As AI commoditizes, Subho Moulik, Founder & CEO of Appreciate explains why trust, explainability, and compliance will determine which Indian FinTechs thrive in the global financial ecosystem.
CIO&Leader: FinTechs are under constant pressure to scale while managing costs. Where do you see AI delivering the biggest business impact today, revenue growth, customer acquisition, or operational efficiency?
Subho Moulik: While everyone talks about revenue growth, the real impact in 2025 is operational efficiency.
The transformation is already visible across leading institutions. JPMorgan Chase’s COiN platform processes 12,000 contracts in seconds, saving 360,000 lawyer hours annually. Goldman Sachs has achieved a 40% reduction in M&A preparation time through AI copilots while deploying AI assistants to all 46,000 employees.
These aren’t isolated experiments they are scalable implementations proving AI’s operational impact across the financial ecosystem. In India’s hyper-competitive market with razor-thin margins, such cost reduction translates directly to sustainable competitive advantage. Revenue growth from AI requires perfect execution across complex customer journeys. Efficiency gains are immediate and measurable.
But here’s the critical insight: efficiency isn’t just about cost cutting it’s about speed and scale. AI is enabling loan processing to move from weeks to days , with document-heavy workflows like tax return parsing and credit file prioritization happening automatically. KYC processes that once took days now complete in hours.
The counterintuitive reality: companies chasing AI-driven revenue growth often fail because they’re building on inefficient foundations. Those focusing on operational efficiency first create the structural advantages that make revenue growth inevitable.
Smart Indian FinTechs are using efficiency gains to fund customer acquisition at scale, creating a virtuous cycle that pure revenue-focused AI strategies can’t match.
CIO&Leader: Do you believe responsible AI will become as important a differentiator as pricing or product innovation in the next three years?
Subho Moulik: Absolutely. Responsible AI is rapidly becoming a strategic differentiator and market/reputational risk-mitigator.
72% of companies now use AI technology, up from just 50% six years ago. Institutions that fail to implement responsible AI frameworks risk falling behind as it becomes a competitive differentiator, not just a regulatory requirement.
In India’s context, where 87% FinTech adoption rate leads globally, responsible AI is essential for maintaining customer trust. The EU AI Act requires mandatory risk assessments for financial institutions an example of the growing regulatory momentum globally. In India, regulators are pushing for transparency and bias testing in AI-driven decisions. Financial institutions must embed fairness, transparency, and accountability into AI-driven decision-making.
In three years, basic AI functionality will be commoditized. Every FinTech will have decent algorithms. The differentiation will come from trust, auditability, and regulatory resilience.
While price and product may get FinTechs noticed, only responsible AI earns customer trust and regulatory approval making it an inescapable competitive lever.
CIO&Leader: Fraudsters are also leveraging AI to launch more sophisticated attacks. How should FinTechs rethink their fraud detection and risk management strategies in this new landscape?
Subho Moulik: The threat landscape has fundamentally changed, and FinTechs must respond with AI-powered defense strategies that match the sophistication of modern attacks.
Generative AI could push U.S. fraud losses from $12 billion in 2023 to $40 billion by 2027. More than 50% of fraud now involves artificial intelligence, while AI-generated emails make up 40% of business email compromise attacks.
However, while fraudsters use AI for attacks, 91% of US banks now use AI for defense. AI-powered fraud detection can spot hidden links in under 50 milliseconds and boost detection rates by up to 300%.
The evolution from static rules to self-learning engines is critical. Legacy rule-based systems built for a slower world can’t keep up with today’s fast, varied fraud. The winners will be platforms that build fraud detection as core infrastructure, not bolt-on security. Every transaction, every user interaction becomes a data point for continuous learning systems.
Leading US FinTechs demonstrate this evolution in practice. Stripe’s AI-powered Radar processes billions of transactions with machine learning algorithms trained on global payment data, helping merchants reduce chargebacks by up to 50%. Square’s Risk Manager uses sophisticated machine learning to automatically identify fraud patterns and suspicious payments in real-time, protecting businesses of all sizes.
These platforms showcase how AI transforms from defensive technology into competitive differentiation enabling faster, safer payments that drive customer trust and business growth. FinTechs must adopt adaptive, continuously trained AI models paired with human analysts to spot novel fraud patterns. The fraud stack has evolved from a static checklist into a living system, and only AI-native approaches can match this new reality.
CIO&Leader: Beyond fraud, what role can AI play in improving regulatory compliance and reducing systemic risk in financial ecosystems?
Subho Moulik: AI is transforming from a regulatory challenge into a compliance enabler, offering unprecedented capabilities for risk management and regulatory adherence. Globally, financial services firms spent $35 billion on AI in 2023, with projected investments reaching $97 billion by 2027. This massive investment is driven partly by AI’s ability to enhance risk management, compliance, and security through constant monitoring and real-time analysis.
Systemic risk reduction comes through AI’s ability to process vast amounts of data to identify trends, assess risks, and ensure better information for future planning. This enhanced risk management capability, combined with real-time monitoring, helps prevent small issues from becoming systemic problems.
Platforms with superior compliance infrastructure attract partners, vendors, and customers who want to minimize their own regulatory exposure. Compliance excellence becomes a business development tool and the network effects are powerful. Indian FinTechs that master regulatory AI will expand globally more easily. The skills and systems developed for India’s complex regulatory environment create transferable advantages in other emerging markets with similar complexity. Smart money builds compliance AI as platform infrastructure, not departmental tooling.
