Evaluate customer creditworthiness using Adjutor’s Oraculi Borrower Scoring.

Introduction

Borrower credit scoring is one of the most important processes in digital lending because it helps lenders evaluate risk, reduce default rates, and make faster lending decisions. Financial institutions use borrower scoring systems to assess whether a loan applicant is likely to repay a loan based on factors such as income, employment status, repayment history, financial behavior, and loan affordability.

For lenders, automated borrower scoring improves credit decision accuracy while reducing manual underwriting time. It also helps standardize approval workflows across lending teams and enables faster loan processing at scale. By integrating borrower scoring directly into loan decision models, lenders can automatically evaluate applications against predefined credit rules before approvals are made.

The Adjutor platform provides a reliable and efficient way to validate borrower creditworthiness through the Oraculi Borrower Scoring endpoint. This endpoint enables lenders to submit borrower data and receive scoring results that can be incorporated into automated lending workflows, decision engines, and risk assessment systems.

This guide explains how to configure borrower scoring within your decision model, prepare the required request data, and score borrowers using the Oraculi Borrower Scoring endpoint.

What is the Oraculi borrower scoring endpoint?

The Oraculi Borrower Scoring endpoint is a scoring service available through Adjutor that enables lenders to evaluate borrower creditworthiness programmatically.

The endpoint works by analyzing borrower data submitted within a request payload and returning a scoring outcome based on configured decision model rules and scoring logic.

Lenders can integrate this endpoint into:

• Loan application workflows
• Automated underwriting systems
• Risk assessment pipelines
• Credit approval processes
• Loan eligibility checks
• Internal credit evaluation systems

The scoring process becomes especially valuable for lenders handling high loan volumes because it reduces manual review dependency and improves operational efficiency.

Why borrower scoring is important for lenders

Effective borrower scoring helps lenders make more informed and data-driven lending decisions.

Some key benefits include:

• Faster loan approvals
• Improved risk management
• Reduced default rates
• More consistent credit decisions
• Better loan portfolio quality
• Increased operational efficiency
• Scalable underwriting processes
• Enhanced fraud prevention

Borrower scoring also enables lenders to create customized lending rules for different loan products, customer segments, and repayment risk categories.

How borrower scoring works

The borrower scoring process typically follows these stages:

1. A borrower submits a loan application
2. The application data is passed into a decision model
3. The scoring module evaluates borrower information
4. The Oraculi scoring endpoint processes the request
5. A scoring outcome is returned
6. The lender uses the result to approve, reject, or further review the application

This process can happen in real time, allowing lenders to automate large portions of their loan approval workflows.

Prerequisites 

Modifying or creating a Decision Model:

To use borrower scoring, you must first configure a decision model within your lender admin console.

Decision models define the rules, validations, and workflows used during loan processing and credit evaluation.

To create or modify a decision model:

1. Log in to your lender admin console
2. Navigate to the decision model configuration section
3. Create a new decision model or modify an existing one
4. Save your configuration changes

For additional guidance, refer to the related documentation on modifying a decision model.

Include a Scoring Module:

After creating or updating the decision model, you must include a scoring module within the decision model settings.

The scoring module enables the system to perform borrower scoring during loan evaluation.

You can enable this by setting the scoring requirement to true within your configuration settings.

"scoring": { "required": true, "sequece": 4, "continue_on_failure": false }

Ensure the data for the scoring user is available:
For borrower scoring to work accurately, the request payload must include sufficient borrower information.

The system uses this data to evaluate the borrower’s financial profile and repayment capability.

At minimum, ensure the following are included:

• Product ID
• Borrower attributes
• Loan amount
• Employment information
• Income information
• Demographic data where applicable

The quality and completeness of borrower data significantly impact scoring accuracy.

Request payload sample

{
  "productId": "324123",
  "attributes": {
    "age": 37,
    "employment_status": "employed",
    "monthly_net_income": "1000000+",
    "location": "City A",
    "loan_amount": "5000"
  }
}

Step-by-step guide to scoring a borrower using Oraculi

Step 1: Obtain the Decision Model ID for the scoring model and use it as a path parameter in the Oraculi Borrower Scoring endpoint. The Decision Model ID will be used as a path parameter when making requests to the Oraculi Borrower Scoring endpoint.

Before proceeding, verify that:

• The model configuration has been published if required
• The decision model is active
• The scoring module is enabled

Obtaining the decision model id from decision model settings.

Step 2: Create an app on the Adjutor web application, ticking all the validation scopes. During app creation:

• Enable all required validation scopes
• Configure the necessary permissions
• Save the application settings

After creating the app successfully, retrieve your API credentials. These credentials are required for authenticating requests to the scoring endpoint.

Step 3: Make a POST request with the user’s details in the request body. Refer to the Adjutor API references for The Oraculi Browser Scoring endpoint.​

Read further: Adjutor your critical support for making better credit decisions

Real-world use cases for borrower scoring

Borrower scoring can support several lending operations across financial institutions and fintech platforms.

Automated loan approvals

Digital lenders can automatically approve low-risk borrowers based on scoring thresholds.

Risk-based lending

Lenders can assign different loan limits, interest rates, or repayment terms based on borrower risk profiles.

Fraud prevention

Scoring systems can identify suspicious or inconsistent borrower information during application reviews.

Loan portfolio management

Lenders can use scoring insights to improve overall loan portfolio quality and reduce exposure to risky borrowers.

SME lending evaluation

Business lenders can incorporate borrower scoring into small business financing workflows.

Best practices for borrower scoring integration

Following best practices helps improve scoring reliability and operational efficiency.

Validate borrower data before submission

Ensure all borrower information is complete and properly formatted before making scoring requests.

Use sufficient borrower attributes

More complete borrower data generally improves scoring accuracy.

Protect API credentials

Store API keys securely and rotate credentials regularly when necessary.

Monitor scoring failures

Track failed scoring requests and investigate recurring issues.

Maintain updated decision models

Regularly review and improve your decision models to align with changing lending policies and risk conditions.

Common issues and troubleshooting

Invalid decision model ID

Ensure the correct Decision Model ID is being used and that the model is active.

Scoring request fails

Check that:

• The request payload is valid
• Required fields are included
• Authentication headers are correct
• API credentials are active

Missing borrower attributes

Incomplete borrower data may lead to inaccurate or failed scoring evaluations.

Unauthorized request errors

Verify that your API key is valid and that the appropriate scopes were enabled during app creation.

Frequently asked questions

Can I customize borrower scoring rules?

Yes. Borrower scoring behavior depends on your configured decision model and lender-defined evaluation rules.

Is borrower scoring mandatory for all loan products?

No. Scoring requirements can vary depending on your loan product configuration.

Can scoring happen in real time?

Yes. The Oraculi Borrower Scoring endpoint supports real-time scoring workflows.

What happens if scoring fails?

This depends on your continue_on_failure configuration setting within the scoring module.

Can I use different scoring models for different products?

Yes. Different loan products can reference different decision models and scoring configurations.