The Impact of Big Data on Fraud Investigations
Keywords:
machine learning algorithms, use of big data, big data analytics, machine learning, fraud detectionAbstract
In several sectors, including finance, healthcare, and insurance, fraud is a major issue. Big data analytics has become a potent instrument for identifying and thwarting fraudulent activities. Big data analytics may assist businesses in finding patterns and anomalies in huge, complex data sets that may be signs of fraudulent activity. These patterns and anomalies can be found by utilizing cutting-edge machine learning algorithms and statistical models. Data gathering, preprocessing, feature engineering, model training, and model validation are all steps in the process. Utilizing different kinds of data, including financial data, user activity data, and social network data, organizations can create scam detection algorithms. However, using big data for fraud identification may present issues with data protection, model interpretability, and scale. However, big data analytics can greatly lower the incidence of fraud in a variety of sectors with the right tools and knowledge.
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