In early April, the World Bank published its State of Social Protection report (2025), earlier known as the ‘State of Social Safety Nets’ series. These reports use global-level data about social protection programs to reflect on trends in social security and to ascertain future requirements of social security.
In the first blog of this series of two, we break down (i) the Committee's methodology, (ii) key insights on the current state of AI adoption, (iii) FREE AI Framework and (iv) its recommendations.
The Consultation Paper sets out the landscape of use cases of AI prevalent in the Indian Securities Market.
IN FOCUS: FINANCIAL LIVES OF POOR HOUSEHOLDS 5 min read Arun and his family live in a rural town in Odisha. He is the sole income earner of his family. Arun is a part- time small holding farmer. He also works as an agri- labourer on his neighbour’s farm to support his family’s expenses. For […]
Fairness, explainability and human-in-the-loop principle are vital elements of Responsible AI
On 13 December 2024, the Ministry of Finance, Government of India (MoF) released the draft Banning of Unregulated Lending Activities (BULA) Act. This aligns with recommendations made in the Report of the Working Group on Digital Lending.
By studying people’s instinctive, unguided ‘trust-decisions’, we hope to uncover their mental models of trust. More specifically, we aim to (i) articulate the expectations that customers have of trustworthy lenders, (ii) help lenders design their products in a manner consistent with the customer's expectations, and (iii) translate these principle-level expectations into processes that lenders may adopt in their customer service to become trustworthy.
This Whitepaper unpacks what RTAI would mean in the context of digital lending. While the term Responsible AI has been adequately conceptualized in academia and elsewhere, it still needs to be coherently contextualized to specific domains.
In this blog, we delve further to uncover the institutional, cultural, and evolutionary factors underpinning the challenges of engendering formal savings among LIHs.
When AI-driven decisions are fair, respect privacy and are not opaque, they foster customer confidence
In all our research efforts, we strive to maintain an independent voice that speaks for the low-income household and household enterprises. Our ability to perform this function is significantly enhanced by our commitment to disseminate as a pure public good, all the intellectual capital that we create.
