Banking Fraud Detector.
A typical organization loses an estimated 5% of its yearly revenue to fraud. We apply supervised learning algorithms to detect fraudulent behaviour based upon past fraud and recommend methods to discover new types of fraud activities.
Banking Fraud Detector Demo..
Our machine learning algorithm has been trained on half-million records and the model is a robust classifier of fraud data. For this demo, we have taken un-seen test data which has the features to help predict the classifier of fraudulent transactions. Please upload the test data and the AI engine can predict in real time whether it is fraud or not. For illustration purposes, we have further selected case of False Positive and False Negative and this subset is downloaded in real time. Use cases span across several sectors Insurance, Banking, Retail, Health etc.
The Benefits of AI-based Banking Fraud Detector .
Minimize losses by detecting and stopping fraud in real-time across banking channels like fraudulent transactions, credit card fraud, fake insurance claim, and potential loan defaulter.
Process large volumes of data at high speeds. Usage and processing big data sets is not such an easy task. With data processing, machine learning and data modelling, huge volumes of data can be handled easily.
Identify Unusual Behaviour
Identify and act on new unusual behaviour. Fraud detectionwith an analytic model helps toidentify possible predictors of fraud associated with known fraudsters and their actions in the past with historical data.
Increase Operational Efficiency
Increase operational efficiency by monitoring fraud investigation efforts. Large volumes of data are processed during real-timefraud monitoring. A powerfulfraud prevention and detection solution helps to manage the tasks successfully.