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Waiting three to five weeks for a loan approval feels antiquated in an era of instant digital services. Yet this is the reality for ING customers, a striking contradiction for a bank that pioneered digital banking in Europe (ING Groep N.V., 2024). With a €164.3 billion lending portfolio and advanced digital infrastructure, ING provides an ideal case for showing how Generative AI can resolve this paradox and help traditional banks defend against fintech disruption.

The ING Innovation Paradox

ING serves 8 million customers, 84% of whom prefer mobile banking, and invests €324 million annually in software (ING Groep N.V., 2024). Yet its loan approvals remain constrained by manual processes consuming 8–10 hours per application. Traditional workflows still require three to five weeks for decisions (McKinsey & Company, 2018).

Meanwhile, fintech competitors leverage automation for near-instant approvals. In the Netherlands, fintech lending doubled to €2.3 billion in just two years (De Nederlandsche Bank, 2024), threatening ING’s market position as customers who experience poor digital service rarely return (Accenture, 2025).

The GenAI Solution

The proposed solution applies Generative AI to analyze unstructured financial documents while maintaining explainability and regulatory compliance. Using natural language processing (NLP), GenAI interprets payslips, invoices, and income records to generate standardized, transparent risk assessments (Al-Hchemi, 2024).

The three-stage process includes secure data ingestion, AI-driven document classification, and generative summarization that produces explainable risk profiles. Critically, humans remain in control, AI accelerates analysis without replacing judgment, ensuring full compliance with GDPR and the EU AI Act (European Parliament, 2023).

Transformative Impact

Processing time drops by 85%, from several weeks to just three days. Per-loan costs decrease 78%, from €450 to €100, as automation reduces manual work from eight hours to fifteen minutes (McKinsey & Company, 2023; Deloitte, 2019). Over three years, this transformation generates an estimated €700 million in cumulative revenue and €10.5–12.6 million in annual cost savings.

Beyond efficiency, AI-driven credit scoring enables 15–20% more approvals by fairly assessing self-employed individuals and those with limited credit histories (Fares et al., 2023). This expansion enhances financial inclusion while profitably serving previously underserved segments.

The model also demonstrates the economics of information goods (Shapiro & Varian, 1998): an upfront investment of €18–23 million yields dramatically lower marginal costs over time, creating sustainable competitive advantage through operational learning and scale.

Strategic Defense

A Business Model Canvas analysis (Osterwalder & Pigneur, 2010) reveals deep transformation across ING’s value structure, from automated key activities and AI partnerships to improved customer relationships, expanded target segments, and scalable cost economics.

Implementation challenges remain. Successful adoption requires robust change management (Gutzwiller et al., 2024), enhanced cybersecurity (FINMA, 2023), continuous bias audits, and strict adherence to evolving regulatory standards (Bank for International Settlements, 2021). Yet these challenges are outweighed by the strategic imperative to lead the AI transformation rather than react to it.

Conclusion

Integrating Generative AI is both technically feasible and strategically essential, fully aligned with ING’s Think Forward strategy. By embedding AI while retaining human oversight (Vial, 2019), ING can transform a core operational weakness into a durable competitive advantage.

The question is no longer whether AI will transform lending, it already is, but whether traditional banks will lead the transformation or be disrupted by it. ING possesses both the capability and the strategic imperative to choose leadership.

Team 22: Elias Stad 715336, Helen Hu 537226, Mihai Tulbure 765023, Vinita Ohm 787879

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References

Accenture. (2025). Global banking consumer study 2025: Banking advocacy powering growth. https://www.accenture.com/us-en/insights/banking/consumer-study-banking-advocacy-powering-growth

Al-Hchemi, L. H. (2024). Evaluating Generative AI in enhancing banking services efficiency. Економічний Форум, 47–54. https://doi.org/10.62763/ef/4.2024.47

Bank for International Settlements. (2021). Artificial intelligence and machine learning in financial services. Basel Committee on Banking Supervision. https://www.bis.org/bcbs/publ/d537.pdf

De Nederlandsche Bank. (2024). Annual report 2024. https://www.dnb.nl/en/publications/publications-dnb/annual-report/annual-report-2024/

Deloitte. (2019). AI leaders in financial services: Common traits of frontrunners in the artificial intelligence race. https://www2.deloitte.com/us/en/insights/industry/financial-services/artificial-intelligence-ai-financial-services-frontrunners.html

European Parliament. (2023). EU AI Act: First regulation on artificial intelligence. https://www.europarl.europa.eu/topics/en/article/20230601STO93804/eu-ai-act-first-regulation-on-artificial-intelligence

Fares, O. H., Butt, I., & Lee, S. H. M. (2023). Utilization of artificial intelligence in the banking sector: A systematic literature review. Journal of Financial Services Marketing, 28(4), 835–852. https://doi.org/10.1057/s41264-022-00176-7

FINMA. (2023). FINMA Risk Monitor 2023. https://www.finma.ch/en/~/media/finma/dokumente/dokumentencenter/myfinma/finma-publikationen/risikomonitor/20231109-finma-risikomonitor-2023.pdf

Gutzwiller, E., Moutiq, N., & Pearce, M. (2024, June 14). Ensuring a successful transformation through non-IT change management. Deloitte. https://www.deloitte.com/ch/en/Industries/financial-services/blogs/navigating-tech-enabled-transformation-of-core-banking-processes-part-3.html

ING Groep N.V. (2024). Annual report 2024. https://www.ing.com/Investors/Investors/Financial-performance/Annual-reports.htm

McKinsey & Company. (2018). The lending revolution: How digital credit is changing banks from the inside. https://www.mckinsey.com/capabilities/risk-and-resilience/our-insights/the-lending-revolution-how-digital-credit-is-changing-banks-from-the-inside

McKinsey & Company. (2023). The state of AI in 2023: Generative AI’s breakout year. https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai-in-2023-generative-ais-breakout-year

Osterwalder, A., & Pigneur, Y. (2010). Business model generation: A handbook for visionaries, game changers, and challengers. John Wiley & Sons.

