementation in recruitment (e.g. screening candidates, etc.) has been highly controversial, here are a few examples where specialised AI models can deliver effective results.
Are you looking for ways to reduce operational costs? Automated data extraction is an ideal place to start. Manual data extraction drains the bottom line by wasting time and introducing human errors. As Irina Staneva, a former auditor at PwC, noted, “Data extraction, when done right, takes a lot of time and requires a lot of effort and focus by the employees. Despite this, it is not uncommon to have inaccuracies and mistakes. Cleaning the data from them is another time-consuming process.”
Now, companies can deploy specialised AI models (not ChatGPT) to extract data, making it accessible in their desired format. But data extraction is only the beginning. We can now use AI to ‘read’ financial reports – extracting the relevant data and leveraging it to calculate key financial ratios, such as EBITDA, OPEX, gross profit and long and short-term debt. In other words, AI isn’t just extracting data – it’s computing it.
Financial advisers can always complete these tasks – but why should they when there’s a convenient and cost-friendly alternative?
Know Your Client (KYC) is an essential part of client onboarding. Analysts can save several hours weekly by establishing a workflow that scrapes data from relevant sources and structures it into client reports.
For example, a financial adviser can quickly scrape data from a client’s financial documents and match it against those registered in Companies House. However, doing this repeatedly is dull and time-consuming for them – but quick and efficient for AI algorithms.
If deployed successfully, KYC processing costs will plummet, meaning analysts will reclaim their time for more interesting, client-centric projects.
Let’s weigh AI's benefits for financial advisers against the potential disadvantages – starting with the benefits.
Back-office costs are a part of the price of doing business. Yet, AI can now trim operating expenses by reducing back-office costs. Case in point – an HFS report suggested that Novuna (formerly Hitachi Capital)’s approach to automating repetitive cross-silo processes is set to save them millions of pounds.
AI projects can deliver cost savings through several avenues, including:
These are counter-balanced against:
When correctly implemented, the costs of many AI tools are significantly lower than manual alternatives.
AI can convert unstructured data into structured insights in seconds, saving advisers several hours each week. The Financial Times explored Square Mile Investment’s research, suggesting that by automating admin tasks, advisers can prioritise ‘sitting with them [clients] and going through their financial affairs’.
Enhanced client data tracking frees advisers to spend more time analysing the data to provide more insightful feedback faster. This way, advisers and analysts can focus on activities that deliver higher value to their clients rather than getting the initial data right (e.g. more in-depth analysis, seasonality, business dynamics, forecasting, etc.).
When well-considered and fully integrated, AI can offer numerous benefits for financial advisers. Implementing such technology raises complex questions surrounding consumer duty protection and longevity.
Relying solely on manual methods for data processing and analysis can lead to overlooked insights. So, AI tools that extract insights from existing datasets can be a game-changer for analysts and advisers. These tools can help us unlock the full potential of our data and gain a deeper understanding of it.
Yet, analysts and advisers must take ownership of the insights unearthed by AI. As the Financial Times suggested, consumer duty regulation addresses the quality of the advice, not its origination. Whether generative AI (or the adviser) spawned the advice, it still must prevent any harm to customers and assist in achieving their financial objectives.
Therefore, companies must embed generative AI into a workflow where a human signs off on the final product. Firms such as Microsoft and Air Canada, which have embedded customer-facing generative AI that went awry, have since faced public backlash and even legal action. In these cases, their AI models generated inaccurate or misleading information, highlighting the importance of human intervention to ensure the final product's accuracy and appropriateness.
Denying the use of generative AI is not the solution, either. A hybrid advice solution will yield many benefits, but building trust is a soft component of the financial advice process that cannot be automated.
One concern when investing in AI is that it will become obsolete after implementation. From the adviser’s perspective, there are several ways to avoid technological obsoletion:
The future of technology for financial advisers is fraught with possibilities, but the integration and development of AI and machine learning-related technologies remain unclear. Taking a proactive approach to embracing technological changes can unlock significant short- and long-term competitive advantages.
AI is constantly becoming faster, more accurate and versatile. Fintech providers are continuing to repackage AI algorithms into more sophisticated financial applications. Broadly, these applications include more personalised advice and accurate projections, along with faster and more sensitive risk and fraud detection.
Let’s return to the question: Can generative AI replace financial advisers?
AI’s ability to assist in providing fast, insightful financial advice continues to push the financial advice sector towards innovation that it may (or may not be) ready for. For example, the Financial Conduct Authority stipulates that ‘Where appropriate, users, impacted third parties and actors in the AI life cycle should be able to contest an AI decision or outcome that is harmful or creates material risk of harms’.
Yet, the same report notes that ‘[regarding openness and transparency] We are considering how best to address these issues.’ This approach is, therefore, reactive rather than proactive.
Innovation in the financial advice sector must align with a narrative that reveres transparency and accountability by prioritising human experience and expertise. While AI innovations promise many benefits, these benefits are best realised when prioritising expert oversight to safeguard client experiences.
AI offers shortcuts for time-pressured financial advisers, including automating admin tasks and saving costs from start to finish. Implementing AI into workflows prepares financial advisers for an unpredictable yet exciting technological future. Though financial advisers rely on human-to-human communication, well-implemented AI has an invaluable role in the financial advice sector.
Financial Statements AI extracts data from financial reports, leveraging it to compute key ratios. Try it today by booking a demo or emailing our team at hello@evolution.ai.
