Fairness, explainability and human-in-the-loop principle are vital elements of Responsible AI

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

The response presents our thinking on the governance of artificial intelligence (AI). It is divided into two parts. Part A summarises our key inputs which are also presented below in the form of this write-up. Part B provides a section-by-section paragraph-wise detailed feedback as per the Ministry’s Consultation Form requirements.

Establishing principle-level guidance on operationalising Responsible AI would induce clarity and confidence

In this post, we present our findings from our literature review based on which we conducted the behavioural study.

This post summarises how BNPL lending functions and highlights customer protection concerns that can put at risk the promise of better credit access.

Even when women are not the primary borrowers of formal finance, studies note that they remain responsible for ensuring timely repayments.