1 00:00:05,020 --> 00:00:11,500 Today, we're going to do a case study on you signing off loan based on financial history of a user. 2 00:00:15,510 --> 00:00:17,150 So what is any? 3 00:00:18,840 --> 00:00:22,110 You signing is signing the documents digitally. 4 00:00:25,500 --> 00:00:30,220 The U.S. is going to complete transactions, loops and agreements electronically. 5 00:00:31,020 --> 00:00:40,020 So in India, the U.S. has been granted legal status amendments to various laws like Information Technology 6 00:00:40,020 --> 00:00:43,230 Act, Indian Evidence Act and many more. 7 00:00:44,500 --> 00:00:51,460 Financial sector has adopted its any good customers sign for loan and credit card applications. 8 00:00:53,340 --> 00:00:59,820 So in this project, we are going to predict the likelihood of you signing a loan based on financial 9 00:00:59,820 --> 00:01:07,170 history of a user, if user really wants the loan, he should proceed through selling technique, back 10 00:01:07,180 --> 00:01:08,850 checks to users, financial history. 11 00:01:09,060 --> 00:01:12,440 If it is gold, then only the user will get the loan. 12 00:01:13,540 --> 00:01:19,900 Many lending companies provide hundreds of loans every day, gaining money for each one does the project 13 00:01:19,900 --> 00:01:23,920 can help them in knowing whether it is risky to give a loan or not. 14 00:01:26,860 --> 00:01:27,360 Blitzstein.