1 00:00:00,837 --> 00:00:02,838 How much do you need to know about a person 2 00:00:02,862 --> 00:00:05,396 before you'd feel comfortable making a loan? 3 00:00:06,044 --> 00:00:08,499 Suppose you wanted to lend 1,000 dollars 4 00:00:08,523 --> 00:00:10,837 to the person sitting two rows behind you. 5 00:00:11,190 --> 00:00:13,322 What would you need to know about that person 6 00:00:13,346 --> 00:00:15,267 before you'd feel comfortable? 7 00:00:15,291 --> 00:00:19,215 My mom came to the US from India in her late thirties. 8 00:00:19,614 --> 00:00:21,046 She's a doctor in Brooklyn, 9 00:00:21,070 --> 00:00:25,293 and she often lets friends and neighbors come to see her for health services, 10 00:00:25,317 --> 00:00:27,546 whether they can pay right away or not. 11 00:00:27,955 --> 00:00:31,162 I remember running into her patients with her at the grocery store 12 00:00:31,186 --> 00:00:32,337 or on the sidewalk, 13 00:00:32,361 --> 00:00:35,201 and sometimes they would come and pay her right on the spot 14 00:00:35,225 --> 00:00:36,583 for previous appointments. 15 00:00:36,607 --> 00:00:37,846 She would thank them, 16 00:00:37,870 --> 00:00:40,591 and ask them about their families and their health. 17 00:00:41,466 --> 00:00:44,718 She gave them credit because she trusted them. 18 00:00:45,122 --> 00:00:47,489 Most of us are like my mom. 19 00:00:47,899 --> 00:00:50,762 We would give credit to someone we know 20 00:00:50,786 --> 00:00:52,182 or that we live next to. 21 00:00:52,206 --> 00:00:55,910 But most of us are probably not going to lend to a stranger 22 00:00:55,934 --> 00:00:59,000 unless we know a little something about them. 23 00:00:59,634 --> 00:01:03,337 Banks, credit card companies and other financial institutions 24 00:01:03,361 --> 00:01:05,621 don't know us on a personal level, 25 00:01:05,645 --> 00:01:08,524 but they do have a way of trusting us, 26 00:01:08,548 --> 00:01:11,009 and that's through our credit scores. 27 00:01:11,033 --> 00:01:13,028 Our credit scores have been created 28 00:01:13,052 --> 00:01:17,572 through an aggregation and analysis of our public consumer credit data. 29 00:01:17,596 --> 00:01:20,837 And because of them, we have pretty much easy access 30 00:01:20,861 --> 00:01:23,305 to all of the goods and services that we need, 31 00:01:23,329 --> 00:01:26,173 from getting electricity to buying a home, 32 00:01:26,197 --> 00:01:28,654 or taking a risk and starting a business. 33 00:01:29,561 --> 00:01:30,722 But ... 34 00:01:30,746 --> 00:01:35,113 there are 2.5 billion people around the world 35 00:01:35,137 --> 00:01:37,223 that don't have a credit score. 36 00:01:37,247 --> 00:01:40,271 That's a third of the world's population. 37 00:01:40,930 --> 00:01:42,185 They don't have a score 38 00:01:42,209 --> 00:01:45,470 because there are no formal public records on them -- 39 00:01:45,494 --> 00:01:46,858 no bank accounts, 40 00:01:46,882 --> 00:01:48,376 no credit histories 41 00:01:48,400 --> 00:01:50,458 and no social security numbers. 42 00:01:50,482 --> 00:01:52,201 And because they don't have a score, 43 00:01:52,225 --> 00:01:56,876 they don't have access to the credit or financial products 44 00:01:56,900 --> 00:01:58,746 that can improve their lives. 45 00:01:59,509 --> 00:02:01,571 They are not trusted. 46 00:02:02,968 --> 00:02:06,138 So we wanted to find a way to build trust 47 00:02:06,162 --> 00:02:10,213 and to open up financial access for these 2.5 billion. 48 00:02:10,772 --> 00:02:13,238 So we created a mobile application 49 00:02:13,262 --> 00:02:17,552 that builds credit scores for them using mobile data. 50 00:02:18,345 --> 00:02:22,801 There are currently over one billion smartphones in emerging markets. 51 00:02:22,825 --> 00:02:25,813 And people are using them the same way that we do. 52 00:02:26,235 --> 00:02:29,204 They're texting their friends, they're looking up directions, 53 00:02:29,228 --> 00:02:30,640 they're browsing the Internet 54 00:02:30,664 --> 00:02:33,143 and they're even making financial transactions. 