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financial inclusion


AI for Financial Inclusion: Banking the Unbanked

#artificialintelligence

But AI is laying the foundations to change that. Pioneers and FinTechs, followed now by some banks, are using AI to bring financial services to the unbanked, starting from "branchless banking" models, which are one hundred percent based on digital channels and with new business models leveraged in economies of scale. These companies are using AI to improve (and also create new) processes related to communication, fraud prevention, credit scoring and financial education.


The world needs better convening that fosters collective action

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COVID-19 has exposed and accelerated many trends of globalization. Development actors find themselves up against a host of risks and challenges that no organization can resolve on their own. Addressing the health and economic fallout from the pandemic, vaccine development, climate change, financial contagion, infectious diseases, forest and biodiversity loss, overfishing, antimicrobial resistance, governance of artificial intelligence, and many other risks and challenges call for effective collaboration across national and organizational boundaries. International development organizations need to collaborate and act collectively to develop effective solutions. Convening, when successful, achieves such collective action.


Super-App Behavioral Patterns in Credit Risk Models: Financial, Statistical and Regulatory Implications

arXiv.org Machine Learning

In this paper we present the impact of alternative data that originates from an app-based marketplace, in contrast to traditional bureau data, upon credit scoring models. These alternative data sources have shown themselves to be immensely powerful in predicting borrower behavior in segments traditionally underserved by banks and financial institutions. Our results, validated across two countries, show that these new sources of data are particularly useful for predicting financial behavior in low-wealth and young individuals, who are also the most likely to engage with alternative lenders. Furthermore, using the TreeSHAP method for Stochastic Gradient Boosting interpretation, our results also revealed interesting non-linear trends in the variables originating from the app, which would not normally be available to traditional banks. Our results represent an opportunity for technology companies to disrupt traditional banking by correctly identifying alternative data sources and handling this new information properly. At the same time alternative data must be carefully validated to overcome regulatory hurdles across diverse jurisdictions.


Solving the Credit Impasse: How Big Data and AI are Generating Funding Opportunities for Smallholder Farmers in Africa - NextBillion

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Agriculture finance represents an important element of eradicating extreme poverty and boosting shared prosperity. According to the International Fund for Agricultural Development, smallholders manage over 80% of the world's estimated 500 million small farms and provide over 80% of the food consumed in a significant part of the developing world, making a major contribution to poverty reduction and food security. Most smallholder farms are in Asia and sub-Saharan Africa, and in both regions over 80% of farmland is managed by smallholders. Even though these farmers are generally characterized by limited resources--particularly in terms of land--and dependence on household members for farm labor, they represent a critical part of food systems in developing countries. In light of the size and importance of the smallholder farming sector, the development community has a growing focus on providing these farmers with the funding they need to thrive.


How artificial intelligence, STOs, decentralization, and financial inclusion will drive the future of investment DAO.digital

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TTChain is a global service provider for decentralized blockchain and cross-chain lightning transaction ecosystems, and it will solve several key challenges of digital asset exchanges and public blockchains, including slow transaction speeds, high handling fees, insecurity due to over-centralized exchanges, and the inability to exchange digital assets across different blockchain protocols. The solution will provide the technical infrastructure to the MASEx platform, through its experienced team led by CTO Simon Hsu, who formerly worked at Microsoft.


Mass Adoption Of AI In Financial Services Expected Within Two Years

#artificialintelligence

Percentage of reported'significant' AI-induced increases in profitability by current R&D ... [ ] expenditure on AI (Figure 2.17 in Survey) A significant number of executives from 151 financial institutions in 33 countries say that within the next two years they expect to become mass adopters of AI and expect AI to become an essential business driver across the financial industry. This information was collected as part of a survey on AI in Financial Services conducted by the World Economic Forum in collaboration with the Cambridge Centre for Alternative Finance at the University of Cambridge Judge Business School and supported by EY and Invesco. The objective of the study was to understand the opportunities and challenges that will result from mass adoption of AI in Financial Services. The research was published in a 127-page report entitled Transforming Paradigms A Global AI in Financial Services Survey. Financial Services sectors represented in the survey sample.


