Using AI to augment pricing intelligence for banks

To deliver a solution that met the needs of different-sized clients, and to capture nuances for different industries, the EY team conducted a series of interviews with the bank’s RMs. These interviews gave EY relevant insights into how the RMs viewed their own pricing decision-making behavior, their approaches to pricing, and their use of existing tools and technology.
 

These interviews included questions, such as:

• When do you start to discuss prices with clients?
  
• What data do you use to decide on a price?
  
• How do you justify the price to clients during the negotiations?
  
• How do you optimize returns?
  
• Do you discuss pricing negotiation strategies with colleagues?
   

This primary research was used to establish customer experience principles to assess the design of the solution to help ensure that it met RMs’ needs and expectations. The principle goal in designing the solution with RMs was to ease the barrier to adoption that exists for any new technology launched to busy RMs.
 

In the second phase, the EY team developed the minimal viable product (MVP) in a series of two-week sprints, incorporating RMs’ feedback in each sprint to optimize the final MVP product.
 

New ways of thinking 

With these principles, the EY team developed an AI-powered digital product, the Smart Advisor (SA).
 

Smart Advisor, a digital application enabled by AI, utilizes historical pricing data and the bank’s deal-structuring expertise to give RMs real-time, contextually relevant pricing intelligence. This helps them to provide better service to their clients while at the same time optimizing returns for the bank.

Smart Advisor enables RMs to unlock greater insights through two main capabilities:

Benchmark

Smart Advisor show RMs how their deal compares with similar booked deals for credit products from the bank’s back book. This allows the RM to instantly understand recent prices and deal structures for similar clients with similar needs. The tool visually shows the range of prices, helping the RM to see if their deal is priced within expected bounds. The RM has the ability to adjust the margin directly using Smart Advisor, which automatically updates the return calculation.

Additionally, the ability to instantly compare the current deal to existing deals provides a digital control to identify pricing anomalies prior to deal closure to support the bank’s conduct and compliance agenda.

Solver

Solver makes suggestions on how to adjust a deal structure to improve returns for the bank. For example, if the deal is below the bank’s target return, then Solver will proactively identify which return levers (margin, fees, tenor, etc.) can be adjusted and by what amount, to hit target. Like Benchmark, RMs are able to adjust the return levers and instantly see the impact on return to iterate the deal parameters and optimize the return.

A better pricing strategy

Smart Advisor helps banks take advantage of AI and leverage their data to produce valid insights for their RMs to structure and price deals that are beneficial for both the bank and their clients. In addition, RMs who use the tool can find a deal structure and price in a single step, rather than through multiple iterations. As a result, the RM can quickly explore different deal options to a find a product that is a fit for their client.

An increasing number of banks are seeking a competitive edge by introducing AI-enabled tools like Smart Advisor. These use portfolio data and analytics to provide real-time insights and support to their RMs in their pricing negotiations. In the end, pricing is crucial for RMs to maintain successful, long-lasting client relationships and build greater value creation.