Lending & Credit Risk

Lending events represent requests to issue credit or increase exposure to existing users. These decisions carry financial risk because default, synthetic identity abuse, and loan stacking can occur if behavior is not thoroughly evaluated. ProfiledRisk provides real-time lending risk intelligence by combining:

Your custom lending rules tailored to product risk appetite
ProfiledRisk intelligence assessing historical activity, device consistency, and identity stability

Each lending event returns a decision via the status field:

allowed — continue loan approval workflow
blocked — reject request
pending — hold for additional checks or analyst decision

This ensures loans are issued only when risk signals align with expected borrower behavior.

When to Use This Use Case

Use the Lending category when:

A user submits a loan application
Borrower profile or credit eligibility is updated
A new loan request is made for an existing borrower
Exposure changes require re-evaluation (credit limit increase, top-up)

Common triggers include:

Salary advance applications
Personal or SME loan requests
Buy-Now-Pay-Later (BNPL) onboarding and repayment approvals
Rapid repeat borrowing behavior

If the event increases business exposure, ProfiledRisk should evaluate it as a lending risk event.

Expected Event Inputs

A Lending event should clearly describe the loan request, the borrower profile, and the context in which the request occurs.

Required signal groups:


Category	Example Fields	Risk Purpose
Loan request	amount, purpose, loan_term, interest_rate	Detect high-risk product misuse and affordability issues
User profile	employment status, income, tier, KYC	Validate credibility and repayment capacity
Behavioral context	registration_time, recent credential changes	Prevent fraudulent credit access and ATO abuses
Device & network	device_id, OS, IP	Identify identity sharing, collusion, and takeover risk
Address & location	declared addresses and geography	Support jurisdiction checks and fraud clustering detection

Full schema is documented in here Example snippet from your payload:

"loan": {
  "id": "87554303-3f75-4883-8fb8-48fce003859f",
  "time": "2022-12-12T12:15:05.391Z",
  "amount": 10000,
  "channel": "mobile",
  "purpose": "Salary Advance",
  "interest_rate": 18.5,
  "loan_product": "personal",
  "loan_term": 24

}

Full Schema definitions are located under API Integration → Lending Events. ProfiledRisk uses onboarding and payment history to contextualize the credit request.

Decisioning Logic

Evaluation combines:

1. Your Rules

Configured in Flows, such as:

Employment and income validation requirements
Minimum credit score thresholds
Rules preventing excessive exposure per borrower
Enhanced verification for first-time lending customers

2. ProfiledRisk Intelligence

Including:

Loan stacking detection across shared devices or profiles
Spike in risk signals preceding requests
Weak identity correlated with high credit appetite
Cross-entity behavioral anomalies (e.g., same device, different users)

Response Example

{
  "status": "pending",
  "risk_score": 78,
  "risk_level": "high",
  "case_created": true
}

Client Enforcement Behavior


Status	Action Required in Lending Workflow
allowed	Continue credit assessment or disbursement
blocked	Reject loan request
pending	Require analyst or step-up verification before decision

Pending is common when risk signals conflict but manual intervention may justify approval.

Case Management

A Case is typically generated when:

Borrower profile changes shortly before request (ATO suspicion)
Loan request is high-value relative to user history
Device/IP suggests collusion or synthetic identity clusters
Required borrower documentation triggers compliance review

Analysts may escalate or finalize decision inline with lending policies.

Example Lending Rules


Objective	Rule Concept	Output Status
Prevent loan stacking	If 2+ applications within 24 hours from same device but different profiles	blocked
Flag high-risk applications	If first-time borrower + amount > profile affordability threshold	pending
Compliance review	If income/employment fields missing or inconsistent	pending
Detect synthetic identity	If identity score below threshold across combined KYC checks	blocked

Rules allow business teams to control exposure while enabling automation.

Summary

ProfiledRisk supports credit operations by:

Preventing high-risk accounts from receiving funds
Reducing default exposure through behavior-based evaluation
Ensuring compliance alignment with lending obligations
Improving analyst efficiency through automated segmentation

This use case should be applied whenever borrower exposure or credit risk changes.

GETTING STARTED

USE-CASES

REPORTS

When to Use This Use Case

Expected Event Inputs

Required signal groups:

Decisioning Logic

1. Your Rules

2. ProfiledRisk Intelligence

Response Example

Client Enforcement Behavior

Case Management

Example Lending Rules

Summary

​When to Use This Use Case

​Expected Event Inputs

​Required signal groups:

​Decisioning Logic

​1. Your Rules

​2. ProfiledRisk Intelligence

​Response Example

​Client Enforcement Behavior

​Case Management

​Example Lending Rules

​Summary

When to Use This Use Case

Expected Event Inputs

Required signal groups:

Decisioning Logic

1. Your Rules

2. ProfiledRisk Intelligence

Response Example

Client Enforcement Behavior

Case Management

Example Lending Rules

Summary