This report examines inclusive credit's impact using a five-factor framework: who, how, what for, where, and when. Drawing on 405 studies and machine learning analysis, it advocates for "precision credit"—matching product design to borrower circumstances—to move beyond average effects and ensure meaningful developmental outcomes in emerging markets.

Developed by the Anthropocene Fixed Income Institute, the SLB Pricer helps investors value the embedded options and step-up features of Sustainability-Linked Bonds. It provides a quantitative framework to ensure bond structures remain robust and ambitious.

The Natura Future P&L Tool is a free digital platform that enables businesses to measure and monetise their environmental, social, and human impacts. It helps finance professionals integrate ESG externalities into traditional financial analysis to assess a company’s total value creation.

CGAP introduces Precision Causal Modelling to help financial providers identify which product configurations work for specific client segments. Piloted across five institutions, the methodology revealed delivery gaps of 32 to 84 percentage points. Key findings highlight the importance of right-sizing loans and monitoring delinquency trajectories to improve client outcomes.

This report provides six policy considerations for financial sector authorities to promote competition and advance financial inclusion. Drawing on eight country case studies, it explains how regulators can use existing mandates, coordinate across sectors, and leverage infrastructure design to address market concentration and support digital financial innovation.

Curating 56 successful fraud protection solutions, this report details strategies across intelligence sharing, telecom security, and biometrics. It highlights how AI-driven oversight and multi-sector collaboration, such as Malaysia's NSRC and UK's reimbursement rules, can reduce losses and restore consumer trust in digital financial ecosystems.

The Climate Risk Signals Explorer is an AI-powered analytical tool by Climate Proof that tracks climate risk mentions in corporate earnings calls, helping finance professionals monitor physical and transition risks.

This research investigates AI-managed household portfolios, finding that large language models recommend undiversified portfolios concentrated in high-momentum, large-cap technology stocks. While returns may exceed market benchmarks, they do not provide abnormal returns when adjusted for risk characteristics. Recommendations are primarily driven by media attention rather than investment skill.

Research demonstrates that Large Language Models exhibit purpose-conditioned bias when informed of downstream tasks. Goal-aware prompting leads to in-sample overfitting and inflated performance before knowledge cutoffs. Results indicate that disclosing objectives compromises neutrality, necessitating the separation of measurement and evaluation in AI-assisted workflows to ensure statistical validity.

This research investigates the demand for explainable AI in high-stakes credit settings. It finds that decision-makers strategically avoid explanations that reveal racial or gender bias to preserve moral wiggle room. Additionally, behavioural biases cause individuals to undervalue explanations even when they complement private information and improve decision accuracy.

This research examines the tradeoff between lender profits and social impact using machine learning across three countries. It demonstrates that algorithmic targeting significantly boosts profit margins in South Africa and the Philippines but may reduce financial inclusion for women and low-income borrowers unless balanced strategies are implemented.

This paper examines Kenya's 2016 interest rate regulation, which capped bank lending rates but exempted the digital platform M-Shwari. Using borrower-level administrative data and a structural model, the authors find that the M-Shwari carve-out preserved credit access for high-risk borrowers, while a uniform cap would have eliminated it entirely.

This paper is the African Chapter of GDF's Global Stablecoin Regulatory Playbook. It examines how global stablecoin regulatory principles can be applied across Africa's diverse markets, addressing reserve management, consumer protection, AML/CFT compliance, and cross-border coordination, while accounting for local financial infrastructure, dollarisation risks, and varying supervisory capacity.

This report examines the impact of digital innovation on financial health. It outlines opportunities in payments, credit, savings, and insurance, whilst highlighting emerging risks such as fraud, overindebtedness, and ill-suited investments. The authors propose policy responses to enhance regulatory frameworks and promote responsible digitalisation in financial services.

This report proposes a rigorous framework for third-party auditing of frontier AI systems to verify safety and security claims. Addressing the opacity of current self-assessments, it advocates for structured AI Assurance Levels, deep access to non-public information, and continuous monitoring to enable confident deployment and standardisation across the industry.

This report explores how alternative data and artificial intelligence are redefining credit scoring to enhance financial inclusion for women and underserved borrowers. It analyses market trends, evaluates the risks of algorithmic bias, and provides actionable recommendations to scale responsible, inclusive credit access across emerging markets.