7 reasons why Ecommerce beats Financial Services- WM

AI RECOMMENDATION SYSTEM

Financial Services and Wealth Management firms continue to face significant revenue and margin pressures as well as market volatility.

THE NEW BATTLEGROUND

Against this backdrop, the new battleground is customer.

In an environment where there is a growing focus on providing holistic and hyper-personalized solutions, AI is enabling firms to provide services ranging from self-service to human advice, based on client preferences.

Using the assets they already hold, financial institutions can understand customer needs, develop customer engagement strategies based on real data generated by their own customers, and combine the human touch with technology.

But implementing it is more complex than in other sectors 💪

👉REASON 1: Data privacy regulations.

You as bankers need to implement solutions to ensure the proper protection of their customers' data as well as keep compliance with cross border rules.

👉REASON 2: The classic recommendation engine in e-commerce is usually customer-centric.

In the banking sector, you have to control the risk position: the product and its features therefore come into play.

So a mix of investors and other items around product need to be taken into consideration

👉REASON 3: Given the volatile market conditions, predictions are only relevant for a limited time.

In e-commerce, except for seasonality or promotion, the environment is quite stable.

In investment, you have an unstable environment of the market and investors acting together in the market.

👉REASON 4: Investors may act differently when exposed to the same signals and create a number of investment strategies.

There can be heterogeneity in the client and product mix leading to heterogeneity in your investment strategy.

👉REASON 5. In financial services, you can't rely only on historical data to make decisions, otherwise your system is over-adjusted by past data.

👉REASON 6: Building a baseline that serves as a reference for determining the performance of such an algorithm is challenging.

The performance of your system will require in an ideal world a deep knowledge of the entire corpus of investors and items (including from the environment) for a human to solve this recommendation. Building a solid benchmark and data set will be difficult.

👉REASON 7: Investors expectations

Leverage technology to enhance and improve human contact, not replace it.

49% of HNW investors prefer to communicate through a face-to-face meeting. In e-commerce, there is more potential to push your recommendation with a 100% digital touch.

TO MOVE FORWARD IN FINANCIAL SERVICES & WEALTH MANAGEMENT

🎯Tailor your recommendation system to your private banking context to provide relevant predictions

🎯Evaluate the performance of out-of-the-box solutions capable of making recommendations in the specific context of your bank

🎯Build strong data capabilities and prepare computing capabilities