Part of Data & Predictive AI

Fraud & anomaly detection

Real-time and batch detection of fraudulent transactions, accounts and unusual behaviour with explainable alerts and review queues.

Book a free AI consultation Back to Data & Predictive AI

In one paragraph

Fraud & anomaly detection: Real-time and batch detection of fraudulent transactions, accounts and unusual behaviour with explainable alerts and review queues. Data analytics and predictive AI services turn your operational data into forecasts, risk scores and dashboards that drive decisions: demand and revenue forecasting, churn prediction, fraud and anomaly detection, customer analytics and real-time BI. Built for finance, retail, SaaS and operations teams that want calibrated predictions and one trusted view of the business rather than competing spreadsheets.

How fraud & anomaly detection fits into data & predictive ai

Reduce churn
Score accounts weekly, surface risk drivers and trigger retention plays before customers leave.
Catch fraud early
Flag suspicious transactions and accounts in real time with a review queue and low false-positive rates.
Plan inventory and capacity
Forecast demand by product and location to cut stock-outs and waste.
One source of truth for KPIs
Replace competing spreadsheets with governed pipelines and dashboards everyone trusts.

How we deliver

1
Data audit and feasibility
Inventory sources, assess quality and answer concretely whether the data supports the prediction you want.
2
Pipeline and features
Reliable ingestion, cleaning and feature engineering into a warehouse or feature store.
3
Model development
Baselines first, then candidate models with time-aware validation, explainability and a written model card.
4
Deployment
Serve predictions by API, batch or directly into CRM and BI tools with monitoring.
5
Monitoring and retraining
Drift detection, performance tracking and a retraining schedule your team can run.

Frequently asked

Less than most teams think for a first model, but it must be relevant and reasonably clean. Churn models need a history of customers who left; forecasting needs enough seasons to learn from. The feasibility study answers this concretely for your case before you commit to a build.

Time-aware validation, honest baselines, confidence intervals and a written model card. If a model does not beat the simple baseline meaningfully, we say so and recommend fixing data or process instead. Accuracy is reported on held-out data, not training data.

Not always. Many engagements start with a targeted analysis on exported data, and we only propose infrastructure once the value is proven. When you do need pipelines, we build them with Airflow or Prefect into Postgres, BigQuery or Snowflake. A modest, well-governed warehouse usually pays for itself quickly once two or more teams depend on the same numbers.

Yes. Churn scores can land in Salesforce or HubSpot, fraud alerts in a review queue, forecasts in your planning system or BI dashboards. We deliver predictions where decisions are made rather than in a separate report. Each integration includes a score explanation, so a salesperson or analyst understands why a record was flagged and what to do next.