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.
Copy 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
}'
Copy {
"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.