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Two Problems, One Solution: How Fintech is Boosting Access to Banking and Insurance for Domestic Workers in Mexico

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More than 2 million domestic workers in Mexico are virtually invisible to the financial system. They perform work as housekeepers, cleaners, cooks, carers, drivers, gardeners and doormen, among other professions. Nearly all are informally employed, which means they get paid in cash, make no contributions to a social security or pension fund, and are uninsured. 

Yet many domestic workers have a relatively stable source of income (when work is available) and they are financially active. More than 75 percent of them earn up to only 58,000 pesos a year (about US$3,100).

In this environment, Comunidad4UNO (4UNO), an early-stage Mexican fintech startup and Catalyst Fund company, is tackling the dual challenges of financial exclusion and affordable insurance access among this market segment. The company is providing domestic workers with tailored and market-based financial products through their employers via an online marketplace, helping reduce their vulnerability to accidents, health issues and the financial challenges they can cause- read more.

Fintech finds a way to reach domestic workers with financial services in Mexico

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Lola, a single mother of three, earns a living cleaning houses in Mexico City. She gets paid in cash every week, and asks for an advance from her employers when she really needs it, like when her mother passed away in her village and she had to cover transportation and funeral costs or when she was mugged on a public bus. Lola stores small sums of money at home to pay for food, rent, electricity, water and gas.

As is the case with many domestic employees, her job is informal and unsecured. Sometimes, she has to miss work when her kids get sick and, depending on the employer, she may or may not get paid for those missed days. She is unfamiliar with her labor rights and her employers, who are primarily concerned with employee retention, are generally oblivious to their legal obligations.

Although Lola uses several financial instruments to make ends meet, pay her bills and provide for herself and her children, her transactions generate no records in the financial system, thus making her invisible to banks and other financial institutions- read more.

Three Powerful Tools for Fintech Practitioners

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Since we launched the Catalyst Fund in 2015, we have helped 15 fintech entrepreneurs deploy novel approaches to bring products and services to their customers. We have distilled the successful patterns and behaviors we have observed into toolkits and posts for those considering fintech methods for their businesses, whether they be startups or established players.

At a high level, successful fintech startups adopt principles of Design, Risk Management and Product Management, and also put modern technologies like smartphones, artificial intelligence and cloud computing at the core of their value propositions. At successful fintech startups Designers, Product Managers, CEOs and Engineers reinforce each other in multidisciplinary teams to explore the overlap between what customers find desirable, what engineers can build, and what the business requires to grow - continue reading.

 

Why We Invested: Meet the newest Catalyst Fund Companies

Abalobi: The fisher's journey

Abalobi: The fisher's journey

What we’re seeing in insurtech and digital credit

We’re excited to announce the latest companies to join the Catalyst Fund portfolio! Our new cohort of inclusive fintech startups is innovating in two promising areas: insurance technology (“insurtech”) and digital credit. As a group, these companies exhibit strong founding teams, experience working in emerging markets, and a resolute commitment to reach the underserved. As an early-stage accelerator committed to expanding innovative financial solutions for the the underbanked, we invested in these companies because they share the following qualities. Read more.

How can investors use machine learning to pick the right startups?

Photo credit:  Machine perception

Photo credit: Machine perception

When considering a startup, especially an early-stage startup, investors want to conduct as much due diligence as possible. What little data they can gather is scattered all over different sources including Crunchbase, LinkedIn, Pitchbooks, company websites, etc. Consolidating this data takes a great amount of time and effort. Furthermore, the data sets can be incomplete or biased depending on the search queries — imagine overlooking a keyword. To make the due diligence process fairer and less cumbersome for investors, various platforms are using machine learning (ML) to pull together information about startups from all available resources to help investors assess companies and investment opportunities. But where machine learning really shines is in the interplay of data-driven insights that are qualified by human intuition and personal experience. Read full blog here.

Top 100 Emerging Market Inclusive Fintechs

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You may have heard of companies like WePay and Oscar. WePay is an online payments service provider; and Oscar is a user and technology-centered health insurance company. And you might have witnessed how these fintech companies are contributing to the transformation of the financial services sector in the United States. But what’s happening in the global south? There, “inclusive fintech”, or fintech products and services that serve the bottom of the pyramid, is thriving in its own right with the likes of Tala and Branch. The sector is growing – enough to compile a list of our top 100 inclusive fintech companies by funding raised in emerging markets.

