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Sutardiman, Mario
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Computer Science & IT

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Risk Assessment and Detection of Fraudulent Claims in Insurance Systems with Machine Learning Approaches Sutardiman, Mario; Arditya, Dyah Ayu; Suroso, Jarot S
Sebatik Vol. 28 No. 2 (2024): December 2024
Publisher : STMIK Widya Cipta Dharma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46984/sebatik.v28i2.2522

Abstract

Fraudulent insurance claims pose a significant challenge to the sustainability and efficiency of insurance systems, resulting in substantial financial losses and eroding trust between insurers and policyholders. The complexity and volume of modern data make traditional fraud detection methods, such as manual assessments, increasingly ineffective. This study investigates the application of machine learning approaches, including Random Forest and Artificial Neural Networks (ANN), to detect fraud in insurance claims. Using a structured methodology, models were trained on historical claim data and evaluated using metrics such as accuracy, F1-score, recall, and precision. The Random Forest algorithm achieved an accuracy of 94%, while the ANN demonstrated superior performance on controlled datasets. Feature importance analysis identified key predictors, including claim amount and submission frequency, offering actionable insights for fraud prevention strategies. The integration of machine learning into claims management systems provides a scalable, accurate, and cost-effective solution to combating fraud. Future research will focus on testing with larger datasets and exploring hybrid approaches to enhance robustness and adaptability.
Improving Meta Ads Efficiency through Multi-Level Campaign Structuring and Budget Optimization Sutardiman, Mario; Mantoro, Teddy
TEPIAN Vol. 6 No. 2 (2025): June 2025
Publisher : Politeknik Pertanian Negeri Samarinda

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51967/tepian.v6i2.3386

Abstract

The rise of digital advertising has transformed the way businesses interact with consumers, making platforms like Meta Ads a cornerstone of marketing strategies. However, achieving optimal efficiency in Meta Ads remains challenging due to the complexity of campaign setups and budget allocation. This study addresses the issue by examining key configurations at three levels: campaigns, ad sets, and individual ads. The research explores how advertisers can tailor campaigns to specific objectives, such as driving traffic or increasing sales, while leveraging ad set customization for audience targeting, placement optimization, and A/B testing. To improve ad performance, this study emphasizes the importance of refining content at the ad level, ensuring alignment with campaign goals. Budget management is also highlighted, contrasting Campaign Budget Optimization (CBO) with Ad Set Budget Optimization (ABO), and offering insights into leveraging these tools to maximize returns. The study further recommends adjusting budgets based on audience behavior patterns, such as spikes in purchasing activity during twin dates or paydays. By providing actionable strategies for configuring Meta Ads, this study contributes to the field of digital marketing by bridging practical implementation and theoretical insights. Evaluation of these strategies is supported through examples of best practices, with recommendations for advertisers to enhance their Meta Ads efficiency through continual testing and strategic budgeting.
Co-Authors Arditya, Dyah Ayu Suroso, Jarot S Teddy Mantoro