Transaction & Behaviour Intelligence
Understand how customers really behave, and spot when it changes.
A behavioural analytics platform that turns raw transaction streams into segments, predictions and real-time anomaly alerts: the shared foundation for fraud, risk, retention and personalisation.
01 · Overview
Years of data, feeding only reports
Banks and fintechs sit on years of transaction history, but most of it only feeds reports. Segments are built by hand on demographics, anomalies are caught by fixed thresholds, and each team builds its own customer view from scratch.
Built with
- Sequence models
- Segmentation
- Anomaly detection
Deployment
On-premise, private cloud or in-region.
02 · How it works
One behavioural foundation
- 1
One feature layer
A shared feature store of spending velocity, category mix, time-of-day rhythm, channel use and movement, used by fraud, risk, marketing and compliance alike.
- 2
Learn the sequences
Sequence models learn each customer's patterns, predict the next transaction, and flag meaningful deviations.
- 3
Discover natural segments
Clustering and graph embeddings group customers by how they behave, refreshing as behaviour shifts.
- 4
Alert and act
Unsupervised anomaly detection surfaces unusual activity in real time and routes it to the right team.
- Transaction streamCards, transfers, wallets
- Behavioural featuresShared feature store
- Sequence & segment modelsLSTM · clustering · anomaly
- Scores & segmentsReal-time API
- Consuming teamsFraud · risk · marketing
03 · Capabilities
What it powers
- Real-time transaction anomaly alerts
- Behavioural segmentation for products and pricing
- Customer lifetime value prediction
- Merchant risk and merchant-fraud monitoring
- Transaction monitoring input for AML
04 · Outcomes
What we measure
- Earlier anomaly detection
- Time from first unusual event to alert
- Sharper segments
- Segment stability and lift on campaign response
- One customer view
- Teams and models using the shared feature layer
- Faster new use cases
- Time to launch a model on existing features
Example scenarios
Illustrative- Fintech appIdentify high-value users for premium tiers from early behaviour.
- Retail bankReal-time alerts for unusual spending on customer accounts.
- Payments platformMerchant-level anomaly monitoring across thousands of merchants.
- Digital walletLifetime-value scoring to focus acquisition spend.
05 · Governance
Data governance & access
Controls built in
- Versioned features and models
- Monitoring dashboards for data quality and drift
- Access controls per consuming team
- Personal data minimised and pseudonymised in the feature layer
Technology
- Python
- scikit-learn
- TensorFlow
- Kafka
- Spark
- Flink
- Neo4j
- Redis
- ClickHouse
- Grafana
06 · FAQ
Common questions
How is this different from Fraud Detection?
Fraud Detection is a decisioning system tuned for one outcome. Transaction Intelligence is the behavioural foundation underneath it, and it also serves marketing, risk and compliance.
Can we start small?
Yes. Most clients start with one use case, usually anomaly alerts or segmentation, and reuse the feature layer for the next one.
Does it need real-time data?
No. It can start on daily batch extracts and move to streaming when a use case needs it.
Put your transaction history to work.
Start with one use case and build a foundation every team can reuse.
Start a project
