By Qelani Makina
“The first rule of any technology used in a business is that automation applied to an efficient operation will magnify the efficiency. The second is that automation applied to an inefficient operation will magnify the inefficiency.” — Bill Gates
In this data-driven era, it comes as no surprise that most decisions and achieved outcomes can be traced back to a data set of one form or another. This data set is utilised or processed by way of an “all-knowing” algorithm, resulting in an actionable outcome. The critical question is: “Is Private Banking’s greatest asset (data), also its greatest liability?”
Picture this scenario: Joe Bloggs is a high-performing Relationship Manager (RM) who manages an impressive portfolio contributing 30 percent of the entire private banking revenue book. Joe’s strength lies in his uncanny ability to “spot the need” of the client and act on it in real-time. He knows what to do because of a suite of tools that help him identify trends and intuitively predict unmet needs.
The reality is that Hyper-personalisation is an integral part of any business success. However, in the absence of explicit and deliberate ethical governance, the efficiency we desire is discredited if it threatens the very essence of relationship management which is client trust.
The Predictive Advantage: Hyper-personalisation
Hyper-personalisation is an advanced strategy to deliver highly individualised and purposefully tailored products and communications in real-time. It moves beyond basic demographics, using predictive analytics and sophisticated artificial intelligence (AI) with behavioural data to pre-empt and fulfil specific client needs before they arise. This proactiveness deepens relationships by making the service essential and unique.
Banks utilise hyper-personalisation for three critical functions:
Proactive Service: Tools assist RMs in interpreting client behaviour to guide the next best action, initiating relevant conversations, reducing turnaround time and delivering exceptional customer experience.It is this function that sharpens Joe’s efficiency. The algorithm flags a significant idle deposit prompting Joe’s instantaneous call proposing a structured investment instrument, transforming an “idle balance” into a profitable client solution.
Strategic Value Realisation: As commercial entities, banks use this tool to identify cross-sell and upsell opportunities, benefiting both the bank and the client by bringing hidden value opportunities to light. Joe’s 30 percent revenue contribution is not luck; it is algorithmic foresight. His tools flag high-value clients approaching major life events such as a business owner reaching retirement, allowing Joe to pre-emptively structure complex wealth transfer products and maximise both the bank’s and the client’s strategic value.
Customer Retention: AI flags subtle behavioural changes that precede a client exit (attrition prevention), giving the RM a crucial window for intervention, useful insights and relationship recovery. Joe recently received an alert detailing irregular, small-scale fund movements, signalling dissatisfaction before a major withdrawal. Joe used this insight not to sell, but initiate a deep-dive conversation, successfully addressing a misalignment in the client’s risk profile and saving a multi-million-dollar relationship from erosion.
The Threshold of Trust Erosion: The ‘Creep Factor’ Explained
Despite its capabilities, Hyper-personalisation can “cross the line” into intrusiveness, a crucial consideration given the reliance on client trust. The intrusion, known as the “creep factor,” occurs when a bank demonstrates knowledge gained from non-financial data or behaviour outside the direct commercial relationship. While culturally informal, the phrase became a necessary descriptor in business circles in the technology and marketing lexicon as predictive AI and massive data collection exploded during the digital transformation era of the mid-2000s.
The potential pitfalls are categorised into The Three Red Zones:
Use of External Data: Using third-party sources, for example social media or behavioural patterns, to infer client intentions, leading the bank to engage on a transactional prospect before the client has shared their intent. This is inherently intrusive and requires strict governance.
Timing and Specificity: Recommendations that are too timely or too specific can raise suspicions about information sourcing, necessitating strict organisational governance against unethical data use.
Lack of Transparency: When a client is unclear on the rationale behind a recommendation, the lack of transparency breeds vulnerability and doubt regarding the legitimacy of the bank’s information sourcing, ultimately manifesting as suspicion and distrust.
The Business Risk of Eroded Trust
Ignoring or poorly managing the three red flags carries significant and costly risks:
The Private Banking Mandate: Discretion is a core service. If trust is compromised, clients will often move their entire portfolios. Affluent clients prioritise their perception of security over maximising returns. A breach in trust collapses the nuanced relationship, creating a “creep factor” liability that necessitates an over-emphasis on governance and ethical oversight.
Regulatory Scrutiny: Data protection regulations, both global and country-specific, are crucial legal tools that protect consumer rights. Failing to adhere to this legislation, even in the spirit of delivering “exceptional service,” can trigger severe regulatory penalties, massive financial fines and instantly destroy the trust required for high-value client relationships.
Establishing an Ethical Framework (The Solution)
To prevent boundary crossing and relationship destruction, an ethical framework must be established:
Consent and Control (Layered Control): Hyper-personalisation strategies must be built to allow clients to opt in or out of various layers of personalisation, providing convenience and proactively eliminating the “creep factor.”
Algorithmic Explainability (XAI) Training: RMs require training to understand the AI facets, enabling them to achieve effective and transparent communication with the client. This alleviates concerns about intrusiveness and fosters trust and comfort.
The Human Touch: The RM acts as a filter for AI-generated prompts, providing validation and a human face. This touch ensures relevant context and empathy, guaranteeing propositions land safely and softly.
Systemic Integrity Checks: Periodic reviews and inspections are necessary to ensure algorithms remain robust and unbiased. A culture of scrutiny allows for timely improvements, enhancements and debugging of potentially damaging features.
What does Trust look like going forward
AI presents a double-edged sword: astonishing efficiencies versus the unsettling fear of RM redundancy. As compelling as this fear may sound, it remains far from the truth. The role of the Relationship Manager remains essential to the success of Private Banking.
Combining these two elements evidently yields a powerful advantage, significantly enhancing the strategic partnership between the Affluent Client and Relationship Manager.
As the World Economic Forum states: “The future of wealth management will not be AI replacing all human advisors, but AI empowering many, creating an ecosystem where AI enhances expertise, deepens client relationships and expands access to guidance at scale.”
The future of private banking is neither human nor machine, but hybrid. It is the precise blend of human empathy and algorithmic foresight that delivers true trust at scale.
Qelani Makina is Stanbic Bank Zimbabwe Head – Affluent, Wealth & Investments.