Shapiro, C., & Varian, H. R. (1998). Information rules: A strategic guide to the network economy. Harvard Business School Press.

Vial, G. (2019). Understanding digital transformation: A review and a research agenda. The Journal of Strategic Information Systems, 28(2), 118–144. https://doi.org/10.1016/j.jsis.2019.01.003

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When we talk about generative AI, most people think of text generation or image creation. Over the past weeks, I explored a different side: how these tools can lower barriers to software development. I experimented with Lovable, a platform that helps build web apps, to create a landing page and a sales page for a product that generates automatic outreach messages for LinkedIn ICPs.

Interestingly, my process began not in Lovable but in ChatGPT, since it was cheaper to draft the right prompts there before using Lovable’s more limited credits. In the backend, I connected Lovable to n8n workflows, which allowed customer inputs to trigger an AI agent that scanned LinkedIn profiles and generated personalized outreach messages.

Working with these tools came with clear advantages. Lovable felt intuitive and easy to use, even for someone without a coding background. The integration with n8n also worked more smoothly than expected, making it possible to put together something functional in a short time. It really gave me the sense that prototyping no longer needs to be reserved for professional developers.

At the same time, the difficulties were hard to miss. Setting up n8n workflows required understanding nodes and connections, which was a challenge without coding experience. The language model in Lovable also felt weaker compared to ChatGPT, and the credits were costly. Another drawback was latency: generating a personalized outreach message took up to 40 seconds, which would likely frustrate end users.

Reflecting on this, I see both the promise and the limits. On the one hand, tools like Lovable make it possible for almost anyone to create digital products. On the other, they reveal how important it still is to understand the underlying complexity. It makes me wonder: if building apps becomes accessible to everyone, will the role of professional developers fade, or will they remain crucial as the architects behind these ecosystems?

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Projects like the CitiVerse initiative in Rotterdam, Flanders, and Tampere (CitiVerse, n.d.) show how cities are no longer only physical infrastructures but also evolving into digital platforms. By combining extended reality with urban data, these pilots aim to expand citizen participation and make urban management more transparent and efficient.

Other cities provide interesting comparisons. Barcelona’s Sentilo uses an open-source system to manage sensor data for traffic, energy, and water while ensuring citizen data ownership (Bakıcı et al., 2013). Virtual Singapore applies a comprehensive digital twin that integrates environmental and social data for simulations (Chourabi et al., 2012). Seoul’s Metaverse Seoul offers municipal services in a virtual environment where citizens interact with civil servants as avatars (Seoul Metropolitan Government, 2023).

Academic debates emphasize both promise and caution. Qanazi et al. (2025) argue that most digital twins overlook the social dimensions of urban life and propose “social digital twins” to capture citizen attitudes and interactions more effectively, as seen in Virtual Singapore (Chourabi et al., 2012). Adade et al. (2023) show that digital twins can enhance citizen participation in land-use planning, but only when designed with accessibility, interactivity, and privacy safeguards. Ntanda and Carolissen (2025) highlight how smart city technologies risk reinforcing inequalities if access and digital literacy are not prioritized. Together, these studies underline that the real challenge is not the technology itself, but whether it is deployed inclusively.

The advantages of city-wide platforms are clear. They break down silos between departments, enable real-time responses, and test infrastructure or climate policies virtually before costly rollout, as used in Singapore’s digital twin approach (Chourabi et al., 2012). They also create new ways for citizens to engage with policy-making by visualizing and debating urban changes in accessible formats.

Yet risks remain. Unequal access could deepen exclusion, and if platforms are driven mainly by commercial incentives, public value may be sidelined.

So the key question is: Should smart city platforms like the CitiVerse be developed as public utilities, guaranteeing universal access, or as competitive marketplaces, driving innovation but risking exclusion?

References

Adade, D., de Vries, W. T., Weidner, S., & Kuffer, M. (2023). Digital twin for active stakeholder participation in land-use planning. Land, 12(3), 538. https://doi.org/10.3390/land12030538

Bakıcı, T., Almirall, E., & Wareham, J. (2013). A smart city initiative: The case of Barcelona. Journal of the Knowledge Economy, 4(2), 135–148. https://doi.org/10.1007/s13132-012-0084-9

Chourabi, H., Nam, T., Walker, S., Gil-Garcia, J. R., Mellouli, S., Nahon, K., Pardo, T. A., & Scholl, H. J. (2012). Understanding smart cities: An integrative framework. In 2012 45th Hawaii International Conference on System Sciences (pp. 2289–2297). IEEE. https://doi.org/10.1109/HICSS.2012.615

CitiVerse. (n.d.). CitiVerse project: Expanding citizen participation through urban digital twins. Retrieved September 20, 2025, from https://xcitecitiverse.eu/

Ntanda, A., & Carolissen, R. (2025). Technology’s dual role in smart cities and social equality: A systematic literature review. Journal of Local Government Research and Innovation, 6(0), a238. https://doi.org/10.4102/jolgri.v6i0.238

Qanazi, S., Leclerc, E., & Bosredon, P. (2025). Integrating social dimensions into urban digital twins: A review and proposed framework for social digital twins. Smart Cities, 8(1), 23. https://doi.org/10.3390/smartcities8010023

Seoul Metropolitan Government. (2023, January 16). Official release of Metaverse Seoul. https://english.seoul.go.kr/official-release-of-metaverse-seoul/