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Pattern Recognition

Machine Learning Models

Natural Language Processing (NLP)

Real-Time Analysis

Predictive Analytics

Network Analysis

Biometric Verification

The branch of artificial intelligence known as machine learning (ML) has been widely used in fraud detection using a variety of techniques. Initially, one of the most widely used methods was to identify outliers by fitting samples to a Gaussian function. Thus, anomaly detection algorithms can recognize unusual patterns in large data sets, while supervised machine learning models can predict the probability of fraud based on historical patterns. This dual approach provides a comprehensive framework for identifying and preventing fraudulent activities. More modern techniques employ, for example, neural networks to find atypical patterns in a dataset.

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FRAUD DETECTION AUTOMATION THROUGH DATA ANALYTICS AND ARTIFICIAL INTELLIGENCE

Wishmy Meinawa Ikhsan , Elzami Haqie Ednoer , Winanda Setyaning Kridantika , Amrie Firmansyah

Riset : Jurnal Aplikasi Ekonomi, Akuntansi dan Bisnis Vol. 4 No. 2, September 2022, Hal 103 – 119

"Big data is a good strategy for providing basic information, which is a significant advantage for its users. However, big data takes a long time, accuracy is not guaranteed, and costs are expensive. Given the availability of huge and complex amounts of data, big data processing will be optimal if it involves a computer system to analyze further the data they get to draw conclusions and take action. This system is called artificial intelligence (AI). AI has intelligence in thinking, has a broad knowledge base in a limited domain, and uses structured reasoning in making decisions to solve problems. It is considered the leading solution for various cases of auditor failure in detecting fraud. AI can provide convenient automation and control and improve audit process efficiency. Muawanah et al. (2022) further observed how auditors perceive the ease with which AI can improve the audit process. It was concluded that the auditor considered the AI system to facilitate the audit process, although it is undeniable that there will also be direct threats to employees' work. In addition, in its implementation, auditors will undoubtedly face challenges in implementing AI to improve the audit process. Specifically, internal audit has a significant role in fraud detection. The reporting and internal auditing media are the most effective means of detecting fraud (ACFE Indonesia, 2020). Reporting media originating from reports of employees of the company/organization is the tool with the most significant contribution to uncovering fraud. Furthermore, an internal audit is also very effective in the early detection of fraud. Internal audits can detect fraud better by utilizing data analytics and AI techniques. It is at the same time to maintain and increase the relevance of the internal auditor function.

This study concludes that various fraud detection models based on data analytics and artificial intelligence have a high accuracy value in improving audit quality. These models with various algorithms, variables, and input data can be a blueprint for developing a fraud detection system following the organization's characteristics."

The use of AI in fraud detection involves several sophisticated techniques and technologies which helps in identifying fraudulent activities more accurately than traditional methods. Here are some ways AI can be used in fraud detection:

• AI utilizes machine learning (ML) algorithms to analyze patterns and trends in vast datasets. By training on historical data, these models can learn to identify suspicious activities that may indicate fraud.
• Deep learning (subset of ML) uses neural networks with many layers, can be particularly effective in detecting complex fraud patterns that are difficult for humans or simpler algorithms to identify.
• In some case we can leverage NLP, which allows AI systems to analyze textual data for potential signs of fraud.
• By using AI to forecast future trends based on historical data, we can predict potential fraud scenarios before they occur.

One simple idea for using AI in fraud detection is to employ machine learning algorithms to analyze patterns in financial transactions. By feeding historical data into the AI system, it can learn what normal transaction patterns look like for a given user or account. Then, when new transactions occur, the AI can flag any that deviate significantly from the established patterns as potential instances of fraud. This approach allows for real-time monitoring of transactions and can help financial institutions or businesses identify and prevent fraudulent activity more effectively.