Big data for automated fraud detection

Big Data plays a major role in automated fraud detection. Thanks to in-depth data analysis, fraud detection systems can identify suspicious patterns and behavior, enabling early and accurate detection of fraudulent activity. This intensive use of data strengthens fraud detection mechanisms, and contributes to effective risk management within organizations. The data collected and analyzed also helps to strengthen internal audit and internal control, providing a more in-depth view of fraud risks, and facilitating the automatic detection of fraud.

To tackle the problem of fraud, companies are calling on data scientists to exploit the massive volumes of data and identify these risks. The aim of the external audit is to ensure that internal control systems are in place and effective in preventing fraud. The implementation of a robust internal control system, combined with real-time data analysis, enables suspicious patterns to be quickly identified, and action to be taken proactively.

Big Data to identify suspicious activity

Machine learning plays a crucial role in fraud detection and prevention. Thanks to this form of artificial intelligence, financial institutions can analyze vast quantities of data in real time to spot suspicious behavior, such as that of bots or hackers. When suspicious activity is identified, additional security measures can be put in place to authenticate the user, and ensure their identity. Machine learning algorithms are also able to detect emerging threats or money laundering scenarios by spotting anomalies, unlike policy-based systems which can only detect familiar attacks. This approach avoids the need to maintain numerous individual rules for each type of fraud.

Big Data and internal audit

Internal auditors work with data scientists to implement advanced risk management and fraud prevention systems. Using Internet of Things technologies and real-time data analysis tools, they can detect suspicious behavior. The aim of the internal audit is to evaluate and improve the internal control system, a continuous process designed to protect the company against risk and fraud. The use of Big Data and real-time data analysis therefore enables internal auditors to carry out a more in-depth risk analysis and take preventive measures more quickly to reduce the risk of fraud. In addition, the use of open source technologies offers internal auditors opportunities to innovate and customize their audit methods to better respond to these challenges and risks.

Big Data for substantial productivity gains

Automated fraud detection using data offers businesses a major advantage in terms of productivity. Automated tools can process large amounts of data quickly, allowing teams to focus on the most complex frauds. Technologies such as data mining and machine learning also enable anomalies to be detected quickly, significantly saving time compared with manual detection methods.

Advanced algorithms for more effective detection

Companies, particularly financial institutions, are increasingly concerned about fraudulent transactions. To counter this, they are increasingly adopting machine learning algorithms to detect and even prevent fraudulent activity. Machine learning algorithms are used to identify patterns in large pools of data, revealing potential fraudulent behavior. By analyzing past transactions, these algorithms can spot suspicious behavior, such as many transactions from the same account or a sudden increase in spending on a specific card. This approach enables organizations to quickly identify and investigate potential fraudulent activity, minimizing their risks.

Big Data for better regulatory compliance

Automated fraud detection tools are also a key element of regulatory compliance. Regulators require companies to have robust internal control systems in place to prevent fraud and abuse. Automated fraud detection enables companies to meet these requirements more effectively, by quickly identifying potential fraud and taking action to prevent it.

Automated fraud detection is an essential asset for internal control teams. Using data, it enables potential frauds to be detected quickly, saving time and making risk management more effective. Automated fraud detection tools are essential to help companies comply with regulations, and protect their assets from fraud threats. Sign up to Kantik, our automated fraud detection software, to find out how it can improve your internal controls.

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Inferensia is a data and innovation consultancy that brings together strategy consultants and other experts. We develop strategies to transform public and private organizations while making data a growth factor.

Beyond the collective and individual interest of its teams, Inferensia also positions itself as a major player in innovation for its clients, thus incubating the best ideas of our partners, clients and collaborators. We do not focus solely on "Delivery", "Technology" or "Doing" (traditional vision) but above all on ROI and usage (innovative vision) on which our achievements are based.