The bank was experiencing high levels of non-performing assets (NPAs) due to many loan defaulters. A machine learning model was developed to predict loan applicants' default status with 90% accuracy. The model was trained on data of over 500,000 customers with 45 variables, including payment history, credit utilization, collections, and application details. When applied to new applicants, the model correctly predicted 68% of defaulters. Important variables for identifying defaulters included last payment amount, credit limits, interest paid, balances, collections, loan grade, and applicant's state of residence.

1. atom D sciences
Building Machine learning applications for Enterprises
Industry : Finance/Micro-Finance/Loans
Case study : Predicting which applicant will be a Loan defaulter

2. Problem Statement
The Bank Indessa has not done well in last 3 quarters. Their NPAs (Non Performing Assets) have reached all
time high. After careful analysis, it was found that the majority of NPA was contributed by loan defaulters.
We developed a machine learning Model that predicted the applicants default status with almost 90%
accuracy.

3. Data set
Number of Rows(Customers) : 532428
Number of Columns (variables) : 45
TARGET : 0 means Default ;1 means- No default

4. Train –Test Division (85% -15%)

5. Sl.no variable class levels missing value percent zeroes percent
1 acc_now_delinq integer 0 0.003052 99.533740
2 addr_state factor 51 0 0.000000
3 annual_inc numeric 425943 0.000704 0.000000
4 application_type factor 2 0 0.000000
5 batch_enrolled factor 104 19.920506 0.000000
6 collection_recovery_fee numeric 425943 0 97.360914
7 collections_12_mths_ex_med integer 0 0.018547 98.664375
8 delinq_2yrs integer 0 0.003052 80.722773
9 desc factor 70639 0 0.000000
10 dti numeric 425943 0 0.047189
11 emp_length factor 11 5.045276 0.000000
12 emp_title factor 190125 0.00047 0.000000
13 funded_amnt integer 0 0 0.000000
14 funded_amnt_inv numeric 425943 0 0.026999
15 grade factor 7 0 0.000000
16 home_ownership factor 6 0 0.000000
17 initial_list_status factor 2 0 0.000000
18 inq_last_6mths integer 0 0.003052 56.156340
19 int_rate numeric 425943 0 0.000000
20 last_week_pay factor 98 0 0.000000
21 loan_amnt integer 0 0 0.000000
22 mths_since_last_delinq integer 0 51.138533 0.189697
23 mths_since_last_major_derog integer 0 75.024827 0.016904
24 mths_since_last_record integer 0 84.580566 0.137108
25 open_acc integer 0 0.003052 0.000939
26 pub_rec integer 0 0.003052 84.713917
27 purpose factor 14 0 0.000000
28 pymnt_plan factor 2 0 0.000000
29 recoveries numeric 425943 0 97.230615
30 revol_bal numeric 425943 0 0.373524
31 revol_util numeric 425943 0.054467 0.394419
32 sub_grade factor 35 0 0.000000
33 target integer 0 0 76.343783
34 term factor 2 0 0.000000
35 title factor 39694 0 0.000000
36 tot_coll_amt integer 0 7.898944 79.062926
37 tot_cur_bal numeric 425943 7.898944 0.014086

6. Applied Machine learning algorithms and
Build Predictive Model on 4.32 lakh
customers

8. Predicting 68% defaulters correctly
90% overall accuracy

9. Which variables are important to find
the defaulters?

10. Dictionary
Last_week_pay,total_revolving credit limit,
Interest received till date,
total current balace of all accounts,
Total collection amount ever owed,
Sub_grade,Addr_state

11. Contact :
V Raviteja Valluri,
Founder & Data Scientist,
Atom D Sciences & Analytics Pvt Ltd,
raviteja@atomdsciences.com,
+91-8501903007
www.atomdsciences.com