Adjútor from Lendsqr
API ReferenceLendsqr
  • Introduction
    • Overview
    • Getting Started
    • Test and Live mode
    • Making your first API call
    • Webhooks
  • Authentication
    • Authentication Type
    • Generating your API key
  • USE CASES
    • Introduction
    • Loan repayment with Direct Debit
    • Corporate Cash and Treasury Management
    • Buy Now Pay Later (BNPL)
    • Embedded Credit/ Finance
    • Loan scoring/ Credit scoring
    • Customer Validation
    • Implementing GSI with Direct Debit
  • Adjutor API Endpoints
    • Oraculi Mobile SDK (Beta)
      • Installing the SDK
      • The Oraculi SDK data journey
    • Validation
      • Bank Verification Number
      • Bank Account Verification
      • Karma Lookup
      • Ecosystem Lookup
    • Decisioning
      • Decision Model Lookup
      • Oraculi scoring
    • Credit Bureaus
    • Direct Debit
      • How Direct Debit works
      • The Direct Debit process
      • Understanding Mandate Statuses
    • Embedded Loans and Payments
    • Platform Data
      • Data for Lenders
      • Operational Services
    • Transactions and Balances with Kolo
      • Initializing Authorization
      • Using your access token
      • Permission Scopes
    • Core Services
  • ADDITIONAL INFORMATION
    • FAQs
    • Getting Support
    • Pricing
    • Glossary
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  1. Adjutor API Endpoints
  2. Decisioning

Oraculi scoring

Summary

Scoring has never been simpler. With this service, you can easily score your lenders based on the settings you configured in your decision models.

Introduction

This endpoint is used for scoring based on the passed parameters/data points. By default, Lendsqr provides you with a proprietary scoring model and its sample request payload which you can tweak to your specification

However, you can pass any data point you wish to; provided that you have configured the scoring model to accept this. Below is a sample request and response body:

It is highly important to you set the specific decision model you wish to use for scoring.

Name
Type
Description

id

Integer

The decision model ID you wish to use. The Get Models API returns this value

curl --location 'https://adjutor.lendsqr.com/v2/decisioning/models/2355' \
--data-raw '{
    "gender": "Female",
    "marital_status": "Single",
    "age": "21",
    "location": "lagos",
    "no_of_dependent": "0",
    "type_of_residence": "Rented Apartment",
    "educational_attainment": "BSc, HND and Other Equivalent",
    "employment_status": "Employed",
    "sector_of_employment": "Other Financial",
    "monthly_net_income": "100,000 - 199,999",
    "employer_category": "Private Company",
    "bvn": "22536051111",
    "phone_number": "08012345678",
    "total_years_of_experience": 5,
    "time_with_current_employer": 2,
    "previous_lendsqr_loans": 3,
    "phone": "07062561111",
    "bvn_phone": "07062561111",
    "office_email": "adojohnsule@lendsqr.com",
    "personal_email": "adojohnsule@lendsqr.com",
    "amount": 10000
}'
{
   "status":"success",
   "message":"Successful",
   "data":{
      "credit_score_items":[
         {
            "score_name":"age",
            "score_value":"21 - 30",
            "weight":"7",
            "maximum_score":10,
            "borrower_score":0,
            "weighted_score":0
         },
         {
            "score_name":"gender",
            "score_value":"Female",
            "weight":"10",
            "maximum_score":10,
            "borrower_score":10,
            "weighted_score":0.0909
         },
         {
            "score_name":"location",
            "score_value":"lagos",
            "weight":"5",
            "maximum_score":10,
            "borrower_score":9,
            "weighted_score":0.0409
         },
         {
            "score_name":"customer_tier",
            "weight":"5",
            "maximum_score":10,
            "borrower_score":0,
            "weighted_score":0
         },
         {
            "score_name":"marital_status",
            "score_value":"Single",
            "weight":"5",
            "maximum_score":10,
            "borrower_score":6,
            "weighted_score":0.0273
         },
         {
            "score_name":"employer_category",
            "weight":"0",
            "maximum_score":10,
            "borrower_score":0,
            "weighted_score":0
         },
         {
            "score_name":"employment_status",
            "score_value":"Employed",
            "weight":"10",
            "maximum_score":10,
            "borrower_score":10,
            "weighted_score":0.0909
         },
         {
            "score_name":"type_of_residence",
            "score_value":"Rented Apartment",
            "weight":"5",
            "maximum_score":10,
            "borrower_score":10,
            "weighted_score":0.0455
         },
         {
            "score_name":"monthly_net_income",
            "score_value":"100,000 - 199,999",
            "weight":"10",
            "maximum_score":10,
            "borrower_score":6,
            "weighted_score":0.0545
         },
         {
            "score_name":"no_of_dependent",
            "score_value":"0",
            "weight":"8",
            "maximum_score":10,
            "borrower_score":0,
            "weighted_score":0
         },
         {
            "score_name":"sector_of_employment",
            "score_value":"Other Financial",
            "weight":"5",
            "maximum_score":10,
            "borrower_score":6,
            "weighted_score":0.0273
         },
         {
            "score_name":"educational_attainment",
            "weight":"5",
            "maximum_score":10,
            "borrower_score":0,
            "weighted_score":0
         },
         {
            "score_name":"total_years_of_experience",
            "weight":"5",
            "maximum_score":10,
            "borrower_score":4,
            "weighted_score":0.0182
         },
         {
            "score_name":"time_with_current_employer",
            "score_value":1,
            "weight":"5",
            "maximum_score":10,
            "borrower_score":2,
            "weighted_score":0.0091
         },
         {
            "score_name":"previous_paid_loans_on_pecunia",
            "weight":"25",
            "maximum_score":10,
            "borrower_score":0,
            "weighted_score":0
         }
      ],
      "total_weight":110,
      "score":40.46
   },
   "meta":{
      "balance":50000
   }
}

This provides you with an intuitive way of customer scoring.

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Last updated 9 months ago

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