The ability to collect and interpret comprehensive financial data is becoming more relevant for wealth management. A recent academic contribution highlights how new data aggregation tools make it possible to access financial information across different providers and asset types, including non-traditional elements such as rental or utility payments.
For decades, private banks have relied on client disclosure and periodic updates to understand external holdings. As a result, advisory models have typically been built on incomplete visibility. New aggregation technologies offer continuous, consolidated data instead of fragmented snapshots. This article examines how such developments could influence investment management, advisory practices, and credit provision in Swiss private banking, and how banks might respond within Switzerland’s regulatory and service environment.
Aggregation as a New Information Infrastructure
Financial data aggregation automates the collection and standardisation of data from bank accounts, securities portfolios, pensions, credit products, insurance, and alternative sources. These tools usually work through API connections and machine-learning models that clean and harmonise data for use in analytics platforms.
This creates an infrastructure fundamentally different from today’s largely manual practices. Instead of relying on client documents or self-reported information, banks could access structured, real-time data. If used responsibly and with clear consent, this data could support more holistic risk assessments, more accurate investment decisions, and advisory processes based on actual financial behaviour rather than assumptions.
Implications for Investment Management
Investment management in private banks still depends heavily on periodic reviews, self-reported holdings, and information limited to assets under custody. These constraints make risk assessments and liquidity planning incomplete.
Aggregated data could allow portfolio managers to monitor exposures, liquidity needs, and concentration risks across all client holdings in real time. This may support a shift toward more forward-looking portfolio management. Issues such as excessive concentrations, rising liabilities, or sudden shifts in liquidity could be identified earlier, and investment actions could be based on a broader financial picture.
However, this would require significant adjustments in risk models, internal controls, and governance processes. Banks would need to ensure data quality, define how aggregated data can be used, and respect consent and confidentiality requirements.
Wealth Planning Based on Continuous Data
Wealth advisory today is still shaped by discussions, document collection, and periodic updates. Aggregation could reduce the reliance on client-provided information by supplying continuously updated financial data. This would improve the accuracy of retirement projections, liquidity planning, tax considerations, and succession scenarios.
Instead of annual or semiannual reviews, advisory interactions could become more continuous, supported by timely alerts and updated simulations. Yet such a shift would require new advisor capabilities, clearer communication with clients on data usage, and ethical rules for how algorithms influence advice. Transparency and explainability would remain essential aspects of advisory governance.
Expanded Data for Credit and Lombard Lending
Credit provision in Swiss private banking is often conservative and based largely on static financial statements and collateral values (also driven by regulation). Aggregated data introduces the possibility of dynamic risk assessments based on ongoing financial behaviour, including payment patterns not traditionally used in private banking.
This may improve access to credit for clients with limited traditional credit histories, such as young inheritors, entrepreneurs, or internationally mobile individuals. It could also enable lending models where collateral values and credit limits adjust more frequently, potentially improving risk control and flexibility in Lombard lending.
However, the use of alternative data raises questions about discrimination risks, explainability of decisions, and compliance with Swiss regulatory standards. The introduction of dynamic credit models would therefore require careful governance and clear ethical frameworks.
How Swiss Private Banks Could Respond
Swiss private banks could respond to aggregation in multiple ways. Some may choose a relatively narrow focus, using aggregation mainly to streamline onboarding, administration, or regulatory reporting. Others may integrate aggregated data into advisory processes to improve planning and investment decisions without changing their business model.
A more far-reaching approach would involve building or joining secure data ecosystems where banks help manage clients’ financial identities. Switzerland’s reputation for confidentiality and fiduciary responsibility could offer advantages in such a role. However, this would require strong standards for data governance, transparency, and consent management.
Conclusion
Aggregation technologies provide a new way to access complete and continuously updated financial data – a typical WealthTech topic that is currently being broadly discussed across the industry. Their influence may extend across investment management, wealth advisory, and credit provision. The degree to which Swiss private banks benefit from these developments will depend on strategic choices, regulatory compatibility, and responsible data governance.
Ultimately, aggregation is not merely a technical innovation. It represents a possible shift in how information is generated, shared, and used across the private banking value chain. Swiss banks that engage thoughtfully with these developments could strengthen the quality and credibility of their advisory models in the years ahead.