CIO&Leader: In an industry built on trust, how can FinTech’s ensure that AI-driven decisions remain explainable and fair, especially in credit scoring or loan approvals?
Subho Moulik: Most discussions about explainable AI assume traditional credit scoring is inherently fair and transparent. It’s not. Traditional systems embed decades of biases in seemingly objective criteria. The housing loan officer who denies applications based on pin codes isn’t more explainable than an AI just more familiar.
The challenge specific to India is to speed up the generational goal of widespread financial inclusion. To that end AI-driven decision-making will bring efficiencies by an order of magnitude, especially in credit scoring and loan approvals for the large underserved Indian population without access to credit. AI can assess creditworthiness for previously “uncreditworthy” individuals using alternative data like mobile usage patterns and transaction history.
However, explainability isn’t optional; it’s the foundation of trust in AI-driven financial decisions, and the technology exists today to achieve both accuracy and transparency. The strategic framework requires three elements: robust model documentation, regular bias testing, and human oversight for complex cases. Institutions must embed explainability from the start of AI development, not as an afterthought.
CIO&Leader: AI is often touted as a driver of personalization but how can it be used to advance financial inclusion, especially in emerging markets where millions remain underbanked?
Subho Moulik: AI is revolutionizing financial inclusion by enabling institutions to serve previously excluded populations at scale and with precision impossible through traditional methods.
The opportunity is massive 1.4 billion adults globally remain unbanked, concentrated in emerging markets. In India, despite leading FinTech adoption at 87%, significant populations still lack access to formal financial services.
AI’s inclusion impact comes through alternative credit assessment. Traditional credit scoring fails for thin-file borrowers, but AI can analyze mobile usage patterns, transaction history from digital payments, and even agricultural data for farmers. This enables creditworthiness evaluation for populations previously considered “uncreditworthy.”
The democratization extends beyond basic banking. Investment platforms like Appreciate now use AI to provide algorithmic insights and portfolio recommendations that were once exclusive to institutional investors. Tools like Trading Signals by Appreciate exemplify this trend delivering sophisticated market analysis and investment recommendations that previously required teams of analysts, now accessible to any Indian investor through their mobile device.
Voice AI banking assistants overcome literacy barriers, supporting many regional languages in India alone. Mobile-first platforms leverage widespread smartphone adoption, while offline capabilities serve areas with limited internet connectivity. When financial services become as intuitive as social media, inclusion follows automatically. This cost efficiency, combined with expanded market reach, creates win-win scenarios for both institutions and underserved customers.
CIO&Leader: How do you strike the right balance between personalized financial services and data privacy concerns for consumers?
Subho Moulik: The personalization v/s privacy paradox is real, but the solution lies in privacy-preserving AI technologies that deliver customization without compromising data security. Consumer expectations create the tension: 84% of banking customers would switch for AI-powered personalized insights, while growing privacy awareness creates resistance to data sharing. This isn’t a choice between personalization or privacy, it’s about achieving both through technology innovation.
Privacy-first personalization strategies are emerging. Zero-party data collection allows voluntary customer preference sharing. Federated learning enables on-device AI processing without centralized data storage. Differential privacy provides mathematical guarantees of individual privacy protection while enabling insights .
The regulatory framework is tightening, as GDPR data minimization principles limit collection to essential data only. India’s evolving data protection laws require transparent opt-in/opt-out mechanisms. Regional data residency requirements affect AI model deployment strategies . Here are some of the trust-building mechanisms that are essential: explainable personalization showing why recommendations are made, user control dashboards for real-time data management, transparency reports on data practices, and demonstrable commitment to ethical AI frameworks . Privacy isn’t a constraint on personalization, it’s a forcing function for better AI.
CIO&Leader: There’s a growing debate about whether AI will replace human roles in financial services. What does an AI “co-pilot” model look like in FinTech, and how can leaders prepare their workforce for this shift?
Subho Moulik: The future isn’t AI replacing humans, it’s AI amplifying human capabilities, and the organizations that master this collaboration will dominate the next decade of financial services.
AI adoption has surged to 72% of companies, up from 50% six years ago. In financial services, 20% average productivity gains are being achieved across AI implementations . The key insight: early movers are seeing AI transform their operations while creating new value-generating roles.
The co-pilot model in practice looks like AI handling data processing while humans provide strategic insight. In investment management, AI performs portfolio analysis with human oversight for client recommendations. In lending, automated initial screening is combined with human review for complex cases. Customer service integrates AI-generated insights with human relationship management .
Goldman Sachs, for example, has deployed AI assistants to all 46,000+ employees, with their Banker Copilot reducing M&A deal preparation time by 40% and developer productivity gains reaching 55% for certain tasks. This isn’t AI replacing humans. It’s AI amplifying human expertise at unprecedented scale.
Skill development requirements are evolving rapidly.
New competencies include AI literacy, data interpretation, human-AI interaction design, and ethical AI governance. Emerging job roles include AI risk managers, human-AI interaction specialists, AI ethics officers, and AI Leaders must invest in workforce transformation now the institutions that successfully integrate human-AI teams will capture the productivity gains that define the next era of financial services.