Dynamic Credit Scoring Agent

Agent Description:

The Dynamic Credit Scoring Agent evaluates a customer's real-time creditworthiness by combining traditional credit data with behavioral insights from recent payment history. It enables adaptive credit decisions by dynamically adjusting credit scores and recommended credit limits based on emerging trends in financial behavior.

Purpose and Components
  • Purpose: To generate a real-time, behavior-aware credit risk score for users by integrating static credit data with dynamic payment patterns.
  • Components:
    • A credit profile retriever that consolidates a user’s financial background.
    • A behavioral analysis agent that interprets payment trends to update credit risk assessments.
Supported Capabilities
  • Fetching comprehensive financial profiles via API (including credit history and demographic data).

  • Querying and analyzing recent payment behavior for consistency and recency.

  • Identifying anomalies or risk patterns in payment history.

  • Synthesizing behavioral and traditional data into a unified risk score.
  • Generating a final risk report that includes a Dynamic Credit Score and reasoning.
LLM Used
  • Google Vertex
    Note: To learn more about the LLM and to modify its behavior, refer to the Configuring LLM settings section.

Sub-Agents

1. Review Credit Agent

  • Role: Credit Reviewer.

    Scope: Collects complete user financial information from a central system.

  • Description: This subagent accepts a user identifier (for example, userID: USR-94301) and connects to the central financial database using the GET Request Tool to retrieve the user's consolidated credit profile. The retrieved JSON includes personal details, credit history, income level, liabilities, and other relevant financial indicators. The structured profile is passed to the next subagent for payment analysis.
  • Tool Used: GET Request Tool
  • LLM Used: Google Vertex (inherits from parent).

2. Analyze Payment Agent

  • Role: Payment Analyst.
  • Scope: Analyzes payment patterns and computes dynamic credit scores.
  • Description: This subagent receives the structured financial profile from the Review Credit Agent and uses the GET Request Tool to fetch detailed payment history (for example, from userpayment.json) for the same user ID. It analyzes the data to detect trends such as timely payments, late payments, missed dues, and frequency of repayments. These behavioral insights are combined with static credit indicators from the profile to compute a Dynamic Credit Score. The agent outputs a detailed credit assessment, including score justifications and risk category (for example, Low, Medium, High).
  • Tool Used:GET Request Tool
  • LLM Used: Google Vertex (inherits from parent).
Tools Used:
  • GET Request Tool : for API data retrieval from credit and payment databases.
Note: For details on modifying the Tools, refer Tools Library section.
Agent Workflow Behavior Summary
  • The Review Credit Agent initiates the process by fetching the full user profile using the user ID.
  • The Analyze Payment Agent enhances this profile by retrieving detailed payment data and interpreting behavioral trends.
  • It then synthesizes both data sources into a final output that includes a Dynamic Credit Score, a short risk summary, and credit adjustment recommendations if applicable.
Sample Questions:
  • Can you assess the current credit score for this user based on recent payments?
  • Has this customer’s risk profile changed in the last 3 months?