The Value of Big Data for Credit Scoring: Enhancing Financial Inclusion using Mobile Phone Data and Social Network Analytics

arXiv.org Machine Learning

Credit scoring is without a doubt one of the oldest applications of analytics. In recent years, a multitude of sophisticated classification techniques have been developed to improve the statistical performance of credit scoring models. Instead of focusing on the techniques themselves, this paper leverages alternative data sources to enhance both statistical and economic model performance. The study demonstrates how including call networks, in the context of positive credit information, as a new Big Data source has added value in terms of profit by applying a profit measure and profit-based feature selection. A unique combination of datasets, including call-detail records, credit and debit account information of customers is used to create scorecards for credit card applicants. Call-detail records are used to build call networks and advanced social network analytics techniques are applied to propagate influence from prior defaulters throughout the network to produce influence scores. The results show that combining call-detail records with traditional data in credit scoring models significantly increases their performance when measured in AUC. In terms of profit, the best model is the one built with only calling behavior features. In addition, the calling behavior features are the most predictive in other models, both in terms of statistical and economic performance. The results have an impact in terms of ethical use of call-detail records, regulatory implications, financial inclusion, as well as data sharing and privacy.


Mass Adoption Of AI In Financial Services Expected Within Two Years

#artificialintelligence

Percentage of reported'significant' AI-induced increases in profitability by current R&D ... [ ] expenditure on AI (Figure 2.17 in Survey) A significant number of executives from 151 financial institutions in 33 countries say that within the next two years they expect to become mass adopters of AI and expect AI to become an essential business driver across the financial industry. This information was collected as part of a survey on AI in Financial Services conducted by the World Economic Forum in collaboration with the Cambridge Centre for Alternative Finance at the University of Cambridge Judge Business School and supported by EY and Invesco. The objective of the study was to understand the opportunities and challenges that will result from mass adoption of AI in Financial Services. The research was published in a 127-page report entitled Transforming Paradigms A Global AI in Financial Services Survey. Financial Services sectors represented in the survey sample.


Deep Dive: How Anuj Kacker Of MoneyTap Uses AI/ML For Financial Inclusion

#artificialintelligence

Lending by Fintech, startups have added new dimensions to the financial intermediation process and has boosted financial inclusion, especially by helping borrowers. The Fintech lending industry is constantly innovating and is set to grow to $100 billion by 2023. Also, a growing number of non-financial startups such as Ola and Mi are trying to become lending players as well. To know more about this industry and for this week's Deep Dive, Analytics India Magazine got in touch with Anuj Kacker, COO and Co-Founder of MoneyTap. Bangalore-based MoneyTap was launched in 2015 and is currently available in 44 cities in India.


The Big 7 2019: Regtech, Cybersecurity, Payments, Insurtech, Blockchain, AI and Financial Inclusion

#artificialintelligence

We asked 9 industry experts to contribute their thoughts on the year ahead, and a common theme was the need for these technologies to mature, with the genuinely useful implementations finally getting to market. Expanding on last year, we have chosen seven areas of interest to focus on in 2019. Each represents a vital area of innovation in the financial industry, and has a particular relevance to Luxembourg's thriving financial technology ecosystem. Each week we will be choosing one of the topics to focus on, both in the content we share on social media, but also in a dedicated newsletter looking at the top five stories from that week. First, let's introduce the topics with some of our favourite summaries for the uninitiated: "Regtech growth will explode in 2019 because regulators worldwide will start truly driving it. Multiple countries will hold a joint hackathon at midyear, aiming to use technology to remove one of the biggest regtech blockers: how to share data widely to find risk patterns and fight financial crime, while fully protecting privacy and cybersecurity. Solutions will solve myriad regulatory problems. Even more importantly, the shared experience will move regulatory bodies into a new era of active innovation and collaboration with each other, industry, and academia. Anti-money laundering will continue to be a leading use case, because the current system is so broken and costly and there's so much low-hanging fruit to harvest through technology. We'll also see AI and blockchain solving more problems, from digital identity and financial fairness and inclusion to API-based regulatory reporting, machine-readable regulations, and even machine-executable compliance. These regulatory breakthroughs are not just nice-to-have. They are essential, if fintech innovation is to flourish. The regulations are the rules of the road we're all traveling."