Penilaian risiko untuk penggunaan Perbankan Retail
NA
$X,XX per pihak retail per tahun
(penggunaan dihitung prorata)
Penilaian risiko untuk penggunaan Perbankan Komersial
Komersial skala kecil
(<500 transaksi/bulan)
$X,XX per pihak komersial skala kecil per tahun
(penggunaan dihitung prorata)
Komersial skala besar
(>=500 transaksi/bulan)
$X,XX per pihak komersial skala besar per tahun
(penggunaan dihitung prorata)
Harga penggunaan produksi dihitung berdasarkan jumlah pihak (nasabah bank) yang dinilai risikonya.
Pihak retail adalah perorangan yang menggunakan layanan perbankan untuk keperluan pribadi sedangkan pihak komersial adalah perusahaan atau perorangan yang memiliki rekening untuk keperluan bisnis. Mesin model dan skema data yang berbeda digunakan untuk menilai risiko pencucian uang di model dan skema data masing-masing.
Pihak komersial dibagi menjadi perusahaan kecil dan besar berdasarkan jumlah rata-rata transaksi bulanan pihak tersebut selama 365 hari sebelumnya.
Pihak harus terdaftar di layanan untuk mendapatkan prediksi. Pihak tanpa niat prediksi dapat dihapus kapan saja, tetapi periode pendaftaran minimum 45 hari berlaku untuk pihak dengan niat prediksi, setelah itu pihak yang berlaku dapat membatalkan pendaftaran berdasarkan layanan. Pendaftaran untuk pelatihan, penyesuaian, atau backtesting tidak diperlukan.
Pihak didaftarkan per instance AML AI menggunakan metode instances.importRegisteredParties. Pihak yang terdaftar di satu instance akan terdaftar di instance lain dan akan tetap terdaftar selama minimal 45 hari jika diprediksi, sebelum dapat dihapus dari registry. Penagihan dilakukan untuk setiap instance secara terpisah selama periode pendaftaran pelanggan.
Daftar pihak yang terdaftar saat ini dapat diambil menggunakan metode instances.exportRegisteredParties.
Anda dapat mendaftarkan pelanggan retail dan komersial di instance yang sama.
Pelatihan dan penyesuaian model
SKU
Tingkat Harga
Pelatihan
$X,XXXX per pihak dalam set data
Penyesuaian
$X,XXXX per pihak dalam set data
Harga pelatihan dan penyesuaian dihitung berdasarkan jumlah pihak dalam set data yang digunakan untuk melatih model atau menyesuaikan mesin. AML AI melakukan pelatihan saat Anda membuat resource dan penyesuaian Model ketika membuat resource Engine Config.
AML AI tidak mengenakan biaya penyesuaian untuk konfigurasi mesin yang mewarisi hyperparameter. Untuk mengetahui detail tentang mewarisi hyperparameter sebagai alternatif penyesuaian, lihat Mengonfigurasi mesin.
1 Harga tahunan ditampilkan demi kemudahan. Semua harga dihitung secara prorata sesuai periode pendaftaran pihak.
Meminta penawaran harga khusus
Dengan model harga bayar sesuai penggunaan Google Cloud, Anda hanya membayar untuk layanan yang Anda gunakan. Hubungi tim penjualan kami untuk mendapatkan penawaran harga khusus bagi organisasi Anda.
[[["Mudah dipahami","easyToUnderstand","thumb-up"],["Memecahkan masalah saya","solvedMyProblem","thumb-up"],["Lainnya","otherUp","thumb-up"]],[["Sulit dipahami","hardToUnderstand","thumb-down"],["Informasi atau kode contoh salah","incorrectInformationOrSampleCode","thumb-down"],["Informasi/contoh yang saya butuhkan tidak ada","missingTheInformationSamplesINeed","thumb-down"],["Masalah terjemahan","translationIssue","thumb-down"],["Lainnya","otherDown","thumb-down"]],[],[[["\u003cp\u003eAML AI pricing is determined by the number of a bank's customers (parties) that the service is used for, and the extent of model training and tuning.\u003c/p\u003e\n"],["\u003cp\u003eRisk scoring for retail and commercial banking usage is priced per party per year, prorated to actual usage, with commercial parties further categorized as small or large based on transaction volume.\u003c/p\u003e\n"],["\u003cp\u003eParties are registered with the service to receive predictions, and while they can be removed if prediction intent is not needed, a minimum 45-day registration period applies to those with prediction intent.\u003c/p\u003e\n"],["\u003cp\u003eTraining and tuning of models are priced based on the number of parties in the dataset used for these processes, with costs incurred when creating Model or Engine Config resources.\u003c/p\u003e\n"],["\u003cp\u003eFor detailed pricing and custom quotes tailored to specific organizations, it is recommended to contact a Google Cloud Representative.\u003c/p\u003e\n"]]],[],null,["# Pricing\n\nAnti Money Laundering AI pricing\n================================\n\n\n| **Important:** For full AML AI pricing details, please contact your [Google Cloud Representative](/contact).\n\n\u003cbr /\u003e\n\nOverview\n--------\n\nAML AI pricing is based on two factors:\n\n1. The number of a bank's customers (referred to as parties) a Google Cloud customer uses AML AI for, billed daily\n2. The amount of experimentation a Google Cloud customer uses for training and tuning models against their datasets\n\nProduction usage for risk scoring\n---------------------------------\n\nProduction usage is priced by the number of the parties (bank's customers) which are scored for risk.\n\n- Retail parties are individuals who use banking services for personal usage whereas commercial parties are companies or individuals with accounts used for business purposes. Different model engines and data schemas are used to score for the risk of money laundering in each. **Note:** Some small business owners or self-employed people may fall into both the retail and commercial category.\n- Commercial parties are divided into small and large companies based on the average number of monthly transactions for the party over the preceding 365 days.\n- Parties must be registered with the service to obtain predictions. Parties without prediction intent can be removed at any point, but a minimum registration period of 45 days applies to parties with prediction intent, after which the applicable party may be deregistered under the service. No registration is required for training, tuning, or backtesting.\n- Parties are registered per AML AI instance using the `instances.importRegisteredParties` method. Parties registered in one instance are registered in other instances and will remain registered for a minimum of 45 days if predicted on, before they can be removed from the registry. Billing occurs for each instance separately for the period the customer is registered for.\n- The lists of currently registered parties can be retrieved using the `instances.exportRegisteredParties` method.\n- You can register both retail and commercial customers on the same instance.\n\nTraining and tuning of models\n-----------------------------\n\nTraining and tuning is priced based on the number of parties in the dataset used to train a model or tune an engine. AML AI does training when you create a Model resource and tuning when you create an Engine Config resource.\n\nAML AI does not charge tuning fees for engine configurations that inherit hyperparameters. For details on inheriting hyperparameters as an alternative to tuning, see [Configure an engine](/financial-services/anti-money-laundering/docs/configure-engine).\n\n^1^ Annual pricing displayed for convenience, all prices are prorated to the period a party was registered for. \n\n#### Request a custom quote\n\nWith Google Cloud's pay-as-you-go pricing, you only pay for the services you use. Connect with our sales team to get a custom quote for your organization.\n[Contact sales](/contact?direct=true)"]]
