WEBVTT 00:00:00.837 --> 00:00:02.838 How much do you need to know about a person 00:00:02.862 --> 00:00:05.396 before you'd feel comfortable making a loan? NOTE Paragraph 00:00:06.044 --> 00:00:08.499 Suppose you wanted to lend 1,000 dollars 00:00:08.523 --> 00:00:10.837 to the person sitting two rows behind you. 00:00:11.190 --> 00:00:13.322 What would you need to know about that person 00:00:13.346 --> 00:00:15.267 before you'd feel comfortable? NOTE Paragraph 00:00:15.291 --> 00:00:19.215 My mom came to the US from India in her late thirties. 00:00:19.614 --> 00:00:21.046 She's a doctor in Brooklyn, 00:00:21.070 --> 00:00:25.293 and she often lets friends and neighbors come to see her for health services, 00:00:25.317 --> 00:00:27.546 whether they can pay right away or not. 00:00:27.955 --> 00:00:31.162 I remember running into her patients with her at the grocery store 00:00:31.186 --> 00:00:32.337 or on the sidewalk, 00:00:32.361 --> 00:00:35.201 and sometimes they would come and pay her right on the spot 00:00:35.225 --> 00:00:36.583 for previous appointments. 00:00:36.607 --> 00:00:37.846 She would thank them, 00:00:37.870 --> 00:00:40.591 and ask them about their families and their health. 00:00:41.466 --> 00:00:44.718 She gave them credit because she trusted them. NOTE Paragraph 00:00:45.122 --> 00:00:47.489 Most of us are like my mom. 00:00:47.899 --> 00:00:50.762 We would give credit to someone we know 00:00:50.786 --> 00:00:52.182 or that we live next to. 00:00:52.206 --> 00:00:55.910 But most of us are probably not going to lend to a stranger 00:00:55.934 --> 00:00:59.000 unless we know a little something about them. NOTE Paragraph 00:00:59.634 --> 00:01:03.337 Banks, credit card companies and other financial institutions 00:01:03.361 --> 00:01:05.621 don't know us on a personal level, 00:01:05.645 --> 00:01:08.524 but they do have a way of trusting us, 00:01:08.548 --> 00:01:11.009 and that's through our credit scores. 00:01:11.033 --> 00:01:13.028 Our credit scores have been created 00:01:13.052 --> 00:01:17.572 through an aggregation and analysis of our public consumer credit data. 00:01:17.596 --> 00:01:20.837 And because of them, we have pretty much easy access 00:01:20.861 --> 00:01:23.305 to all of the goods and services that we need, 00:01:23.329 --> 00:01:26.173 from getting electricity to buying a home, 00:01:26.197 --> 00:01:28.654 or taking a risk and starting a business. NOTE Paragraph 00:01:29.561 --> 00:01:30.722 But ... 00:01:30.746 --> 00:01:35.113 there are 2.5 billion people around the world 00:01:35.137 --> 00:01:37.223 that don't have a credit score. 00:01:37.247 --> 00:01:40.271 That's a third of the world's population. 00:01:40.930 --> 00:01:42.185 They don't have a score 00:01:42.209 --> 00:01:45.470 because there are no formal public records on them -- 00:01:45.494 --> 00:01:46.858 no bank accounts, 00:01:46.882 --> 00:01:48.376 no credit histories 00:01:48.400 --> 00:01:50.458 and no social security numbers. 00:01:50.482 --> 00:01:52.201 And because they don't have a score, 00:01:52.225 --> 00:01:56.876 they don't have access to the credit or financial products 00:01:56.900 --> 00:01:58.746 that can improve their lives. 00:01:59.509 --> 00:02:01.571 They are not trusted. NOTE Paragraph 00:02:02.968 --> 00:02:06.138 So we wanted to find a way to build trust 00:02:06.162 --> 00:02:10.213 and to open up financial access for these 2.5 billion. 00:02:10.772 --> 00:02:13.238 So we created a mobile application 00:02:13.262 --> 00:02:17.552 that builds credit scores for them using mobile data. 00:02:18.345 --> 00:02:22.801 There are currently over one billion smartphones in emerging markets. 