Ahmad Salahuddin

Nepal's banking sector is at an inflection point — and AI is the catalyst. From Darchula to Taplejung, every district deserves financial services that are fast, secure, and intelligent. Yet for too long, geography, language, and legacy infrastructure have kept millions out of the formal banking system. That changes now. This carousel breaks down 5 real AI use cases — NPL early warning, deepfake fraud detection, Nepali-language assistants, remittance-based credit profiling, and cyber resilience — all grounded in Nepal's regulatory reality and NRB's evolving AI framework. The infrastructure is being built. The data is here. The talent is here. The next fintech success story doesn't have to come from Silicon Valley. It can come from Kathmandu. What use case do you think moves the needle first for Nepal's banks? Drop your thoughts below 👇 #NepalBanking #AIinFinance #Fintech #DigitalNepal #ArtificialIntelligence #BankingInnovation #FinancialInclusion #NepaliStartups #Remittance #CyberSecurity #NLP #FraudDetection #CreditRisk #SovereignAI #FutureOfBanking #Nepal

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If I were building in tech in Nepal, I wouldn’t rush into AI just because the world is hyped about it. The real needs are still very fundamental: Functional, reliable websites Online ordering and payment systems Removing operational bottlenecks for companies Helping small businesses go digital Expanding platforms like DoorDash-style logistics for local contexts Building fraud detection and trust systems where it actually matters Tech should follow the problem, not the trend. We don’t need to copy what Silicon Valley is building. We need to customize solutions to local realities, understand ground-level pain points, and solve what’s immediately broken. Less obsession with shiny tech. More focus on real problems, real users, real impact. That’s how ecosystems actually grow. #TechForGood #NepalTech #ProblemFirst #BuildWhatMatters #Startups #DigitalTransformation

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Is Nepal heading toward digital sovereignty, or are we destined to become a "data colony"? In my latest article for the Nepal Economic Forum, I move beyond the AI hype to examine the structural hurdles we face as a nation. We have the "crude oil" of data, but we lack the refineries to make it useful. I discuss three major barriers: • The Tokenization Tax: Why it costs Nepali developers 3x more to build an app than an American developer. • The "Hollow Middle": Why our senior engineering talent leaves just as they become most valuable. • Green Compute: Why we should use our surplus hydropower to run local data centers instead of just exporting electricity. The technology is global, but for it to work for us, the solutions must be local. Read the full report here: 📖 Thuprai: https://lnkd.in/gcKJKykK A big thank you to the members of the Nepal Economic Forum for publishing this piece and fostering this critical dialogue.

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DATA SCIENTIST INTERVIEW QUESTION A nationwide survey of 1,000 randomly selected Sri Lankans, reports its findings, with a ±3% margin of error at the 95% confidence level. What factors, beyond the sai sampling error, could cause the survey results to be inaccurate, unreliable, or unrepresentative of the true views or characteristics of the entire population of Sri Lanka? #SriLanka

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Non Nepali audience may use AI to translate in your language 😀 The race to the Data Universe has begun. For Nepal’s BFI sector, data monetization and AI are no longer optional — they are becoming the foundation of competitiveness, innovation, and new revenue models. In my recent #Bizmandu article, I highlight why BFIs must start treating data as a strategic asset, invest in strong data governance, develop talent, and build an enabling ecosystem for AI adoption. The institutions that move early will unlock new value; those that delay risk being disrupted. The journey to a data-driven financial sector starts now. www.kathmandufintek.com.np #DataMonetization #AIinBanking #DigitalBanking #NepalBFI #FintechNepal #DigitalEconomy #AITransformation #FutureOfBanking #InnovationLeadership #NepalFintech Non Nepali audience may use AI to translate in your language 😀 https://lnkd.in/guB_dF8Z

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🚀 Breakthrough in Sinhala AI! Researchers at the University of Moratuwa have developed Sri Lanka’s first large-scale Sinhala-only Large Language Model (LLM), marking a milestone in advancing local language computing. 🔹 The model, named “SinLlama”, was built by continually pre-training Llama-3-8B with nearly 10 million Sinhala sentences. 🔹 SinLlama is the largest Sinhala LLM to date and has already outperformed Llama-3-8B on Sinhala text classification benchmarks. 🔹 Both the model and the dataset are freely available for researchers and innovators. 👨‍💻 Research Team H.W.K. Aravinda, Rashad Sirajudeen, Samith Karunathilake, Nisansa de Silva, Rishemjit Kaur, Surangika Ranathunga The project was led by the Department of Computer Science & Engineering, University of Moratuwa, with the aim of ensuring that local languages thrive in the AI era. 📂 10M Dataset: lnkd.in/gi43HaXg ℹ️ More Info: lnkd.in/gc3A4jUt #AI #Sinhala #LLM #SinLlama #SriLanka #ArtificialIntelligence #LanguageTechnology #Innovation #UniversityofMoratuwa #Research #OpenSource #AIForGood

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Google Pay has officially launched in Bangladesh, and City Bank has proudly played a key role in making this a reality. As part of City Bank’s commitment to continuous innovation and digital transformation, our Information Technology (IT) team has successfully completed the integration of Google Pay within the banking ecosystem. This milestone reflects our ongoing efforts to enable world-class fintech experiences for our customers, aligning with global standards and embracing the power of open banking.

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To the CEOs and senior decision-makers in Nepal’s banking industry. AI buzz is everywhere, models, GPUs, data scientists, RL experts, agentic systems. It's hard to not get swept up by all the talks. Yet leapfrogging to AI without the right foundations is like shopping for a Mercedes even before the road reaches your town. Here’s a quick way to test your organization’s readiness. No consultants required: Ask your tech team to segment your mobile or internet banking users into 4 buckets: - Active: Used the app in the last 30 days - Warming: Last activity 31–90 days ago - Dormant: Over 90 days ago - Inactive: Never engaged or lapsed users If it takes them more than 2 hours, your data infrastructure needs work before AI can create real value. Let's get back to the basics. If they do come back with an accurate report, yes let's move a step forward.

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