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.