00:02:22.825 --> 00:02:25.813 And people are using them the same way that we do. 00:02:26.235 --> 00:02:29.204 They're texting their friends, they're looking up directions, 00:02:29.228 --> 00:02:30.640 they're browsing the Internet 00:02:30.664 --> 00:02:33.143 and they're even making financial transactions. NOTE Paragraph 00:02:33.167 --> 00:02:36.353 Over time, this data is getting captured on our phones, 00:02:36.377 --> 00:02:40.172 and it provides a really rich picture of a person's life. 00:02:41.132 --> 00:02:43.308 Our customers give us access to this data 00:02:43.332 --> 00:02:46.442 and we capture it through our mobile application. 00:02:46.466 --> 00:02:49.110 It helps us understand the creditworthiness 00:02:49.134 --> 00:02:53.835 of people like Jenipher, a small-business owner in Nairobi, Kenya. 00:02:54.290 --> 00:02:59.173 Jenipher is 65 years old, and for decades has been running a food stall 00:02:59.197 --> 00:03:01.255 in the central business district. 00:03:01.897 --> 00:03:05.096 She has three sons who she put through vocational school, 00:03:05.120 --> 00:03:07.644 and she's also the leader of her local chama, 00:03:07.668 --> 00:03:09.131 or savings group. NOTE Paragraph 00:03:09.609 --> 00:03:11.532 Jenipher's food stall does well. 00:03:11.556 --> 00:03:15.000 She makes just enough every day to cover her expenses. 00:03:15.024 --> 00:03:17.392 But she's not financially secure. 00:03:17.842 --> 00:03:20.582 Any emergency could force her into debt. 00:03:20.887 --> 00:03:22.734 And she has no discretionary income 00:03:22.758 --> 00:03:24.996 to improve her family's way of living, 00:03:25.020 --> 00:03:26.259 for emergencies, 00:03:26.283 --> 00:03:28.956 or for investing into growing her business. 00:03:30.065 --> 00:03:33.291 If Jenipher wants credit, her options are limited. 00:03:33.733 --> 00:03:35.111 She could get a microloan, 00:03:35.135 --> 00:03:38.979 but she'd have to form a group that could help vouch for her credibility. 00:03:39.003 --> 00:03:41.975 And even then, the loan sizes would be way too small 00:03:41.999 --> 00:03:44.256 to really have an impact on her business, 00:03:44.280 --> 00:03:46.568 averaging around 150 dollars. 00:03:47.525 --> 00:03:50.043 Loan sharks are always an option, 00:03:50.067 --> 00:03:54.017 but with interest rates that are well above 300 percent, 00:03:54.041 --> 00:03:55.595 they're financially risky. 00:03:56.079 --> 00:04:00.064 And because Jenipher doesn't have collateral or a credit history, 00:04:00.088 --> 00:04:03.875 she can't walk into a bank and ask for a business loan. NOTE Paragraph 00:04:04.331 --> 00:04:05.654 But one day, 00:04:05.678 --> 00:04:09.656 Jenipher's son convinced her to download our application 00:04:09.680 --> 00:04:10.931 and apply for a loan. 00:04:11.407 --> 00:04:13.946 Jenipher answered a few questions on her phone 00:04:13.970 --> 00:04:17.964 and she gave us access to a few key data points on her device. NOTE Paragraph 00:04:18.561 --> 00:04:19.899 And here's what we saw. 00:04:20.392 --> 00:04:21.891 So, bad news first. 00:04:22.996 --> 00:04:27.762 Jenipher had a low savings balance and no previous loan history. 00:04:28.395 --> 00:04:29.546 These are factors 00:04:29.570 --> 00:04:32.596 that would have thrown up a red flag to a traditional bank. 00:04:32.620 --> 00:04:35.351 But there were other points in her history that showed us 00:04:35.375 --> 00:04:38.706 a much richer picture of her potential. 00:04:39.342 --> 00:04:40.493 So for one, 00:04:40.517 --> 00:04:44.372 we saw that she made regular phone calls to her family in Uganda. 00:04:45.629 --> 00:04:48.086 Well, it turns out that the data shows 00:04:48.110 --> 00:04:50.844 a four percent increase in repayment 00:04:50.868 --> 00:04:55.604 among people who consistently communicate with a few close contacts. 00:04:56.657 --> 00:04:57.816 We could also see 00:04:57.840 --> 00:05:00.648 that though she traveled around a lot throughout the day, 00:05:00.672 --> 00:05:03.403 she actually had pretty regular travel patterns, 00:05:03.427 --> 00:05:06.562 and she was either at home or at her food stall. 