The effectiveness of banks in pricing their products and managing discounts varies widely. While pricing should be a key lever for growth and revenue generation, it is often under-prioritized, with more focus placed on volume growth and market share. As markets mature, however, pricing becomes increasingly important. When growth rates slow, profitability from the existing customer base takes precedence. This is particularly true in wealth management in Hong Kong and Singapore, where pricing has traditionally been risk-oriented, driven largely by fair dealing guidelines from MAS and HKMA. But as market dynamics evolve, the importance of strategic commercial pricing is coming into sharper focus.
The need for strategic price management
Pricing in banks is often approached reactively, driven by regulatory changes or competitive pressures, rather than by a strategic long-term vision. Many banks lack dedicated center of excellence for pricing that collaborate closely with product and front-office units to optimize overall profitability. Instead, pricing strategies are often managed in silos by different product units, leading to inconsistent pricing practices across products and geographies. Discounts and individualized pricing conditions are common across retail, corporate, and wealth management sectors, but these are often not managed systematically, resulting in significant revenue leakage. In some cases, discount rates can exceed 50% relative to headline prices, severely impacting profitability.
Key success factors in professional price management
Establishing an effective pricing foundation is essential for sustainable revenue growth. Here are four critical factors for success:
1. Invest in capabilities and teams
Financial Services providers need to establish dedicated pricing functions. These teams should have strong analytical capabilities to understand market elasticities and make data-driven pricing decisions. Pricing is ultimately about analytics of client behavior and deriving the right strategies. Having the right expertise in place is crucial.
2. Define a pricing Target Operating Model (TOM) and leverage software
A well-defined pricing TOM is essential. Many players still manage discount processes manually, often through email and XLS-Files, creating inefficiencies and unnecessary workloads. The adoption of digital pricing tools with integrated workflows can improve transparency and streamline approval processes, particularly for front-office staff. This allows relationship managers (RMs) to access pricing information more easily and focus on value-based selling.
3. Enhance front-office value-selling capabilities
Pricing is not just about numbers; it's about communicating value of the bank to clients. Many sales representatives lack the training needed to defend pricing in conversations with clients. Instead, they default to lowering prices. Continuous, in-depth training and support can empower RMs to articulate the value of the bank's offerings more effectively.
4. Align incentive structures with profitability
If performance metrics are tied primarily to volume metrics like assets under management (AuM) rather than profitability, fostering a profitability mindset among the sales forces becomes challenging. Aligning incentives with profitability targets can drive more balanced, long-term decision-making.
The role of dynamic pricing and pricing personalization
Dynamic pricing, already prevalent in many sectors like travel or retail, is becoming increasingly relevant in financial services. It involves adjusting prices for deposits, foreign exchange, or loans based on factors such as client demand, client attributes, risk, competition, or interest rates. Today, banks are transitioning from static data analytics to advanced pricing software that can analyze client behaviors and estimate price elasticity. However, this technology is still evolving, as there are ongoing issues such implementation constraints as well as concerns about clients’ trust.
For example, dynamic rate management for deposits allows banks to optimize pricing strategies, attract deposit flows, and reduce funding costs. This is especially crucial for challenger banks seeking to gain market share and for incumbents aiming to defend their deposit bases. Additionally, effective pricing tools can measure the impact of deposit promotions and minimize the risk of cannibalization between products.
Personalization and systematic relationship pricing are also making their mark in wealth management. Leading banks are investing in relationship pricing software that provides RMs with high levels of transparency to define individualized pricing bundles. Digitized discount workflows not only reduce RM workloads but also improve pricing transparency and increase profit margins by better managing pricing outliers.
When implemented with the right TOM, banks can reduce discount workflows by up to 50-60%. This leads to higher profit margins through better pricing oversight, while also ensuring compliance with regulatory guidelines by providing audit trails, managing price disclosures, and generating fee letters where required.
The future: AI's growing role in commercial pricing
AI is poised to revolutionize how financial service companies price their products and services. Many banks are currently experimenting with AI use cases, and there is enormous potential for AI-driven price management. Machine learning is already widely used in many banks, and in the coming years, generative AI will be employed in even more advanced ways.
Predictive analytics for price elasticity in Consumer Banking and D2C services
In retail banking, especially in non-advised business, AI can be used for predictive analytics to estimate price elasticity. By analyzing vast amounts of data, AI can identify patterns and trends that influence pricing decisions. This enables banks to optimize pricing in real-time and offer personalized promotions in areas such as deposits and FX. In the lending space, AI can tailor loan rates more accurately by assessing individual risk profiles and willingness to pay. Both revenue growth and lower NPL-ratios can be achieved.
AI-based pricing decision support tool in Wealth Management and advised business
In private banking which, AI can support RMs by providing data-driven insights for more informed pricing decisions. AI can also analyze triggers for discount requests, identifying that more than 40% of today’s discount requests cannot be justified by objective client characteristics. Instead, they often depend on the negotiation skills or habits of individual RMs. AI-powered decision support tools can help structure discount practices at point of sale, improving overall pricing consistency and reducing the risk of unfair treatment.
Additionally, AI could serve as a co-pilot for RMs, generating tailored negotiation strategies and helping RMs defend pricing proposals. Continuous learning algorithms will refine these models over time, ensuring more effective and fair pricing outcomes.
Governance and fairness in AI-driven pricing
As AI becomes more integrated into pricing strategies, model governance will be crucial to ensuring fairness and compliance. Regulatory bodies worldwide are issuing new guidelines on responsible AI, with a clear emphasis on safety, transparency, and non-discrimination. Ensuring that AI-driven pricing models are governed effectively will not only mitigate risks but also build trust with customers. In the future, governance will be a competitive advantage, ensuring that pricing decisions benefit customers and prevent unintended outcomes.
