Empowering Fin-tech Merchants: AI-Powered Interactive Information Journeys in Real-world Trials

Yixiang Xu, Rupa Ruchismita, Ganesh Iyer

In emerging markets, local merchants often act as financial agents, providing crucial access to banking services for underserved communities. By significantly reducing the cost of financial access, these fintech-enabled merchants offer a promising path for financial inclusion.

However, this channel suffers from poor merchant retention rates. A range of factors, including the high cost of sourcing both firm and market information impede merchant profitability. Indeed, of the 3,000 surveyed merchants, more than 50% found it difficult to acquire necessary know-how to provide reliable banking services. For the fintech firms, providing timely assistance to a large number of dispersed merchants has proven to be financially unsustainable, creating a demand for low-cost technologies for network support.

To mitigate these challenges, we leverage Generative AI to design personalized, interactive and digestible ‘information journeys’ covering a wide range of business-relevant content for merchants. To further enhance the information acquisition experience for merchants, we use Large Language Models (LLM) to create multimedia content with text, audio and video content journeys in the local language.

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Impact of Video Know-Your-Customer (KYC) technology to improve financial inclusion in India

A thought leader, innovator and implementer of technology solutions. FINO enables end-to-end customer sourcing and servicing.

Segments of the population in low- and middle-income countries, such as daily wage earners or small business owners in rural areas, typically have limited access to formal financial services. Contactless digital payment technology could expand financial access for underbanked populations by reducing the need for physical banks and in-person services, which would also limit risky person-to-person interaction during the COVID-19 pandemic.

On My Own Time: Asynchronous Teamwork and Gender Differences in Performance

Aruna Ranganathan, Associate Professor

The future of work is seeing increased temporal restructuring, with less full-time work, less adherence to the five-day work week and greater scope for working from anywhere at any time (Moen, Kelly and Hill, 2011; Moen et al., 2011; Correll et al., 2014). Temporal restructuring at the workplace has manifested itself particularly in the form of asynchronous teamwork, where team members complete work at different times of the day, either from the same or different geographic location. However, there is little research on the effects of asynchronous teamwork on the performance of individuals within such teams.

Researchers work with Baul singers’ folk-music-ensemble recordings as the setting for their study. Bauls typically perform in ensembles consisting of one lead singer and a number of instrumentalists. While the lead singers can be men or women, the instrumentalists are almost always men. We focus on synchronous and asynchronous music recording experiences of men and women Baul lead singers to investigate differential performance effects by gender as a result of temporally restructuring work.

Can a multi-generational intervention improve daughters’-in-law sexual and reproductive health and mothers’-in-law psychological well-being? In Tamil Nadu, India

David Levine, Professor

Poor women (and their children) suffer from many sexual and reproductive health challenge, including unsafe menstrual supplies, lack of access to family planning, and domestic violence. The Tamil Nadu State Rural Livelihoods Mission (TNSRLM) runs a network of self-help groups (SHGs) for millions of rural women. This project involves a pilot test  of an intervention to improve sexual and reproductive health. Our intervention will both provide information and focus on behavior change. For example, we will offer safe and reusable menstrual supplies (that can improve women’s savings), a meditation audio that helps some women with menstrual cramps, and an app or calendar to track periods.

Reducing Tariff Evasion at the Ports of Paraguay using Machine Learning (ML)

Ernesto Dal Bó & Frederico Finan

Gender Representation, Identity, and Learning in Brazilian politics

Francesco Trebbi

Developing an ML Decision Support System for Digital Finance in Emerging Markets

Ganesh Iyer, Yixiang Xu, Rupa Ruchismita

The rails for the ongoing digital payments revolution in emerging markets are dependent on Cash-in Cash-out merchant terminals at small mom and pop stores. These merchants play a crucial role in providing access to financial services in underserved regions. However, payment firms face high merchant churn rates and an even greater merchant dormancy challenge.

To tackle the challenge, we develop a two staged project and test a machine learning (ML)-driven decision support system (DSS) to augment merchant decision-making.

Improving online sales through a new model of gamification-Help and Haggle: China

Luyi Yang, Assistant Professor

This research is focussed on the fundamental question of firm growth through the lens of a novel approach – “Help-and-Haggle,” a social e-commerce scheme pioneered by Pinduoduo (PDD), one of China’s most popular e-commerce sites. “Help-and-Haggle” integrates online shopping with social media and effectively turns shopping into a game. A consumer initiates the help-and-haggle campaign by selecting her desired product. Then, a 24-hour timer is started. The consumer shares a custom link about the campaign and the product with friends in her social network, inviting them to “help” her “haggle” over the product price. Each friend who helps click on the link (no purchase required) triggers a tentative, random discount on the product price (i.e., a price cut) for the initiating consumer. If the consumer manages to gather enough clicks and cut the price down to zero within 24 hours, she gets the product for free. Otherwise, the campaign fails, and the product reverts to the original price.

The research will develop a dynamic game-theoretic model of “Help-and-Haggle ” to gain insight into how to manage such a program most effectively.
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How trust and reputation factor into the investment in, adoption of, and pricing of emerging AI enabled technologies in healthcare and financial markets.

Matthew Grennan, Associate Professor

The Potential of Transport Platforms to Reduce Emissions in Developing Country Cities: Cairo, Egypt

Nick Tsivanidis, Assistant Professor

Transportation generates the largest share of greenhouse gas (GHG) emissions and is a primary driver of pollution in cities. In rapidly growing cities that lack formal mass transit systems, new app-based platforms like SWVL, Uber Bus, Treepz and ViaPool are introducing transit services across the Middle East, Africa and Asia and are rapidly growing.

Human Centric Operations for the Future of Work

Park Sinchaisri, Assistant Professor

The rapid pace of technological change and the rise of the gig economy are leading to a computationally mediated future of work. While such a future promises increased productivity and flexibility, most work is characterized by tasks carried out in isolation and mostly devoid of learning and collaboration. For example, the independent nature of ride-hailing work means that drivers do not experience the benefits of learning from colleagues. At the same time, the decisions a worker faces have become more complex as platforms dynamically offer competing incentives.

To address these issues, researchers introduce and explore human-centric operations management for the future of work with the emphasis on the development of algorithmic tools that can automatically generate useful advice to human users. This research involves designing and evaluating human-AI interfaces to facilitate understanding and adoption of algorithmic advice in ways that upskill individuals, enhance their experience, and lead to improved collective outcomes.

Mobile Money in Tanzania

Przemyslaw Jeziorsk, Assistant Professor