Catalyst Fund presents the Top 100 Emerging Market Inclusive Fintechs. Get insights about accessibility, affordability & flexibility, customer-centric design, speed and digital-first approaches and how these approaches can improve outcomes.

Article originally published on the World Economic Forum

Next-level computer vision: When off-the-shelf software options don’t cut it

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Let’s say you’re in Tokyo and you see a billboard with a catchy photo. You want to know what the billboard says, but you can’t read Japanese. No problem! You can use Google Translate to take a picture of the billboard, highlight the text you care about, and then your phone will translate it into English. This is a prime example of computer vision, which allows us to automatically extract, analyze, and understand information from images due to recent advances in machine learning.

Given these advances, we thought it would be straightforward to adapt off-the-shelf computer vision programs to pull important data from identification documents, a task that most fintech companies face in the field. Ultimately we found that off-the-shelf computer vision programs were not sufficiently accurate  - continue reading here.

Early insights on incentivizing Indian customers to go cashless

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Early this year, Amitabh Kant, CEO of the National Institution for Transforming India (Niti Aayog), declared “Cards, ATMs, POS will all become redundant in India by 2020, and India will make this jump because every Indian will be doing his transaction just by using his thumb in thirty seconds….”Although evidence indicates that demonetization has moved India towards a digital economy, there is still much work to do before Kant’s vision becomes a reality. Read insights on going cashless.

SOCAP Conversations: The State of the Inclusive Fintech Field

The Inclusive Fintech Track at SOCAP17

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We are excited to announce that Catalyst Fund and JPMorgan Chase & Co. are supporting a content track on Inclusive Fintech at SOCAP17. David del Ser, Director of Inclusive Fintech for Catalyst Fund, and Colleen Briggs, Executive Director of Community Innovation at JPMorgan Chase & Co. sat down with the SOCAP organizers to discuss the state of the inclusive fintech field and the sessions they are developing for SOCAP17. Read the interview http://bfa.works/thest2ce2

Originally published on the SOCAP blog on September 12, 2017.

Register for SOCAP17 to attend the Inclusive Fintech Sessions and learn more.

Catalyst Fund and JPMorgan Chase & Co. are sponsoring the Inclusive Fintech Track at SOCAP17

The AI-Driven Bank in Emerging Markets

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Article written by David del Ser and Alexandre Lazarow

If you think artificial intelligence is a thing of the future, ask yourself why your Amazon homepage looks different from ours or how Google translates websites on the fly. Consumers in developed and developing countries are benefiting today from these smarter types of software. In particular, the subfield of Machine Learning is making rapid strides in everything from detecting rare types of cancer to correcting our grammar. In essence, software development is transitioning from a process where humans figure out a few rules for the machines, to one where the machines learn the many implicit rules from the existing data. Continue reading.

An Interview with Alexandre Lazarow of Omidyar Network

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Catalyst Fund recently had the opportunity to speak with Alexandre Lazarow, CFA, Principal, Investments at Omidyar Network and member of the Catalyst Fund Investors Advisory Committee (IAC) about the Catalyst Fund model, fintech startups, innovations and InsureTech. Read his interview on our medium blog

What is the Investment Climate for Fintech in Emerging Markets?

A positive outlook according to our investor network

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We recently had the opportunity to survey the Catalyst Fund Circle of Investors, which connects our startups to a wider group of investors and also acts as a channel for sharing our insights about the investment climate for fintech in emerging markets. The Circle of Investors are optimistic about funding for inclusive fintech. Results of the survey provide insights into what investors think about inclusive fintech, investments and emerging markets. Read more

Originally published on the SOCAP blog on August 22, 2017.

Register for SOCAP17 to attend the Inclusive Fintech Sessions and learn more.

Catalyst Fund and JPMorgan Chase & Co. are sponsoring the Inclusive Fintech Track at SOCAP17

 

Racing into Machine Learning: Data Readiness and the Developing World

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While machine learning has become more widespread and has been applied across different industries, few organizations have the adequate expertise to utilize the technology properly. Before jumping into a #machine learning investment, stop and consider your data and how a strong data foundation should be used to inform the insights from machine learning. Read about #datareadiness to start off on the right foot.