Notes, receipts and tags
Optional personal context stored against a transaction so the next time the merchant appears, the customer remembers why it mattered.
Product Design · UX
Lloyd’s Insights Concept
A concept for making everyday banking transactions easier to understand, remember and act on through transaction memory, predictive spending and contextual cashback.
The behaviour I noticed
The problem did not arrive as a product brief. It came from repeatedly reviewing my own transactions and realising that the bank could tell me how much I spent, but not always enough context for me to remember what the purchase actually was. When a merchant description was vague, I had to reconstruct the transaction across receipts, messages and other apps.
That suggested a gap between the KPI a banking app can already report and the job the customer is actually trying to complete. Transaction history records money movement; the user often needs memory. I reframed the opportunity around reducing reconstruction effort rather than simply adding more transaction detail.
Process
I treated the concept as an extension of an established banking experience rather than a visual redesign. The first step was mapping the moment where confidence breaks: a customer sees an unfamiliar payment, cannot recover the real world context, and moves toward help or fraud reporting. From there I explored the smallest intervention that could solve the problem before expanding into predictive insights.
Early wireframes
The early wireframes test hierarchy before styling. The transaction detail remains the anchor, personal context is added as a secondary layer, and predictions sit separately so the app does not blur factual transaction data with inferred behaviour.
Design system and states
Rather than inventing one off UI, I considered how each feature would exist as a reusable component with default, completed, eligible, ineligible, low confidence and high confidence states. That matters in banking because a component has to behave predictably across many merchants, account types and customer circumstances.
Validation
Research status: this is an independent concept and I have not presented fabricated interview findings as real user research. The next validation step would be five short interviews with Lloyds or comparable mobile banking users, followed by task based testing of the note and prediction flows.
“Tell me about the last card transaction you did not immediately recognise.”Tests whether the problem exists beyond my own experience.
“What do you do before reporting a transaction as suspicious?”Maps the current recovery journey and support burden.
“Would you ever add a note or receipt to a banking transaction?”Tests whether annotation creates value or simply more admin.
“When would a spending prediction feel useful rather than intrusive?”Tests trust boundaries around inferred behaviour.
“What would you need to see before trusting an automatically activated cashback offer?”Tests clarity, control and commercial trust.
Accessibility and inclusion
Financial products need to work when people are stressed, distracted or using assistive technology. I therefore treated accessibility as part of the interaction model rather than a final compliance pass.
One important trade off
Business need: Lloyds has an incentive to increase engagement with rewards and paid account benefits. User need: a customer opening transaction details primarily wants clarity, not an upsell.
I would push back on placing an aggressive Silver upgrade prompt directly inside the transaction recovery flow. My preferred approach is to prioritise recognition first, then surface a clearly labelled cashback opportunity only when it is relevant. For ineligible customers, “Available with Silver” should remain secondary to the transaction itself. I would validate the position and wording using comprehension, task completion and offer activation rather than optimising only for upgrade clicks.
What I explored
I explored personal notes, receipts, tags and merchant location information where reliable merchant data is available. I then connected that richer transaction context to forward looking spending insights, recurring payment recognition and relevant cashback offers.
Concept exploration. Not a Lloyds Banking Group product or an official interface.
Better merchant and location data
Where data is reliable, the flow resolves processor names into recognisable merchants, categories and place names inside the app.
Predictive spending
Once transactions are better understood and categorised, spending patterns become more useful for forecasting, recurring payment recognition and monthly planning.
Contextual cashback
Rewards appear only when they are relevant to the customer and stay secondary to clarity, avoiding an intrusive upsell inside the recovery flow.
Transparent privacy choices
The customer chooses what to save, what predictions to allow and whether to opt into cashback. Suggestions support the decision, but the system never silently invents personal context.
Interactive prototype
The concept is designed as one connected system. Transaction context improves understanding. Better categorisation improves spending patterns. Spending patterns improve predictions. Merchant behaviour makes rewards more relevant.
Validation priority
The assumption that could break the whole idea
Biggest assumption
Will customers actively annotate transactions?
The concept only creates durable value if the benefit of adding context is greater than the effort. Merchant enrichment, receipt capture and low effort suggestions would therefore be central to the next iteration.
Notes + receiptsAdoption
PredictionsEngagement
CashbackActivation
InsightsRepeat use
SupportReduced enquiries
As an independent concept, the right next step is validation rather than invented production results. These measures would show whether the experience improves recognition, trust and usefulness without adding unnecessary customer effort.