Fraud detection

Data analysis combined with customer behaviour and historical transaction data can assist for example credit card companies and insurance companies to identify fraudulent activities. Various sectors including banking, finance, insurance, government, and law enforcement use data analysis approaches to detect irregular transactional behaviour that indicates a high likelihood of for example, a stolen card or a fraudulent insurance case.

Mostly, this is done by identifying different patterns in transactions with data mining techniques like decision trees, machine learning, association rules, clustering, and neural networks. Statistical data analysis techniques are also used for this purpose such as:

• Data preprocessing techniques for error corrections

• Probability distributions

• Time-series analyses

• Clustering and classification techniques

• Algorithm matching for detecting anomalies in transactions