The better the question
Is AI smart enough to deliver real-time insights to relationship managers?
Relationship managers need an expanded suite of technology tools to match their clients’ needs today.
The role of a commercial bank relationship manager (RM) is to build long-term relationships with clients and support them to grow their businesses through banking products and financial services advice. The RM plays a crucial role in building a strong level of trust and partnership between their clients and the bank. This is essential to personalize client service, improve client experience, and grow the bank’s relationship with clients.
Banks rely heavily on RMs to decide the most suitable products to meet clients’ needs and to negotiate loan terms, helping to maximize returns. However, the pressure to improve returns is growing in an increasingly competitive and price-sensitive market. In many banks, existing pricing tools are not designed to proactively assist RMs to make better decisions and, as a result, they do not achieve optimal returns.
In deciding the right price for clients, RMs need to:
- Understand the client’s needs by gathering essential information
- Match the client’s needs to the most suitable product or products, while assessing the level of risk
- Use pricing tools to assess the risk-weighted returns
- Negotiate the deal price with the client
The need to create a decision support system whereby RMs can provide clients with faster and more accurate pricing – while also maintaining profitability – was the focus of one global bank looking to enhance the role its RMs could play in this space. The bank’s main objective was to provide RMs with new tools to help them optimize profitability from loan products.
Ensuring that the price is right
The bank decided that to help its RMs secure the best possible deal price for clients, it needed to take greater advantage of enterprise data assets that could provide more relevant insights at the right point in the pricing decision-making journey.
Traditionally, the bank’s RMs had to complete a manual pricing modeling process when pricing a competitive deal for clients. This time-consuming process meant that RMs would spend more time trying to figure out the deal parameters, rather than building better relationships with clients. Augmented intelligence assistants will change the way RMs use technology. Real-time, contextually relevant insights will help RMs to provide better service to their clients and drive improved business outcomes.
By working together, the EY team and the bank set out to design and build a customized, AI-based decision support system, that would give RMs real-time pricing recommendations. By unlocking the power of AI, the tool would help RMs quickly access the pricing insights most relevant to their specific deals, increasing efficiency and enabling them to spend more time building stronger relationships with their clients.
Augmented intelligence assistants will change the way RMs use technology. Real-time, contextually relevant insights will help RMs to provide better service to their clients and drive improved business outcomes.
The better the answer
Building a banking platform with human behavior at its core
Technology platforms can be excellent tools for improving client relationships, but only if they focus on the real needs of their users.
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 provides RMs with pricing insights and recommendations at the right time in the client journey that are relevant and add true value to their decision-making process.
The better the world works
How banks can build better pricing systems for customers
AI and advanced analytics can strengthen customer relationship management, driving growth and profitability.
Smart Advisor enables RMs to unlock greater insights through two main capabilities:
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 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.
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