Can AI help retail customers get HNW-like wealth advice?

Bankers often reserve their financial advice for high net-worth (HNW) individuals, those who can make investments worth a few hundred thousand in USD, EUR, or GBP at a time.

It’s not like they don’t want to help others — the fact is, they’re just too busy to make time to understand and assess the needs of retail customers and advise them on the best investment vehicles and products that would suit their risk portfolio.

However, it seems as though artificial intelligence (AI) can make that possible. According to a recent report by the World Economic Forum (WEF), AI allows wealth advisers to provide personal and targeted investment advice to mass-market customers in a cost-effective manner.

It’s a great opportunity for financial institutions as AI and automation solutions can help them decrease the cost of serving customers through reduced human capital costs, and faster timelines, while also boosting customization and personalization of advice.

How AI transforms wealth managers

AI makes it possible for wealth managers to ‘become hyper-efficient’. As a result, they’re able to get machines to do the number crunching and literally gain insights about customers on the fly, allowing wealth managers to ‘personally’ serve the lower-end of the market.

Financial institutions also have an incentive to enable their advisors with smart AI tools. Their ability to serve more customers (especially those from retail markets) will help create a strong competitive advantage, and draw more ‘investments’ into the institution’s overall portfolio.

“Improved wealth-management advice can serve as a differentiator for mass-market customer segments that are traditionally under-served by advisers,” said the WEF.

Another advantage that AI will create for wealth managers is that it’ll help them reduce costs, especially when it comes to administrative, management, and compliance expenses.

Every financial services institution hires back office executives in droves because they need the manpower to create hundreds of thousands of reports and fill-up endless regulatory filings. AI can automate it all.

It’s why the WEF insists that AI will combine high efficiency with low fees to allow institutions to reduce expense ratios and remain competitive. Here are three things that AI will help financial services institutions do more efficiently:

# 1 | Equip advisers with highly personalized insights

Bankers in retail banking branches have a lot of customers to deal with, as a result, they are limited in their ability to serve each one individually. This gap is often extended by other disconnects (e.g. age, lifestyle) between advisers and their clients.

However, using advanced analytics dashboards, wealth managers in banks can provide detailed insights into clients’ needs and enable easy calculations to optimize products, services, and advice.

This can help expand the branch’s capabilities to provide personal – and potentially niche – advice to clients.

# 2 | Share more detailed economic insights

With limited access to data, wealth managers find it difficult to offer the kind of detailed and specific financial advice customers are looking for. As a result, the (generic) advice they end up providing lacks differentiation and is therefore not interesting to customers. This, in turn, leads to low customer stickiness.

According to the WEF, econometric indicators can combine economic datasets and market events to provide customers with relevant insights and data on macroeconomic trends; customers will tend to stay with the platform that houses their historic data and consequently delivers stronger insights.

# 3 | Enable users to effectively manage their investments

Mass-market investors are usually more susceptible to “beginner traps” (e.g. buying high and selling low, impulse investing, tax inefficiencies), as they don’t have anyone to guide them.

Cross-product analysis can use machine learning to look across a customer’s financial products and automatically optimize areas of improvement (e.g. suboptimal savings allocations).