55 00:02:33,167 --> 00:02:36,353 Over time, this data is getting captured on our phones, 56 00:02:36,377 --> 00:02:40,172 and it provides a really rich picture of a person's life. 57 00:02:41,132 --> 00:02:43,308 Our customers give us access to this data 58 00:02:43,332 --> 00:02:46,442 and we capture it through our mobile application. 59 00:02:46,466 --> 00:02:49,110 It helps us understand the creditworthiness 60 00:02:49,134 --> 00:02:53,835 of people like Jenipher, a small-business owner in Nairobi, Kenya. 61 00:02:54,290 --> 00:02:59,173 Jenipher is 65 years old, and for decades has been running a food stall 62 00:02:59,197 --> 00:03:01,255 in the central business district. 63 00:03:01,897 --> 00:03:05,096 She has three sons who she put through vocational school, 64 00:03:05,120 --> 00:03:07,644 and she's also the leader of her local chama, 65 00:03:07,668 --> 00:03:09,131 or savings group. 66 00:03:09,609 --> 00:03:11,532 Jenipher's food stall does well. 67 00:03:11,556 --> 00:03:15,000 She makes just enough every day to cover her expenses. 68 00:03:15,024 --> 00:03:17,392 But she's not financially secure. 69 00:03:17,842 --> 00:03:20,582 Any emergency could force her into debt. 70 00:03:20,887 --> 00:03:22,734 And she has no discretionary income 71 00:03:22,758 --> 00:03:24,996 to improve her family's way of living, 72 00:03:25,020 --> 00:03:26,259 for emergencies, 73 00:03:26,283 --> 00:03:28,956 or for investing into growing her business. 74 00:03:30,065 --> 00:03:33,291 If Jenipher wants credit, her options are limited. 75 00:03:33,733 --> 00:03:35,111 She could get a microloan, 76 00:03:35,135 --> 00:03:38,979 but she'd have to form a group that could help vouch for her credibility. 77 00:03:39,003 --> 00:03:41,975 And even then, the loan sizes would be way too small 78 00:03:41,999 --> 00:03:44,256 to really have an impact on her business, 79 00:03:44,280 --> 00:03:46,568 averaging around 150 dollars. 80 00:03:47,525 --> 00:03:50,043 Loan sharks are always an option, 81 00:03:50,067 --> 00:03:54,017 but with interest rates that are well above 300 percent, 82 00:03:54,041 --> 00:03:55,595 they're financially risky. 83 00:03:56,079 --> 00:04:00,064 And because Jenipher doesn't have collateral or a credit history, 84 00:04:00,088 --> 00:04:03,875 she can't walk into a bank and ask for a business loan. 85 00:04:04,331 --> 00:04:05,654 But one day, 86 00:04:05,678 --> 00:04:09,656 Jenipher's son convinced her to download our application 87 00:04:09,680 --> 00:04:10,931 and apply for a loan. 88 00:04:11,407 --> 00:04:13,946 Jenipher answered a few questions on her phone 89 00:04:13,970 --> 00:04:17,964 and she gave us access to a few key data points on her device. 90 00:04:18,561 --> 00:04:19,899 And here's what we saw. 91 00:04:20,392 --> 00:04:21,891 So, bad news first. 92 00:04:22,996 --> 00:04:27,762 Jenipher had a low savings balance and no previous loan history. 93 00:04:28,395 --> 00:04:29,546 These are factors 94 00:04:29,570 --> 00:04:32,596 that would have thrown up a red flag to a traditional bank. 95 00:04:32,620 --> 00:04:35,351 But there were other points in her history that showed us 96 00:04:35,375 --> 00:04:38,706 a much richer picture of her potential. 97 00:04:39,342 --> 00:04:40,493 So for one, 98 00:04:40,517 --> 00:04:44,372 we saw that she made regular phone calls to her family in Uganda. 99 00:04:45,629 --> 00:04:48,086 Well, it turns out that the data shows 100 00:04:48,110 --> 00:04:50,844 a four percent increase in repayment 101 00:04:50,868 --> 00:04:55,604 among people who consistently communicate with a few close contacts. 102 00:04:56,657 --> 00:04:57,816 We could also see 103 00:04:57,840 --> 00:05:00,648 that though she traveled around a lot throughout the day, 104 00:05:00,672 --> 00:05:03,403 she actually had pretty regular travel patterns, 105 00:05:03,427 --> 00:05:06,562 and she was either at home or at her food stall. 