Brewen Couaran

Delft, Netherlands

Project

RevFlōw

Intelligent transaction categorisation and financial coaching for Revolut

Completed Hackathon Jun 2026· 24 hours
RevFlōw

A financial intelligence platform built in 24 hours at HackDelft 2026 for the Revolut challenge. It fixes broken merchant labels and generic categorisation with a two-stage ML pipeline (rule pre-pass plus CalibratedLinearSVC on sentence embeddings), KMeans life-stage personalisation, an Isolation Forest subscription engine, and a Claude Haiku money coach with one-tap action cards.

RevFlōw is a financial intelligence platform built in 24 hours for the Revolut challenge at HackDelft 2026 with Team YUBRE. It categorises Revolut transactions, personalises the results per user, and surfaces subscription and spending insights through a four-stage pipeline.

  1. Categorisation

    • Keyword pre-pass with 18 Dutch merchant rules; a confidence of 1.0 short-circuits the ML stage

    • CalibratedLinearSVC trained on MiniLM-L6 384-d sentence embeddings plus one-hot features (entry method, merchant domain, amount) across 12 spending categories

    • 75% accuracy on high-confidence predictions (≥0.45); uncertain merchants fall back to Miscellaneous instead of a wrong label

    • Under 50 ms end-to-end inference latency

  2. Personalisation

    • KMeans clustering segments users into four life-stage cohorts: Student, Family, Professional, Senior

    • Per-category spend share re-weights the global model probabilities (P' ∝ P_global × f_user) to fit each user's life stage

    • Every categorised transaction carries a human-readable explanation grounded in the user's history

  3. Subscription insight engine

    • Six parallel detectors: 4-week z-score anomalies, CV and cadence subscription detection, new-merchant bursts, fraud flags (velocity, smurfing), cohort-versus-peers comparison, and Isolation Forest user profiling

    • Flags forgotten, overlapping, or creeping recurring charges before they add up

    • Emits structured insight objects (type, severity, metrics) that drive both the deterministic action cards and the AI coach

  4. AI money coach and command centre

    • Insights feed Claude Haiku for concise two-sentence advice, with a supplier-agnostic self-hosted path in production

    • Deterministic action cards for one-tap interventions: spending limits, recurring-charge cancellation, analytics drills

    • Revolut-styled React command centre with a transaction timeline, monthly budget ring, live insight cards, and voice query input

    • Deployed to Railway; live at revflow.brewen.dev

References

  1. [2025] Transaction Categorization with Relational Deep Learning in QuickBooks — Dong, K., Jonnalagedda, P., Gao, X., Acharya, A., Kissa, M., Flores, M., Chawla, N. V., Das, K.. arXiv preprint arXiv:2506.09234 DOI ↗
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Brewen Couaran's Portfolio