Aplikasi Notifikasi Mobile untuk Pencegahan Fraud

Telepon mobile pintar sudah banyak dimanfaatkan dalam melakukan transaksi perbankan misalnya melihat saldo rekening dan riwayat transaksi, transfer dana antar rekening, atau membayar tagihan secara online. Dalam beberapa tahun belakangan, peretasan transaksi dari perbankan melalui kartu kredit meningkat sangat tinggi, misalnya pada tahun 2013, sekelompok hacker berhasil membobol Bank Muscat yang berada di negara Oman dan menyebabkan kerugian senilai 45 juta USD.Dalam paper ini dikembangkan sebuah prototipe notifikasi pencegahan kejahatan fraud berbasis mobile. Sampel data digenerate dari sampel random transaksi nasabah dan beberapa data nasabah. Prototype kemudian diuji pada berbagai kondisi normal, dan ekstrim. Berdasarkan hasil pengujian, didapat beberapa variasi notifikasi fraud, seperti pada jam sibuk dan tidak sibuk. Notifikasi yang dikirimkan pada jam sibuk relatif lebih cepat dibandingkan saat jam tidak sibuk

Aplikasi Notifikasi Mobile untuk Pencegahan Fraud

Telepon mobile pintar sudah banyak dimanfaatkan dalam melakukan transaksi perbankan misalnya melihat saldo rekening dan riwayat transaksi, transfer dana antar rekening, atau membayar tagihan secara online. Dalam beberapa tahun belakangan, peretasan transaksi dari perbankan melalui kartu kredit meningkat sangat tinggi, misalnya pada tahun 2013, sekelompok hacker berhasil membobol Bank Muscat yang berada di negara Oman dan menyebabkan kerugian senilai 45 juta USD.Dalam paper ini dikembangkan sebuah prototipe notifikasi pencegahan kejahatan fraud berbasis mobile. Sampel data digenerate dari sampel random transaksi nasabah dan beberapa data nasabah. Prototype kemudian diuji pada berbagai kondisi normal, dan ekstrim. Berdasarkan hasil pengujian, didapat beberapa variasi notifikasi fraud, seperti pada jam sibuk dan tidak sibuk. Notifikasi yang dikirimkan pada jam sibuk relatif lebih cepat dibandingkan saat jam tidak sibuk

Learning Fraud Detection from Big Data in Online Banking Transactions: A Systematic Literature Review

The implementation of fraud detection in online banking transactions on big data is one of the most important strategies applied by banks to protect their transactions and highly related to algorithms. In fact, it is not easy to successfully implement this strategy because it requires a huge investment and is influenced by complexity algorithms, training, and testing. The frauds bring fatal impact, such as destruction of the banking reputation, banking loss, and state financial loss. One target of the fraud perpetrators in banking is online banking transactions. Security has become a major issue in the online banking transaction. Furthermore, the research of fraud is switching to big data and turns out that online banking data are stored in the database operational and big data. This study aims to find out what kind of algorithms fraud detection for online banking transactions using a systematic literature review to the 25 relevant papers

Scenario-based requirements elicitation for user-centric explainable AI

Explainable Artificial Intelligence (XAI) develops technical explanation methods and enable interpretability for human stakeholders on why Artificial Intelligence (AI) and machine learning (ML) models provide certain predictions. However, the trust of those stakeholders into AI models and explanations is still an issue, especially domain experts, who are knowledgeable about their domain but not AI inner workings. Social and user-centric XAI research states it is essential to understand the stakeholder’s requirements to provide explanations tailored to their needs, and enhance their trust in working with AI models. Scenario-based design and requirements elicitation can help bridge the gap between social and operational aspects of a stakeholder early before the adoption of information systems and identify its real problem and practices generating user requirements. Nevertheless, it is still rarely explored the adoption of scenarios in XAI, especially in the domain of fraud detection to supporting experts who are about to work with AI models. We demonstrate the usage of scenario-based requirements elicitation for XAI in a fraud detection context, and develop scenarios derived with experts in banking fraud. We discuss how those scenarios can be adopted to identify user or expert requirements for appropriate explanations in his daily operations and to make decisions on reviewing fraudulent cases in banking. The generalizability of the scenarios for further adoption is validated through a systematic literature review in domains of XAI and visual analytics for fraud detection

Aplicação de técnicas de descoberta do conhecimento em investigações de lavagem de dinheiro.

Lavagem de dinheiro é um método utilizado por criminosos para dar aparência lícita a recursos obtidos de maneira ilícita. Estimativas de entidades mundialmente reconhecidas apontam que tal atividade é responsável por algo entre 2 e 5% do PIB mundial e está se tornando cada vez mais sofisticada. Pela dificuldade de identificação utilizando métodos tradicionais de investigação, a tecnologia tem desempenhado um papel importante nesse processo. Busca-se com este trabalho identificar as técnicas de descoberta do conhecimento aplicadas nas investigações da lavagem de dinheiro, o que foi conseguido através de uma revisão sistemática de literatura. As técnicas encontradas serão utilizadas em uma pesquisa experimental que visa compará-las quanto à eficácia na identificação de relacionamentos em uma rede de transações bancárias provenientes de uma investigação real de lavagem de dinheiro