AI

Predicting Payment Behavior in PAYGo: Machine Learning Can Power Customer Retention

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The PAYGo model makes solar affordable for end-users and provides sufficient margin for providers to scale last-mile distribution. However, for the model to succeed PAYGo operators must retain customers and build a base of loyal and engaged customers. Our project with Zola Electric (formerly Off Grid Electric) demonstrates that machine learning can help them do so. Read the post.

 

AI: From Automation to Augmentation

Discovering the Practical Superpowers of Machine Learning

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At FIBR, we are excited about the potential for AI technologies to reduce some of the complexity that defines the lives of lower-income segments that we work with. Our new report gathers examples of fintech providers already using AI to extend financial inclusion across Africa and shows the practical superpower applications of AI. Read on to find out what we’re discovering about machine learning and AI in Africa.

Interested in learning more about AI and our AI applications, join us for the AI webinar on June 6, 2018, 9am EST.

 

FIBR.AI - An Experimental Gallery of AI Applications for MSMEs and PAYGo

The FIBR AI Gallery serves the goal of accelerating the introduction, adaptation and productization of AI tools in financial services in the PAYGo and MSME sectors. As a starting point we are actively exploring scenarios in which computer vision, predictive analytics and natural language processing can be used to produce gains and alleviate pains for PAYGo providers and merchants.

Our goal for the AI Gallery is to bridge the gap between the real needs of our partners and the cutting-edge AI developments that have the potential to address these needs. We are accomplishing this by first demonstrating to these partners some of these technologies in their raw, non-productized form and openly discussing whether and how they see these technologies being useful in their day-to-day life. 

Artificial Intelligence, Machine Learning and Financial Innovation

The Case for AI for Financial Services in Africa

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AI has the potential to transform financial inclusion across Africa. A PAYGo provider no longer has to conduct a lengthy field visit to assess whether to enter a new market. Instead, they harness their own data along with contextual data like access and usage of mobile money agents to analyze the business case. SME retailers no longer struggle with inventory management and restocking their shelves in a timely manner. Rather, they use a product recognition app to automatically notify their wholesaler when they need more inventory. These are just a few of the potential uses of artificial intelligence (AI) and more specifically, machine learning (ML), to change the landscape of financial innovation across sub-Saharan Africa. Continue reading

 

Webinar: "Artificial Intelligence: Practical Superpowers," a New Report by BFA

Following the launch (May 16th) of the FIBR report, “Artificial Intelligence: Practical Superpowers,” we are hosting a follow-up webinar to present the paper and its insights.

As one of the first reports that looks at AI applied to financial services in Africa, this webinar is for fintech companies and FSPs in Africa, that might be interested or looking to move into AI.

By presenting insights from the report and having a panel discussion, we seek to create awareness around the real-use cases of AI for FSPs that can augment the ability of companies to do business better with AI-powered tools.

  • When: 6 June 2018, 9am-10:00am EST

  • What and Where: Presentation and panel via webinar

Panelists:

  1. Matt Grasser, Deputy Director of Inclusive Fintech | BFA

  2. Qiuyan Xu, Chief Data Scientist | Cignifi

  3. Andrea Ottina, Chief Business Development Officer | Access Tanzania

  4. Sheel Mohot, Partner | 500 startups

Moderated by Jane del Ser, Insights & Influence at BFA

 

Related Content

"Artificial Intelligence: Practical Superpowers," a New Report

With knowledge comes power. Artificial intelligence (AI) is a powerful way to harness data into knowledge and into action.  For financial services providers in Africa, the real value of AI and its forms, such as machine learning, lies in practical applications that can reduce the cost to serve and acquire customers -- creating more viable business models for broader segments of the market. Several financial services providers are already using AI to eliminate business inefficiencies, manage business and customer risk and create more seamless customer experiences. More and more providers, not just the large players, have the opportunity to do the same.

On May 16th, BFA launched the first-ever report on artificial intelligence as it relates  to financial services in Africa and the low-income customer. “Artificial Intelligence: Practical Superpowers,” is relevant for fintech companies and FSPs in Africa who are interested in or looking to move into AI. The FIBR paper presents a practical and compelling case for companies to seriously consider AI in their business by:

  1. Identifying the main use cases in financial services through industry players in Africa

  2. Providing real examples of applied machine learning through BFA’s portfolio of work with low-income customers and inclusive fintech companies

  3. Covering the challenges and introducing an AI readiness framework for firms take next steps and test-driving AI demos from the MSME and PAYGo sectors

 

Join Us for the Report Launch Reception

Date: Wednesday, May 16th, 2018

Time: 6:00 - 9:00 PM

Venue: VILLA ROSA KEMPINSKI, Nairobi

With remarks from: 

Amolo Ng'weno - FIBR Program Director, East Africa director, BFA

Matt Gamser -  CEO of the SME Finance Forum

Finbots for Shopkeepers #3: Going from Data to Information

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Finbots for Shopkeepers Series #3

With a single smartphone, small business owners can go from completely opaque information to being inundated with raw business data. Not bad for a $50–100 investment in technology! However, the raw data alone doesn’t make a difference — it’s the actionable insights distilled from the data that matter. So how can we turn sensor data into information that shopkeepers can use?

Learn more