00:05:07.315 --> 00:05:11.302 And the data shows a six percent increase in repayment 00:05:11.326 --> 00:05:13.713 among customers who are consistent 00:05:13.737 --> 00:05:16.346 with where they spend most of their time. NOTE Paragraph 00:05:17.349 --> 00:05:19.930 We could also see that she communicated a lot 00:05:19.954 --> 00:05:22.302 with many different people throughout the day 00:05:22.326 --> 00:05:24.668 and that she had a strong support network. 00:05:25.287 --> 00:05:26.680 Our data shows 00:05:26.704 --> 00:05:31.384 that people who communicate with more than 58 different contacts 00:05:31.408 --> 00:05:34.309 tend to be more likely to be good borrowers. 00:05:34.333 --> 00:05:35.602 In Jenipher's case, 00:05:35.626 --> 00:05:39.669 she communicated with 89 different individuals, 00:05:39.693 --> 00:05:42.944 which showed a nine percent increase in her repayment. NOTE Paragraph 00:05:43.888 --> 00:05:47.655 These are just some of the thousands of different data points 00:05:47.679 --> 00:05:50.912 that we look at to understand a person's creditworthiness. 00:05:51.397 --> 00:05:54.470 And after analyzing all of these different data points, 00:05:54.494 --> 00:05:56.466 we took the first risk 00:05:56.490 --> 00:05:58.533 and gave Jenipher a loan. 00:05:59.370 --> 00:06:02.696 This is data that would not be found on a paper trail 00:06:02.720 --> 00:06:05.288 or in any formal financial record. 00:06:05.720 --> 00:06:07.508 But it proves trust. 00:06:08.193 --> 00:06:10.170 By looking beyond income, 00:06:10.194 --> 00:06:12.351 we can see that people in emerging markets 00:06:12.375 --> 00:06:16.044 that may seem risky and unpredictable on the surface 00:06:16.068 --> 00:06:20.294 are actually willing and have the capacity to repay. NOTE Paragraph 00:06:21.334 --> 00:06:26.519 Our credit scores have helped us deliver over 200,000 loans in Kenya 00:06:26.543 --> 00:06:28.140 in just the past year. 00:06:28.164 --> 00:06:31.466 And our repayment rates are above 90 percent -- 00:06:31.490 --> 00:06:36.785 which, by the way, is in line with traditional bank repayment rates. NOTE Paragraph 00:06:37.564 --> 00:06:40.473 With something as simple as a credit score, 00:06:40.497 --> 00:06:44.012 we're giving people the power to build their own futures. 00:06:44.647 --> 00:06:47.954 Our customers have used their loans for family expenses, 00:06:47.978 --> 00:06:49.835 emergencies, travel 00:06:49.859 --> 00:06:52.831 and for investing back into growing their businesses. 00:06:53.792 --> 00:06:57.264 They're now building better economies and communities 00:06:57.288 --> 00:06:59.612 where more people can succeed. NOTE Paragraph 00:07:00.664 --> 00:07:03.392 Over the past two years of using our product, 00:07:03.416 --> 00:07:07.600 Jenipher has increased her savings by 60 percent. 00:07:07.624 --> 00:07:10.276 She's also started two additional food stalls 00:07:10.300 --> 00:07:13.377 and is now making plans for her own restaurant. 00:07:13.401 --> 00:07:16.805 She's applying for a small-business loan from a commercial bank, 00:07:16.829 --> 00:07:21.554 because she now has the credit history to prove she deserves it. NOTE Paragraph 00:07:22.038 --> 00:07:24.953 I saw Jenipher in Nairobi just last week, 00:07:24.977 --> 00:07:28.228 and she told me how excited she was to get started. 00:07:28.252 --> 00:07:29.540 She said, 00:07:30.178 --> 00:07:35.657 "Only my son believed I could do this. I didn't think this was for me." 00:07:36.250 --> 00:07:38.536 She's lived her whole life 00:07:38.560 --> 00:07:42.853 believing that there was a part of the world that was closed off to her. NOTE Paragraph 00:07:43.536 --> 00:07:47.892 Our job now is to open the world to Jenipher 00:07:47.916 --> 00:07:52.111 and the billions like her that deserve to be trusted. NOTE Paragraph 00:07:52.135 --> 00:07:53.294 Thank you. NOTE Paragraph 00:07:53.318 --> 00:07:57.650 (Applause)