# Oraculi scoring

{% hint style="success" %}
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. 
{% endhint %}

## 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 |

```bash
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
}'
```

```json
{
   "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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