Agents-internal

About this Agent

The AML Compliance Monitoring Agent is an AI digital worker designed to ensure adherence to anti-money laundering regulations. It operates by continuously monitoring financial transactions, identifying and flagging any suspicious activities that may indicate money laundering. This proactive surveillance tool helps financial institutions protect themselves from legal risks and maintain regulatory compliance by providing timely alerts for further investigation.

Accuracy
TBD

Speed
TBD

Input Data Set

Sample of data set required for AML Compliance Monitoring Agent:

Aml input transactions

Transaction ID	Sender Name	Recipient Name	Transaction Date	Amount	Transaction Type	Country	Previous Transactions
001	John Smith	David Brown	2024-10-13	15000	Credit	USA	5000
002	Mary Johnson	Emily Davis	2024-10-14	2000	Debit	England	2000
003	James Wilson	Michael Clark	2024-10-14	300000	Credit	Cayman Islands	15000
004	Linda Williams	Olivia Johnson	2024-10-14	8000	Debit	England	8000

Deliverable Example

Sample output delivered by the AML Compliance Monitoring Agent:

Transaction ID	Sender Name	Amount	Reason for Flagging	Flag Date
001	John Smith	15000	Large amount and Unusual deviation from previous transactions	2024-10-14
003	James Wilson	300000	Large amount and High-risk country: Cayman Islands and Unusual deviation from previous transactions	2024-10-14

ZBrain credits and their usage

A credit is a unit of usage on ZBrain. Credits are consumed whenever you perform actions such as embedding documents or querying an app. Here’s how credits are utilized:

Document Embedding:

When you upload a document, it is processed and embedded to create a searchable vector representation. This step consumes credits based on the size of the document and the embedding model used.

Input Processing:

When you ask a question, the relevant context is retrieved from the embeddings and processed by the Large Language Model (LLM). This step incurs a credit cost based on the model and the number of input tokens.

Generating Output

The LLM then generates a response based on the processed input, consuming additional credits based on the number of output tokens.

The cost in credits for various models and processes is detailed below:

Models and Credit Consumption:

Model	Input Cost	Output Cost
GPT-4o	2,500 credits / 1M tokens	7,500 credits / 1M tokens
GPT-4	15,000 credits / 1M tokens	30,000 credits / 1M tokens
GPT-4-32k	30,000 credits / 1M tokens	60,000 credits / 1M tokens
GPT-3.5 Turbo	250 credits / 1M tokens	750 credits / 1M tokens

Embedding Models:

Model	Input Cost
text-embedding-3-small	20 credits / 1M tokens
text-embedding-3-large	130 credits / 1M tokens
ada v2	100 credits / 1M tokens

When you create a knowledge base or query an app, credits are deducted based on the embedding, input, and output token usage. This ensures you only pay for what you use while leveraging the full power of advanced AI models.

AML Compliance Monitoring Agent

About this Agent

Input Data Set

Deliverable Example

Company

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