Dr. Peter Martey Addo

What I do

I can help you generate new ideas, innovation and improve growth.

Foresight & Strategy

Provide understanding of the potential of unleashing data & emerging technologies to improve growth.

Lead Interdisciplinary Teams

Experimentations, Research & Innovation.


Establishing the right partners to address key challenges.


Peter Martey Addo

Head of Emerging Tech Lab

Agence Française de Développement (AFD)


Dr Peter Addo is an experienced data scientist, and has an extensive background in working with data, and emerging technologies in the developing contexts. Currently, he is the lead on Artificial Intelligence, and serves as the head of Emerging Tech Lab at the French Development Agency (AFD), Paris, France. He leads efforts to provide advisory and actionable research on harnessing data and the technologies of the Fourth Industrial Revolution (4IR) driven by artificial intelligence (AI) for a sustainable development agenda, both broadening and deepening current action of Groupe AFD.

He formerly worked at the French National Center for Scientific research (CNRS) as a researcher, and at the French National railway company (SNCF) as the Lead Data Scientist. He holds a double PhD in Applied Mathematics and Economics.

He is keen on ways we can leverage data and emerging technologies to improve growth, nature, and people’s lives.

Recent Posts

Découvrez en vidéo le métier de Data Scientist à l’ AFD.

L’intelligence artificielle (IA) en Afrique - Entretien avec Next Einstein Forum

« Je vois la nécessité d’élargir notre connaissance et notre compréhension de la pertinence de l’intelligence artificielle pour l'emploi et la croissance en Afrique,» selon @PMarteyAddo. De quelle manière? Lisez son entretien sur le blog du #NEF2020!

Projects Opportunities


Open research collaboration

Outline of some of the research interest at the AFD Datalab that might interest you.

Opportunities - Research Assistant, Internships, Postdocs, CIFRE PhD

Research opportunities at AFD on data & emerging technologies (AI, Blockchain, …)

Potential Partnership with AFD

Seeking for an institutional partner to support initiatives that improve people’s life and leverage on emerging technology? Get in touch.

Recent Publications

Quickly discover relevant content by filtering publications.

Exploring Nonlinearity on the CO2 Emissions, Economic Production and Energy Use Nexus - A Causal Discovery Approach

Can countries make economic progress while reducing carbon emissions? Will reinventing multilateralism improve policy effectiveness on climate actions? Is it necessary to have new forms of cooperation & coordination between countries in other regional geographies to addressing the economic and decarbonisation debate? We examine the interactions between growth in CO2 emissions, economic production, and energy use at the global and multi-regional levels over the period 1990-2014. Methodologically, we use causal discovery that relies on linear and nonlinear tests of conditional independence to study their relationships.

Emerging Uses of technology for development - A new intelligence Paradigm

With only ten years left to achieve the Sustainable Development Goals (SDGs), development organizations rapidly need to innovate their approach to decision making and problem solving. New lessons and understandings, born from emerging uses of technology, can enable these innovations. This position paper uses the new paradigm of intelligence — which includes data intelligence, artificial intelligence, collective intelligence, and embodied intelligence — to provide development practitioners, policymakers, and decision-makers with an overview of the benefits and risks associated with various emerging uses of technologies. These assessments are illustrated where possible with examples from the field. It recommends the creation of a decision-making framework to help practitioners determine whether to invest in emerging technologies and how such technologies can effectively support development objectives. This early framework iteration focuses on carefully defining the relevant development objectives while taking into account the prevailing environment before addressing the solution by assessing the maturity, challenges, cost implications and risks of the technology’s use as well as the presence of enablers or disablers that could determine its impact and appropriateness.

Credit Risk Analysis Using Machine and Deep Learning Models

Due to the advanced technology associated with Big Data, data availability and computing power, most banks or lending institutions are renewing their business models. Credit risk predictions, monitoring, model reliability and effective loan processing are key to decision-making and transparency.

Insights to the European debt crisis using recurrence quantification and network analysis

The turmoil in the sovereign debt markets in Europe has raised concerns on the usefulness of sovereign credit default swaps and government bond yields in periods of distress. In addressing this issue, we introduce a novel nonlinear approach for the analysis of non-stationary multivariate data based on complex networks and recurrence analysis. We show the relevance of the approach in studying joint risk connections, extracting hidden spatial information, time dependence, detection of regime changes and providing early warning indicators. The feasibility and relevance of the approach in studying systemic risk is discussed. Finally, we share more light on possible extensions and applications of the approach to systemic risk.

Coupling direction of the European Banking and Insurance sectors using inter-system recurrence networks

Modern financial systems exhibit a high degree of interdependence making it difficult in predicting. This has raise concerns on the correct identification of coupling direction in financial sectors of the economy. This study explores a “two-way” risk connection between the European banking and insurance sector based on geometrical closeness of observations. Specifically, the study looks at the inter-system recurrence networks in tracing dynamical transitions and detecting coupling direction between these sectors. The overall results shows that the banking sector is central in risk transmission compared to the insurance sector. A comprehensive discussion of the feasibility and relevance of the approach in studying systemic risk is provided.


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