106 00:05:07,315 --> 00:05:11,302 And the data shows a six percent increase in repayment 107 00:05:11,326 --> 00:05:13,713 among customers who are consistent 108 00:05:13,737 --> 00:05:16,346 with where they spend most of their time. 109 00:05:17,349 --> 00:05:19,930 We could also see that she communicated a lot 110 00:05:19,954 --> 00:05:22,302 with many different people throughout the day 111 00:05:22,326 --> 00:05:24,668 and that she had a strong support network. 112 00:05:25,287 --> 00:05:26,680 Our data shows 113 00:05:26,704 --> 00:05:31,384 that people who communicate with more than 58 different contacts 114 00:05:31,408 --> 00:05:34,309 tend to be more likely to be good borrowers. 115 00:05:34,333 --> 00:05:35,602 In Jenipher's case, 116 00:05:35,626 --> 00:05:39,669 she communicated with 89 different individuals, 117 00:05:39,693 --> 00:05:42,944 which showed a nine percent increase in her repayment. 118 00:05:43,888 --> 00:05:47,655 These are just some of the thousands of different data points 119 00:05:47,679 --> 00:05:50,912 that we look at to understand a person's creditworthiness. 120 00:05:51,397 --> 00:05:54,470 And after analyzing all of these different data points, 121 00:05:54,494 --> 00:05:56,466 we took the first risk 122 00:05:56,490 --> 00:05:58,533 and gave Jenipher a loan. 123 00:05:59,370 --> 00:06:02,696 This is data that would not be found on a paper trail 124 00:06:02,720 --> 00:06:05,288 or in any formal financial record. 125 00:06:05,720 --> 00:06:07,508 But it proves trust. 126 00:06:08,193 --> 00:06:10,170 By looking beyond income, 127 00:06:10,194 --> 00:06:12,351 we can see that people in emerging markets 128 00:06:12,375 --> 00:06:16,044 that may seem risky and unpredictable on the surface 129 00:06:16,068 --> 00:06:20,294 are actually willing and have the capacity to repay. 130 00:06:21,334 --> 00:06:26,519 Our credit scores have helped us deliver over 200,000 loans in Kenya 131 00:06:26,543 --> 00:06:28,140 in just the past year. 132 00:06:28,164 --> 00:06:31,466 And our repayment rates are above 90 percent -- 133 00:06:31,490 --> 00:06:36,785 which, by the way, is in line with traditional bank repayment rates. 134 00:06:37,564 --> 00:06:40,473 With something as simple as a credit score, 135 00:06:40,497 --> 00:06:44,012 we're giving people the power to build their own futures. 136 00:06:44,647 --> 00:06:47,954 Our customers have used their loans for family expenses, 137 00:06:47,978 --> 00:06:49,835 emergencies, travel 138 00:06:49,859 --> 00:06:52,831 and for investing back into growing their businesses. 139 00:06:53,792 --> 00:06:57,264 They're now building better economies and communities 140 00:06:57,288 --> 00:06:59,612 where more people can succeed. 141 00:07:00,664 --> 00:07:03,392 Over the past two years of using our product, 142 00:07:03,416 --> 00:07:07,600 Jenipher has increased her savings by 60 percent. 143 00:07:07,624 --> 00:07:10,276 She's also started two additional food stalls 144 00:07:10,300 --> 00:07:13,377 and is now making plans for her own restaurant. 145 00:07:13,401 --> 00:07:16,805 She's applying for a small-business loan from a commercial bank, 146 00:07:16,829 --> 00:07:21,554 because she now has the credit history to prove she deserves it. 147 00:07:22,038 --> 00:07:24,953 I saw Jenipher in Nairobi just last week, 148 00:07:24,977 --> 00:07:28,228 and she told me how excited she was to get started. 149 00:07:28,252 --> 00:07:29,540 She said, 150 00:07:30,178 --> 00:07:35,657 "Only my son believed I could do this. I didn't think this was for me." 151 00:07:36,250 --> 00:07:38,536 She's lived her whole life 152 00:07:38,560 --> 00:07:42,853 believing that there was a part of the world that was closed off to her. 153 00:07:43,536 --> 00:07:47,892 Our job now is to open the world to Jenipher 154 00:07:47,916 --> 00:07:52,111 and the billions like her that deserve to be trusted. 155 00:07:52,135 --> 00:07:53,294 Thank you. 156 00:07:53,318 --> 00:07:57,650 (Applause)