Our latest blog post discussed how price discovery for the options used in structured products is one of the biggest pain points for banks issuing personalised investments, as highlighted in our recently published report, A Step-by-Step Guide to Offering Bespoke Investment Products to Retail Clients. In this second blog post in the series, we skip forward to the final stage of the product journey to explore some of the challenges a bank faces when managing the lifecycle of an investment. We also explain how an automated solution eases the burden on product and sales teams.
Meeting customer expectations
When a bank issues a personalised investment to a customer, it must support the product throughout its lifecycle. That process breaks down into three stages: maintaining an updated valuation so a bank can provide timely and accurate reports, issuing payment (if the underlying asset generates an income) and ultimately settling the investment if the customer has earned a return.
Having an accurate valuation is important because at various points during the lifetime of the investment, a customer will want to know how it has performed. Valuing assets like a share is straightforward as its price either rises or falls. However, structured products are more complex because the payoff is nonlinear. Unless the customer is a sophisticated investor, they are highly unlikely to understand the calculations behind the valuation.
Before going any further, it is worth mentioning that structured products trade on both primary and secondary markets. On the primary market, a sell-side institution such as an investment bank issues the product directly to the buyer. Valuing products on the primary market is simpler as the buyer can compare quotes. The process becomes more intricate on the secondary market where prices depend on what traders are willing to pay and receive, known as the ‘bid’ and ‘ask’. Additionally, the buyer is tied to the issuer on the secondary market, so it must accept a price without knowing whether it is a fair valuation.
A bank requires a pricing engine to work out the value of each investment to allow it to meet the needs of the mass market. Relying on an Excel spreadsheet to manually calculate valuations is inefficient because the process could take hours, depending on the product’s complexity, not to mention unrealistic if a bank wants to scale the offering. It also needs the capacity to produce a report to demonstrate to customers how their investment has performed, including an easily digestible visual aid like a graph and key information such as whether a barrier was breached, coupon payments and accrued interest. A relationship manager should have the means to export the report so it can be shared as an email attachment.
Another challenge for banks is managing the incoming and outgoing cash flows they generate, especially when issuing personalized investments on a large scale. Tracking cashflows is particularly important as a structured product is reaching maturity because ideally, the sales team would like to recycle the position. However, predicting when the funds will land in a customer’s account can be hard because in some cases it may make sense to sell the position on the secondary market and take a profit. If the customer holds the product until maturity, those funds should arrive roughly eight days after the trade closes. That gives the team a specific window to persuade the client to sign up for a new investment.
Automation is the answer
Futora’s platform automates the processes of valuation and cash flow management, saving a significant amount of time and allowing a bank to redirect resources to higher-value activities.
Its functionality does not end there. The platform has a built-in analytical tool which helps a bank manage each investment. This tool quantifies the risk/reward ratio of a product on both the primary and secondary markets to confirm it meets the needs of the customer. Making sure an investment is suitable for its target market is a key requirement of the Markets in Financial Instruments Directive (MiFID), one of two EU regulations governing structured products (which we cover in our report).
The platform can also forecast the potential performance of an investment in the time left before the option matures. It uses a mathematical technique known as a Monte Carlo simulation to map out multiple trajectories for how the investment could perform and a valuation for each scenario. Having access to this data helps customers decide if they should hold on to the investment until maturity or exit the position early and take a profit.
