As India concluded the AI Impact Summit, it chose an unusually explicit framing for a technology often discussed in abstract terms with three key themes: People, Planet, and Progress. This was not accidental. They can be operationalised now. Artificial intelligence is already diffusing rapidly through financial systems, shaping how credit is allocated, risk priced, and public services delivered.
The question is no longer whether AI will transform finance, but whether that transformation can be steered toward inclusion, sustainability, and broadly shared growth. For India, financial technology offers the most immediate proving ground.
The country’s advantage lies less in frontier research than in its digital public infrastructure. Identity systems, instant payments, and consent-based data sharing have created interoperable rails at the population scale. UPI alone now processes more transactions than most global card networks. These systems have already lowered the cost of inclusion. The next step is embedding AI into them without eroding trust.
AI-driven finance promises to extend credit and insurance to those long excluded from formal systems. Indian fintech firms are already using transaction histories, GST flows, and supply chain data to underwrite small businesses and first-time borrowers. Properly deployed, such models can unlock working capital for kirana stores, small manufacturers, and women-led enterprises that traditional banks have struggled to serve.
But algorithmic inclusion carries risks. Automated decision-making can entrench bias, obscure accountability, and weaken consumer protection. Trust, once lost, is difficult to regain.
Singapore’s experience is instructive. Its National AI Strategy placed early emphasis on explainability, auditability, and human oversight. Financial institutions were encouraged to adopt human-in-the-loop systems, ensuring that AI supports judgment rather than replacing it. This slowed deployment marginally but improved public confidence and regulatory clarity.
India would do well to adopt similar principles at scale. Consent-based data use, explainable models, and clear grievance mechanisms are not regulatory luxuries. They are prerequisites for durable adoption, particularly in markets where financial mistakes have outsized social consequences.
Planet: Making Climate Finance Investable
AI’s second major test lies in climate finance. India faces mounting climate risks from erratic monsoons and heat stress to urban flooding that already affects agricultural incomes and credit quality. Yet capital for adaptation and transition remains scarce, fragmented, and poorly targeted.
AI can help close this gap. Machine learning models improve climate risk assessment, satellite imagery enables rapid loss estimation, and digital platforms reduce the cost of monitoring outcomes. India has begun experimenting. InsurTech firms now use remote sensing to speed up crop insurance payouts under public schemes. Some lenders are integrating climate exposure into agricultural and MSME credit decisions. Energy-focused fintechs are financing rooftop solar through pay-as-you-save models.
Singapore again offers a useful comparison. Ravi Menon, the city-state’s Climate Action Ambassador, has argued that the climate challenge is as much financial as technological. Singapore’s Financing Asia’s Transition Partnership FAST P uses public capital to crowd in private investment, often mobilising four dollars of private finance for every public dollar committed. Such blended finance structures rely heavily on data transparency and risk analytics, areas where AI plays a growing role.
Singapore is also developing high-integrity carbon markets, aiming to channel capital to decarbonisation projects in emerging economies. The emphasis has been on credibility and verification, responding to scepticism about carbon credits rather than ignoring it.
India’s opportunity is to combine similar financial structures with its own scale, using AI-enabled monitoring to make climate action measurable, and fintech platforms to make it investable across agriculture, infrastructure, and small enterprise finance.
Progress: Avoiding Concentration
The largest risk of AI is not failure but concentration. Productivity gains accrue unevenly, favouring firms with data, capital, and technical talent.
Here again, finance matters. AI can lower intermediation costs, expand access to savings and insurance, and improve the targeting of welfare programmes. Transaction data already allows governments to design more precise subsidies and benefits. Digital lenders can tailor products to women entrepreneurs and informal workers at a lower cost.
Yet progress also depends on skills. AI will reshape jobs in finance rather than eliminate them outright. Demand is rising for data scientists, compliance technologists, cyber risk specialists, and digital auditors. Singapore anticipated this early, aligning education, industry, and regulation to support workforce transition.
India’s scale makes this harder and more important. Fintech platforms can double as skilling engines, but only if training, certification, and industry demand are aligned.
From Principles To Practice
The lesson from Singapore is not about speed or size. It is about coherence. AI strategies succeed when regulation, market adoption, talent development, and climate policy reinforce each other.
India does not face a choice between innovation and responsibility. Through finance, it can test whether ethical AI can function at scale, serving people without eroding trust, supporting climate action without greenwashing, and distributing gains without excessive concentration.
Singapore's experience shows that responsible and inclusive AI is built through systems more than slogans. India now has the scale and the political will to test those systems under real pressure. If India succeeds, it will not just shape its own future. It will set the benchmark for how emerging economies can deploy AI with trust, credibility and impact.
Disclaimer: The views expressed in this article are those of the author and do not necessarily reflect the views of the publication. |
