Agents-internal

About this Agent

The Treasury Compliance Monitoring Agent is a dedicated AI digital worker designed to ensure strict adherence to both internal and external treasury policies. This includes vigilant monitoring of regulations related to cash management and liquidity planning. By continuously analyzing transactions and financial activities, this AI worker identifies potential compliance issues and provides actionable insights to maintain regulatory standards, ensuring smooth and compliant treasury operations.

Accuracy
TBD

Speed
TBD

Input Data Set

Sample of data set required for Treasury Compliance Monitoring Agent:

Treasury input

Transaction ID	Account Holder Name	Transaction Date	Amount	Currency	Policy Compliance
TX1001	James Anderson	2024-10-01	50000	USD	Yes
TX1002	Emily Davis	2024-10-03	125000	USD	Yes
TX1003	Oliver Wilson	2024-10-05	75000	GBP	No
TX1004	Liam Brown	2024-10-06	42000	USD	Yes
TX1005	Charlotte Johnson	2024-10-08	68000	USD	No
TX1006	Ava Taylor	2024-10-10	55000	GBP	Yes

Deliverable Example

Sample output delivered by the Treasury Compliance Monitoring Agent:

Transaction ID	Account Holder Name	Status	Issues Found	Action Required
TX1001	James Anderson	Compliant	None	None
TX1002	Emily Davis	Compliant	None	None
TX1003	Oliver Wilson	Non-Compliant	Exceeds liquidity threshold	Review and Correct
TX1004	Liam Brown	Compliant	None	None
TX1005	Charlotte Johnson	Non-Compliant	Missing documentation	Submit Documents
TX1006	Ava Taylor	Compliant	None	None

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.

Treasury Compliance Monitoring Agent

About this Agent

Input Data Set

Deliverable Example

Company

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