Pendahuluan
Kejahatan (fraud/scam) kartu kredit masih terus terjadi dan angka kasus penipuan masih terus meningkat dari tahun ke tahun. Hal ini telah menjadi permasalahan internasional hingga saat ini, termasuk Indonesia dengan jumlah kasus yang cukup tinggi. Berdasarkan data yang didapat dari berbagai kasus kejahatan perbankan yang terjadi di Indonesia pada tahun 2017 – 2018, ditemukan bahwa hampir 50 % kasus kejahatan perbankan terjadi pada bank pemerintah dan 80% pelaku kejahatan perbankan adalah di tingkat manajemen. Belum lagi ditambah kasus kejahatan dari pihak lain, seperti pencurian kartu kredit/debit, pengambilalihan akun (account takeover), kartu tiruan (counterfeit cards), penipuan dalam pengajuan kartu (fraudulent application), cetakan berkali-kali (multiple imprints), dan kejahatan melalui pemesanan surat, telepon maupun e-commerce (Mail order, telephone order or e-commerce fraud).
Pengawasan pada tingkat internal perbankan yang masih lemah serta kepercayaan nasabah kepada perbankan, dijadikan kesempatan untuk melancarkan aksi kejahatan oleh para pelaku.
Diperlukan waktu beberapa bulan untuk dapat mendeteksi aksi kejahatan (fraud/scam).
Pada proposal ini, kami menawarkan sebuah sistem yang mampu mendeteksi apakah sebuah transaksi perbankan itu sah (legit) atau tidak sah (fraud) secara real-time dengan menggunakan sebuah kecerdasan buatan (artificial intelligence) dan machine learning.
Latar Belakang
Berikut ini adalah latar belakang yang menjadi permasalahan umum:
- Transaksi jual/beli online di Indonesia menaik sangat besar dan pesat.
- Penggunaan E-Commerce Indonesia Tertinggi di dunia, hingga 88,1 % [Data April 2021].
- Nilai transaksi e-commerce sebesar Rp 266 Triliun.
- Angka ini akan terus meningkat pada tahun berikutnya.
- 86 % pengguna e-commerce menjadi korban cyber-crime (fraud).
- 91 juta akun dan kata sandi Pengguna Tokopedia dijual di Dark Web.
- Apakah ada kerugian ?
Menurut data statistik tahun 2020 yang bisa saya dapat, terdapat kasus fraud sebagai berikut:
- Scam : 26 Miliar USD
- Cyber-crime : 3,5 Miliar USD
- E-Commerce : 25 Miliar USD
- Penipuan lain : 2,48 Miliar USD
Tipe Kejahan (Fraud) E-Commerce
Pada dasarnya ada 6 tipe kejahatan (fraud) pada dunia e-commerce, seperti terlihat pada Gambar dibawah ini.
Kenapa hal ini bisa terjadi ?
Menurut hasil literatur, dapat disampaikan bahwa:
- 60 % Anggaran Cybersecurity kurang.
- 69 % Biaya Cybersecurity tidak berkelanjutan.
- 82 % Chief Information Security Officer (CISO) sangat kewalahan.
- 62 % Tim Cybersecurity kekurangan staf.
- 57 % Posisi staf tidak terisi.
- 72 % Dept. HR tidak paham kebutuhan Cybersecurity.
- 85 % Staf IT hanya memiliki min. 1 sertifikat.
- 60 % Website hanya mengandalkan HTTPS
- 74 % Website phising melalui HTTPS
- Ancaman orang dalam.
- Kelalaian atau disengaja.
- reCaptcha palsu.
- 65 % Kata sandi yang lemah.
- > 3.900 User mobile banking terkena phising
AI – Fraud Detector
AI-Fraud Detector menggunakan Machine Learning untuk mendeteksi fraud dengan mengintervensi transaksi yang sedang terjadi antara Bank dan web E-commerce dan/atau antara Client dan web E-commerce. Machine Learning digunakan karena dia dapat memberikan deteksi fraud yang baik dan efisien. Sebagai tambahan, proses ini dilakukan sepenuhnya otomatis dan memungkian untuk mendeteksi variasi penipuan dan kebiasaan model penipuan.
Alur kerja operasi AI-Fraud Detector dapat dilihat pada diagram berikut.
Dan AI-Fraud Detector ini bekerja secara Real-Time. Berikut alur diagram cara kerja Real-time Fraud Detection.
Aristektur Sistem
Arsitektur sistem AI-Fraud Detector dapat diilustrasikan sebagai berikut.
Objektif AI-Fraud Detector
Tentunya tools AI-Fraud Detector ini memiliki objektif, yaitu:
Mengurangi atau meminimalisir penipuan (fraud) dari beberapa sudut dan bentuk, seperti :
–pencurian identitas,
–Pemalsuan,
–Penyalahgunaan, dan
–Penipuan online.
Contoh Penerapan AI-Fraud Detector di Eropa
Di Eropa pada umumnya, dan khususnya di Perancis dimana saya berperan pada team pengembangan ini, telah memiliki kesepakan bersama diantara negara-negara Eropa.
Sebelum Penerapan AI-Fraud Detector:
– Proses Fraud detection dilakukan secara manual.
– Perlu ~116 hari (4 bulan) untuk mendeteksi sebuah fraud.
– Tingkat Fraud detection error tinggi.
Sesudah Penerapan AI-Fraud Detector:
– Proses Fraud detection secara otomatis.
– Instan dan real-time.
– Tingkat error rendah.
Penutup
Demikian sekilas penjelasan AI-Fraud Detector project yang saya dan team kembangkan. Terima kasih sudah membaca artikel singkat ini. Silahkan, jika ada yang belum mengerti atau ada pertanyaan, tuliskan pada kolom komentar dibawah. Atau silahkan hubungi saya.
Semoga bermanfaat.
Colmar, 16/08/2023. Summer.
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