This simulated case explores a prepaid B2B discount programme that grows in volume while revenue per service falls faster than the discount should explain. Intake can surface anecdotes of card sharing, unusual usage and franchisee complaints. Anecdotes do not quantify leakage or prove abuse. Stage 2 would test the programme-design hypothesis using transaction patterns, cardholder attributes, location data and interviews. The directional decision is FIX, not Stop. Use AI to rank anomalies for human review, then redesign the programme economics and franchise incentives. Do not let the model accuse or automatically block customers. The objective is programme integrity and defensible action, not automated punishment.
What does this case show? AI can help quantify and prioritise suspected abuse, but the deeper value comes from fixing the programme incentives that make leakage rational.
A franchise-based consumer-services network offering a prepaid B2B volume card to small-business intermediaries. The card gives a meaningful discount in return for committed volume. The programme has grown, but headquarters sees revenue per transaction fall and receives complaints that trade discounts are reaching retail customers.
Are customers abusing the programme, and should management shut it down, police it harder or redesign it?
The issue sits at the channel interface between headquarters, franchisees and cardholders. Headquarters sees aggregate data, franchisees see local behaviour but benefit from volume, and cardholders control the credential. No actor owns programme integrity end to end.
The following items may be reported during the initial intake or interviews. They are not treated as measured performance values.
Initial intake, interviews and limited document signals may make the following hypothesis a priority. It is not presented as a confirmed root cause until it is tested against operational evidence.
The working hypothesis is that the programme’s economics may make abuse rational. The gap between trade and retail prices is large, verification is weak and franchisees may have limited incentive to police activity because discounted transactions still generate local revenue. A model can help quantify and rank patterns, but it cannot repair the incentive design by itself. The highest-value intervention may combine targeted review with changes to terms, verification and franchisee incentives.
The following are value hypotheses, not achieved or validated outcomes.
Customer value: Legitimate trade customers retain a differentiated benefit. Retail customers are treated more consistently, and the brand avoids a visible fairness problem.
Internal value: Management can separate concentrated abuse from anecdote, act surgically, protect margin and reduce conflict between headquarters and franchisees.
Automatically block cards that exceed fixed limits or trigger identity checks.
The AI role, human decision boundary, data readiness and measurement logic are designed together. No scale decision is made without pilot evidence.
This table is not an investment decision or a definitive ranking. Priority and evidence readiness are reassessed during Stage 2.
| Opportunity hypothesis | AI role | Value connection | Evidence readiness | Directional priority |
|---|---|---|---|---|
| Multi-layer card risk scoring | Detect + Explain | Prioritise review and quantify leakage patterns | Transaction and cardholder attributes required | High; human-reviewed pilot |
| Franchise-level pattern detection | Detect + Explain | Identify local tolerance and incentive issues | Location and franchise context required | High; second analytical layer |
| Programme redesign scenario analysis | Simulate + Recommend | Align discounts, verification and economics | Cost, churn and volume assumptions required | Medium-high; management workstream |
| Automatic card blocking | Execute | Fast enforcement | High false-positive and relationship risk | Not recommended as the first design |
The design below is not a solution commitment. It is a pilot hypothesis to be validated.
Human-Reviewed Programme Integrity Model
The following are readiness hypotheses that require document and process review.
This plan is adapted to data access and client scope. Stage 1 alone does not include implementation or outcome validation.
| Period | Purpose | Main actions |
|---|---|---|
| Days 1–30 | Establish ownership and evidence | Name the programme-integrity owner, review the top-ranked cases manually and assess legal and contractual options. |
| Days 31–60 | Act surgically and redesign | Take documented action on confirmed high-risk cases. Draft new verification, tiering and franchise-incentive rules. |
| Days 61–90 | Pilot continuous monitoring | Run monthly risk scoring, introduce redesigned terms for renewals and measure post-intervention effects. |
The model concentrates review on genuinely problematic cases and programme changes reduce leakage without harming legitimate customers.
Signals are useful but thresholds, features or escalation rules create too many false positives.
Legal, contractual or data-quality constraints make individual-level monitoring indefensible.
Transferable lesson: When people exploit a system, the incentive design is part of the root problem. Quantify the leakage first, then redesign the economics, not only the locks.
Assess the available evidence, business value and implementation conditions together.