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Banks should choose a lead loyalty model based on the customer problem they need to solve—not start with points or an AI tool. The four useful models are Value Exchange, Benefits, Engagement, and Brand Affinity. Each aims to earn a different customer belief, and AI can help execute each one only when the bank has reliable data, operational capacity, and customer trust.
The framework comes from Gavin Wassung, a principal strategy consultant at Tredence, in a September 16, 2026, article in The AI Journal. It is a strategic framework, not a four-model taxonomy independently validated by the other sources cited here.
Start with the relationship problem, not the rewards mechanic
A loyalty investment should address a specific constraint: customers may be leaving, using only one product, overlooking the bank in everyday financial decisions, or lacking a reason to trust and recommend it. Those problems call for different interventions. A discount may encourage a first habit; it does not, by itself, create useful service or long-term affinity.
Wassung’s framework treats the models as complementary engines rather than permanent, mutually exclusive program types. Value Exchange can encourage a behavior, Benefits can recognize a deeper relationship, Engagement can make the bank more relevant in daily money life, and Brand Affinity can help sustain a relationship when competitors match offers. That is a proposed strategic sequence, not a universal causal law.
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Which of the four loyalty models fits your bank?
| Model | Customer belief to earn | Best starting condition | Potential AI role | Useful measures | Main delivery challenge |
|---|---|---|---|---|---|
| Value Exchange | “The more I do with you, the more value I get in return.” | Attrition, unstable deposits, or low card-of-first-use makes near-term behavior change important. | Offer decisioning, behavior prediction, and incentive efficiency. | Adoption of target behaviors such as direct deposit or bill pay; incremental payments or card use; rewards return on investment. | Customers may respond only while incentives continue. Measure incremental behavior, not reward uptake alone. |
| Benefits | “I stay because you treat me differently.” | The bank already has primacy and credible data to recognize a broader relationship across products. | Relationship analytics, tier rules, differentiated service triggers, and recommendations. | Tier movement, multi-product penetration, and retention by tier or segment. | Recognition is credible only if qualification rules are clear and the promised treatment is delivered consistently. |
| Engagement | “You fit naturally into my life.” | Clean signal data, digital instrumentation, and cross-channel coordination can support relevant interactions. | Lifecycle orchestration, timely nudges, and decisions about when not to contact. | Activation, engagement frequency, early-life milestone completion, lifetime-value lift, and attrition versus a control. | More messages are not necessarily more useful. Timing, coordination, and restraint matter. |
| Brand Affinity | “You align with who I am.” | The bank’s economics and customer experience are credible enough for trust, advocacy, and identity to compound. | Sentiment analysis, identity modeling, and personalized storytelling. | Advocacy or NPS, referrals and referred-customer quality, and resilience during price or rate pressure. | Affinity is difficult to manufacture quickly; it depends on consistent actions and trust. |
How to choose a lead model
- Diagnose the constraint. Decide whether the priority is short-term attrition or primacy, shallow product penetration, weak daily relevance, or limited trust and advocacy.
- Name the belief to earn. Choose one lead model and describe what customers should come to believe. Points and perks are mechanics, not the strategy itself.
- Define an observable promise. For a Benefits strategy, specify what recognition actually changes—such as service priority, pricing treatment, advice, issue resolution, or access. Do not promise treatment the operating model cannot reliably deliver.
- Build for one use case first. Identify the required data, decisioning, journey, measurement, technology, and talent capabilities. McKinsey’s customer-value-management guidance recommends focused use cases and relevant data rather than requiring an all-encompassing data pipeline from the outset. It also emphasizes privacy requirements and customer consent. See McKinsey’s guidance on AI-powered banking personalization.
- Test incremental outcomes. Where appropriate, compare results with a control. A rise in message volume, clicks, or reward redemption alone does not establish that loyalty improved.
- Add complementary models when the relationship is ready. A later model should address the next customer need, not simply increase program complexity.
What AI can—and cannot—do
AI can help select offers and manage incentive efficiency for Value Exchange; identify relationship signals for Benefits; coordinate lifecycle interactions and timing for Engagement; and analyze sentiment or tailor storytelling for Brand Affinity. It cannot decide which customer belief the bank should earn, make an unworkable promise credible, or substitute for trust.
McKinsey describes four practical foundations for customer-value management: customer data and decision-making; personalized campaigns and journeys; measurement and marketing technology; and operating model and talent. Its guidance also discusses testing and refining campaigns, embedding relevant content in app and web experiences, and using near-real-time transaction triggers. Data collection and personalization must comply with applicable privacy rules and customer-consent frameworks.
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Reported results provide context, not a guaranteed forecast. In its 2026 Global Banking Annual Review, McKinsey reports that leading banks using a full customer-value-management engine achieved 20–30 percentage points higher customer engagement and 10–25% higher customer value. Those are reported results among leading banks, not a promise that any one AI intervention or loyalty model will produce the same outcome. The review also reports that generative AI use among US working-age adults rose from 45% in 2024 to 55% in 2025. These figures describe adoption, not customer preference for bank personalization. See McKinsey’s Global Banking Annual Review 2026.
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A separate McKinsey article on AI personalization reports 20–30% engagement improvement, 10–25% customer-value improvement, and 15–25% customer-experience improvement from its work with banks. It also reports 2–3 times higher click-through rates from banks using hundreds of tested near-real-time triggers. These findings are distinct from the annual review’s engagement measure and are not guaranteed results for every bank or program. Read McKinsey’s article on personalizing banking experiences with AI.
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What the broader evidence says about loyalty and competition
Customer relationships are contested. Accenture’s 2025 Global Banking Consumer Study covers 49,300 customers in 39 countries and 700 banks. Its overview reports that 73% engage with multiple banks beyond their main bank, 58% bought a financial service or product from a new provider in the prior 12 months, and 33% have a relationship with a digital bank. These findings support the need to earn relevance and trust; they do not establish that one of the four models is superior. See Accenture’s Global Banking Consumer Study 2025.
Accenture also reports that banks in the top 20% for customer advocacy grew revenue 1.7 times faster, rising to 2.6 times in North America. Its overview reports a 5–30% boost in share of wallet across products and says advocates hold 17% more products with their primary bank on average. These are associations reported by the study, not proof that advocacy alone caused faster growth.
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Wassung’s article attributes a 15–25% annual revenue increase among customers who redeem points to McKinsey, but the article does not establish the original study’s date or scope. Treat that figure as an attributed claim, not evidence that rewards cause the increase or that it validates the four-model framework.
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The practical test is whether personalization makes customers feel accompanied or managed. A relevant offer can still feel intrusive if data use is unexpected; a tier can undermine trust if service staff cannot honor its benefits. Set boundaries for outreach, make the value exchange understandable, and check that front-line service and digital journeys can deliver what the program promises.
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For each proposed use case, leaders should be able to answer:
Quick Recap
- What customer problem and behavior is this meant to change?
- What data is necessary, and is its use consistent with applicable privacy rules and customer consent?
- How quickly must a decision be made, and can the bank act across the relevant channels?
- What comparison or control will distinguish incremental impact from activity that would have happened anyway?
- Can the operating team consistently deliver the service, treatment, or value the customer is being promised?
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