Economics & Development

The Global Biodiversity Data and Analytics Hackathon was a year‑long virtual initiative (October 2024 – December 2025) led by the World Bank’s Knowledge, Information & Data Services (KIDS) team. It aimed to harness open biodiversity, geospatial, and landscape data to generate innovative policy insights, analytical tools, and digital products supporting both Biodiversity and Blue Biodiversity agendas. The hackathon was designed to support both Biodiversity and Blue Biodiversity work, reinforcing the World Bank’s integrated land‑to‑sea approach to nature conservation. By engaging in a multidisciplinary global community, the hackathon demonstrated how open data and analytics can inform conservation action, policy dialogue,

The development objective of the Gulf of Guinea Northern Regions Social Cohesion Project for Benin, Cote d’Ivoire, Togo is to improve regional collaboration and the socioeconomic and climate resilience of border-zone communities in the target northern regions of the Gulf of Guinea countries exposed to conflict and climate risks. This third additional financing (AF) will support the Governments of Benin, Côte d’Ivoire, and Togo to expand coverage across northern and border areas, while maintaining targeted support to areas with a growing presence of refugees and host communities. The project is also being restructured to: (a) adjust the results framework, including the addition of corporate scorecard indicators; (b) extend the closing date of the project for Benin from December 31, 2027 to December 31, 2028, for Côte d’Ivoire from May 31, 2027 to December 31, 2028, and for Togo from December 31, 2027 to May 31, 2029; and (c) change the implementing agency in Togo from the National Agency for Support to Grassroots Development (Agence Nationale d’Appui au Développement à la Base, ANADEB) to the Ministry of Finance and Budget (Ministère des Finances et du Budget, MFB).

The rapid advancement of Artificial Intelligence (AI), particularly in Large Language Models (LLMs) like Generative Pre-trained Transformer (GPT), presents significant opportunities for enhancing financial risk management practices. This paper examines the application of large language model (LLM)–based news summarization to financial risk surveillance at the World Bank Group. A cloud-native prototype, developed jointly by the Chief Risk Officer Market and Counterparty (CROMC) and the Technology Innovation Office (ITSTI), automates the sourcing, filtering, and summarization of financial news using the GPT family of models (OpenAI). The prototype integrates automated sourcing from curated outlets, taxonomy-based filtering, full text retrieval, LLM summarization, hallucination checks, and threshold-based alerting tied to key financial indicators (e.g., yields, foreign exchange, equity volatility). The paper concludes with lessons learned from this fruitful collaboration and recommendations for future work, including the potential for broader application for other departments and beyond.

The development objective of the Social Protection and Economic Resilience Project for Mozambique is to improve the effectiveness and efficiency of the social protection system. The additional financing is proposed in response to overlapping climate and economic shocks, including severe floods and global price pressures, a persistent jobs shortfall for youth, and the need to modernize delivery systems to support both protection and economic inclusion; it seeks to provide timely consumption support to poor and vulnerable households, accelerate a coherent pathway to income generation, and consolidate core systems to operate at scale with greater transparency and accountability. The Bank will work in three areas: first, shock-responsive assistance by scaling up emergency cash transfers for households affected by floods and price shocks, prioritizing rapid vertical expansion using existing registries and piloting data-driven horizontal expansion where needed; second, a consolidated recovery and productive inclusion package delivered through the existing safety net, combining sequenced interventions such as climate-smart public works, regular cash top-ups to stabilize consumption, life skills and financial literacy, peer coaching, and a one-time grant for productive investment, with a focus on women and youth in the poorest, disaster-prone districts; and third, systems strengthening by legally and technically establishing a national social registry, modernizing the management information system (MIS), expanding interoperable and tiered digital payments, reinforcing institutional capacity at central and local levels including the National Social Protection Institute (INAS), enhancing monitoring, grievance redress, and communications for transparency, and supporting performance-oriented management. The project intends to deliver timely post-shock support, enable households to sustain gains and transition toward livelihoods and jobs, and embed resilient, digital delivery systems that improve targeting accuracy, payment integrity, and service delivery nationwide while maintaining a contingent emergency response mechanism for future crises.