This report examines how digital innovations address barriers to inclusive agricultural credit in developing countries. Through five case studies, it highlights the role of agri-data stacks and AI-powered analytics in reducing information asymmetries, improving risk management, and lowering service delivery costs for smallholder farmers and agri-MSMEs.

This report examines the growth of crypto-enabled financial crime and the resulting governance gap. It proposes a four-layer multilateral framework to harmonise international standards between bodies like the FATF and INTERPOL. The strategy aims to protect financial integrity through technical integration and cross-border cooperation without suppressing permissionless innovation.

Wikirate 2.0 is an open-source ESG data platform providing over 8 million data points on 150,000+ companies. It centralises benchmarks, corporate metrics, and supply chain data to support transparent investment analysis and corporate accountability.

This field experiment in rural Sierra Leone evaluates upgrading Village Savings and Loan Associations into digital financial service agents. While infrastructure expands access, introducing consumer feedback mechanisms significantly improves service quality, transaction usage, and household shock mitigation. Results demonstrate that accountability is essential for developing functional, scalable financial markets.

This report examines implicit and explicit LLM biases in investment management. Through a case study on framing bias across nine OpenAI models, it identifies significant sensitivities in drawdown and client retention scenarios. It concludes that manual prompt restructuring and human-in-the-loop protocols are the most effective mitigation strategies.

This research analyses Singapore's multi-layered approach to regulating artificial intelligence in finance. It evaluates frameworks like the FEAT Principles and Project MindForge while identifying risks such as deepfakes and bias. The paper recommends establishing a dedicated regulatory sandbox and shared responsibility frameworks to manage evolving technological and third-party risks.

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.