{"id":22526,"date":"2026-07-16T13:30:32","date_gmt":"2026-07-16T11:30:32","guid":{"rendered":"https:\/\/www.neuraldesigner.com\/use-cases\/fraud-detection\/"},"modified":"2026-07-16T13:47:25","modified_gmt":"2026-07-16T11:47:25","slug":"fraud-detection","status":"publish","type":"page","link":"https:\/\/www.neuraldesigner.com\/use-cases\/fraud-detection\/","title":{"rendered":"Fraud detection using machine learning"},"content":{"rendered":"

Analyzing a company’s payments or transaction data enables us to identify which ones are fraudulent and which are not.<\/p>\n

Knowing this classification, we can prevent them in our company.<\/p>\n

The data science and machine learning platform\u00a0Neural Designer<\/a> helps banks, insurance companies, telecommunications companies, and retail companies detect and prevent these fraudulent payments.<\/p>\n

Contents<\/h3>\n
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  1. Objectives<\/a>.<\/li>\n
  2. Benefits<\/a>.<\/li>\n
  3. Approach<\/a>.<\/li>\n
  4. Results<\/a>.<\/li>\n
  5. Conclusions<\/a>.<\/li>\n<\/ol>\n

    <\/p>\n

    \n

    Objectives<\/h2>\n

    Fraud detection has become one of the biggest challenges for all companies.<\/p>\n

    Frauds pose significant problems for any business, and specialized analysis techniques are required to detect them effectively.<\/p>\n

    However, it is not easy to develop these techniques to detect fraud.<\/p>\n

    By harnessing the power of fraud data sets, companies can develop predictive models that enable the detection of any possible fraud.<\/p>\n

    <\/p>\n

    Some of the factors that can influence committing fraud are the following:<\/p>\n