Banking

Pricing as a strategic advantage for a global investment bank

Zühlke helped a global investment bank replace fragmented pricing processes with a centralised, real-time pricing engine. The solution applies client-specific pricing at the point of trade, strengthens governance, and gives the bank a scalable foundation for more intelligent pricing.

Collaboration overview

A global investment bank needed to standardise pricing governance across regions while preserving flexibility for relationship managers.
Zühlke implemented a centralised pricing engine that calculates client-specific fees in real time and integrates directly with trading workflows.
With a modular architecture enabling rapid rollout to new regions, the solution now processes more than 1.2 million pricing requests each year, supporting the foundation for more AI-augmented intelligent pricing.

The challenge

In wealth management, pricing is rarely straightforward. High-net-worth clients expect tailored fee structures. Relationship managers (RMs) negotiate discounts based on assets, relationships, and future commitments. Over time, this creates a highly fragmented pricing landscape, often managed through a mix of spreadsheets, email communications, and manual approvals.

For a leading global investment bank client, this challenge became increasingly visible:

Pricing agreements were inconsistent and difficult to track
Special discounts were managed manually across regions
There was limited governance and transparency
Scaling pricing practices across markets was slow and costly

In some regions, there was no system at all, with pricing decisions tracked in Excel or informal communication channels. At the same time, the business faced growing pressure to protect margins amid fee compression, ensure consistent treatment of clients, and enable faster onboarding and trading execution.

Across the industry, many wealth managers are facing a similar reality: pricing has evolved organically over time, resulting in fragmented logic, inconsistent client treatment, and limited visibility into profitability.

The turning point: pricing becomes mission-critical

The real inflection point came when pricing moved closer to the core transaction flow. Instead of being a static agreement, pricing needed to be:

Dynamic: applied at the point of trade
Contextual: tailored to client, product, and agreement
Reliable: always available in real time

Every trade, whether equities, funds, or foreign exchange (FX), required validation against client-specific pricing conditions. Pricing had become business-critical infrastructure, and no longer a back-office function. More importantly, pricing is one of the few remaining levers for differentiation in an increasingly commoditised market.

As products converge, effective pricing and personalisation are becoming critical drivers of client value and competitiveness.

The solution: a real-time pricing engine with built-in governance

To address this, we helped the bank implement a centralised pricing engine designed to:

1. Apply pricing in real time

Whenever a trade is initiated, the system:

Checks if a client has special pricing conditions
Calculates the correct fee dynamically
Returns the price instantly to the order management system

This ensures pricing is always accurate at the point of execution.

2. Enable controlled flexibility for relationship managers

Rather than removing RM discretion, the solution introduced structured flexibility. RMs can request client-specific discounts, which can be one-off or time-bound (e.g. valid for 12–36 months)

All requests are processed through a rules-based approval workflow where smaller discounts can be approved directly while larger discounts require escalation to the next level, hence balancing client-centricity with governance.

Every pricing decision is logged, creating a clear audit trail, critical for both internal governance and regulatory alignment.

Scaling across regions: from fragmentation to consistency

A key strength of the solution was its ability to scale across regions with differing business requirements. Initially deployed in select Asian markets, the platform was successfully extended to a new region with additional constraints and approval structures, without requiring a complete rebuild. By leveraging a flexible, modular design, the team was able to adapt the solution to local needs and go live in months.

In contrast to legacy enterprise tools, the solution built in the cloud was faster to deploy, more aligned with business needs and more cost-effective. This solution foundation is critical for control, but also as a prerequisite for more advanced pricing capabilities such as predictive pricing, scenario simulation, and AI-driven recommendations.

Performance at scale: built for high-volume trading environments

The platform is built to operate reliably in high-volume, real-time trading environments where performance is critical. It processes over 1.2 million pricing requests annually, with peak activity concentrated around market opening hours when large volumes of trades are executed within short time windows.

Despite this, the system consistently meets a sub-3-second response time service-level agreement (SLA), ensuring seamless integration with trading workflows. Given that pricing directly determines whether trades can be executed, the solution is designed for high availability and resilience, making uptime and speed essential to maintaining uninterrupted business operations.

Business impact: beyond efficiency

The transformation delivered value across multiple dimensions. Beyond operational gains, the bank gained something more strategic: visibility into pricing decisions and their impact, a critical step towards active margin management.

Operational efficiency

Eliminated manual pricing processes and reduced reliance on spreadsheets and emails

Consistency and control

Standardised pricing logic across regions and reduced pricing leakage

Faster time-to-market

Enabled rapid rollout to new regions while adapting to local business rules with minimal effort

Improved RM experience

Clear guidance on pricing decisions with faster approval cycles

The future of pricing in wealth management

Pricing in wealth management is shifting from static fee structures to more dynamic, data-driven approaches. Financial institutions are starting to use better data and analytics to guide pricing decisions, moving towards more consistent and client-specific pricing.

At the same time, there is increasing pressure to strengthen governance to ensure pricing decisions are transparent, controlled, and aligned across the organisation.

What’s changing now is the role of intelligence in pricing decisions. Rather than replacing relationship managers, AI can augment them by suggesting optimal pricing ranges, flagging outliers, predicting client sensitivity to pricing changes, and identifying opportunities to improve margin without impacting client relationships.

However, AI-driven pricing is only effective when built on a strong foundation of clean data, consistent rules, and governed processes, making today’s investments in pricing infrastructure essential for tomorrow’s capabilities.

Financial institutions that take these steps now will be better positioned to protect margins, scale efficiently, and deliver a more consistent client experience.