AI is no longer just a technological trend — it is a transformative force that is reshaping how businesses operate. Its rapid deployment interests all-providers, policymakers, and those for whom its designed. As AI adoption accelerates across sectors, so does the need for a better understanding of its risks and benefits. For commercial adopters, this may translate into higher gains and better bets, while for regulators, this is imperative for an informed policy stance that mitigates attendant AI risks.
AI is witnessing extensive adoption in the digital lending sector, where it is transforming business operations — from enhancing customer experiences through chat automation and personalisation of services to optimising credit decision-making using algorithms. Hand in hand, there are the regulators’ reminders to digital lenders to consider fairness, prevent biases and safeguard against other risks in algorithm-based decision-making. Moreover, with the Framework for Responsible and Ethical Enablement of Artificial Intelligence Committee [FREE-AI Committee] of the RBI underway, the responsible deployment of AI could gain further salience. This piece answers what Responsible AI is, what gains it may have for stakeholders and unpacks some of its key attributes.
Why Responsible AI?
Responsible AI, a concept presently being met with several deliberations, at its core is a set of desired attributes that ensure AI systems function as intended, uphold fair practices while ensuring the risk of unintended consequences is minimised. The adoption of Responsible AI ensures that AI-driven decisions and outcomes are trustworthy for those directly impacted by them. It is more than just a regulatory checkbox and has implications for overall customer protection as well as the stability of the broader financial system.
When AI-driven decisions are fair, respect privacy and are not opaque, they foster customer confidence and trust in them. For regulators, it is a toolkit to craft a supervisory approach that minimises exclusion and systemic harms that AI can pose. Perhaps the most compelling case is for the digital lenders themselves. Integrating Responsible AI right from the outset, not only mitigates potential risks for lenders, but also distinguishes them from others in the market. These lenders would witness improved customer trust, better brand reputation, and sustainable business growth rather than short-term gains. Thus, measures to protect and nurture this trust, may not just be the right thing to do but also the smart thing to do.
Key considerations
Though a composite of several important features, Responsible AI is synonymous with three key dimensions: fairness and non-discrimination, technological dependability and human agency — each essential to making the AI outcome trustworthy. Let’s unpack what these terms imply in the context of digital lending.
First, fairness and non-discrimination: One of the most pressing concerns in AI-driven lending is bias and discrimination. Algorithms trained on biased or misrepresented datasets have the potential to reinforce existing inequalities or create new ones. For example, women-owned businesses have historically faced higher loan rejection rates. A credit scoring model trained on this data may associate this demographic with higher risk and deny them loans, reinforcing bias against them.
To prevent such harms, AI systems should be designed to detect biases throughout their lifecycle. Regulatory bodies like the Monetary Authority of Singapore (MAS) emphasise that any disparities in treatment due to AI-driven decisions must be justifiable and that justification be subject to regular review. For digital lenders, this underscores the need to invest in bias detection mechanisms and bias measurement techniques. Ignoring the risks stemming from bias can lead to not only reputational damage but also legal consequences.
Second, beyond fairness, technological dependability is crucial for Responsible AI deployment. It emphasises an AI system’s ability to generate reliable outputs and withstand threats like data manipulation or malware attacks. For digital lenders, this means much more than just deploying AI into operations; it requires implements like performance monitoring against deviations, threat detection and mitigation strategies to prevent confidentiality breaches. Providers globally have already experienced sophisticated attacks to their AI-powered financial systems — from transactional data manipulation to exploited chatbot vulnerabilities. Deploying AI without these safeguards would be akin to a high-speed train without brakes; efficient but dangerously reckless.
Finally and, perhaps, most importantly, human agency must remain at the core of AI-driven lending. When algorithms are making decisions, it is natural for anxieties to arise especially when customers may have no way to fully understand nor question these decisions. In some cases, they may not even realise when a decision has been made by an AI system, let alone if it was unfair or erroneous.
AI-driven lending must not come at the expense of human autonomy. For digital lenders, this means ensuring that critical decisions such as credit underwriting are not left entirely to AI. A decision like this must have human oversight that remains accountable for it. Digital lenders must carefully assess the level of human intervention required commensurate to the risk of the AI use case. This warrants clarity in their protocols: when should a human underwriter step in? What level of override powers should they have? This is not only crucial to embed greater customer trust and stronger relationships but also has implications for the lender’s portfolio quality.
As AI reshapes the future of digital lending, the journey to do this responsibly has only just begun. As this industry gears up to deploy AI that is fair and dependable, first movers will not just set themselves apart — they will also align with the regulatory direction that the RBI is signalling. In an era where trust and transparency are more valuable than ever, Responsible AI is good business for all.
The writers are Research Associates in the Future of Finance Initiative at Dvara Research
