Retail & Chain Stores
Till, refund and discount abuse
Voids, refunds and manual discounts are tracked by cashier, hour and store, flagging behaviour that stands apart from peers.
Loss-prevention audits become targeted instead of random sampling.
Overview
What is anomaly detection, and why not just write rules?
Rules (“alert on refunds above 3,000 TL”) only catch what you already imagined; anyone who knows the threshold stays under it, and when volumes change the rule drowns you in false alarms. Anomaly detection instead learns a profile of normal behaviour from history — by branch, hour, product and season — then scores meaningful deviations from it. New abuse patterns nobody predicted get caught, while seasonal swings stop generating noise.
Learns normal behaviour from your data
Catches new and unknown patterns too
Threshold calibration that cuts false alarms
An explanation and evidence for every alert
E-mail, SMS and dashboard notifications
Runs alongside your existing rules
How it works
How does it work?
Scope
What the solution includes
Risk scoring
Each transaction or event is scored 0–100 so the team works down from the riskiest.
Explainable alerts
Each alert shows which signals triggered it — “why did this fire?” always has an answer.
Real-time notification
Critical anomalies reach the responsible manager instantly by e-mail, SMS or WhatsApp.
False-alarm management
Team feedback and seasonality modelling steadily reduce unnecessary alerts.
Rules and models together
Your existing business rules stay in place; the model adds the patterns they cannot catch.
Case review & audit trail
Every alert opens as a case, recording who reviewed it and what was decided.
Industry Use Cases
How is it used, and in which industries?
“Abnormal” means something different in every industry — a till shortfall here, a vibrating bearing there. The setups we build most often:
Finance & Payments
Suspicious transaction and fraud detection
Combinations of amount, time, device and location that deviate from a customer’s usual profile are scored instantly.
Suspicious activity can be stopped before the loss grows.
Manufacturing
Quality drift and predictive maintenance
Machine sensors and quality measurements are monitored to catch pre-failure drift and rising scrap rates early.
Unplanned downtime and scrap costs go down.
Logistics & Fleet
Fuel leakage and route deviation
Fuel use, distance, load and route data are compared per vehicle, flagging consumption that cannot be explained.
Leakage in the fuel budget becomes visible.
Facilities & Energy
Abnormal consumption and leak tracking
Electricity, water and gas use is monitored per building and floor, reporting deviations such as consumption rising in an empty building overnight.
Water leaks and idling equipment are noticed days earlier.
IT & Cyber Security
Log and access anomalies
Access at unusual hours or from unusual locations and abnormal data-download volumes are detected.
Insider data leaks and compromised accounts surface early.
Typical Gains
Dakikalar
Time between event and alert
%40–60
False-alarm reduction after calibration
12 ay
Typical history needed to train
3–6 hafta
To the first working alert pipeline
Figures show typical ranges from comparable projects; actual results depend on your data quality and processes.
Integration
It works alongside your existing systems
ERP & muhasebePOS sistemleriSCADA / IoT sensörleriAraç takip (GPS)Sayaç & enerji izlemeSIEM & log kaynaklarıSQL veritabanlarıBI panelleri
FAQ
Frequently Asked Questions
Let’s find the deviations hiding in your data
A short analysis on your historical data shows what the system would catch — before any deployment.