@CONFERENCE{MTMT:36931649,
	title = {Agentic AI in Finance Operations: Causal Effects on Discount Capture and Audit Delivery},
	url = {https://m2.mtmt.hu/api/publication/36931649},
	author = {Nagy, Zsombor and Dobák, Dóra Éva},
	booktitle = {Absztraktkötet - XXI. OGIK Országos Gazdaságinformatikai Konferencia „Adatvezérelt döntéshozatal és MI az üzleti gyakorlatban”},
	unique-id = {36931649},
	abstract = {Agentic large language model (LLM) workflows are entering finance back offices, yet causal evidence on their operational impact remains scarce. We evaluate two agentic workflows—(i) an AP agent that detects reverse-factoring eligibility, prioritizes invoices, and orchestrates outreach; and (ii) an audit-export agent that shards and validates large database extracts under file-size limits. Using a stepped-wedge rollout across AP pods and audit teams, we estimate effects with a difference-in-differences design that compares treated and not-yet-treated units over time while controlling for supplier mix, period-end effects, weekday, and seasonality. The primary outcome for AP is realized early-payment discount per € of eligible spend; secondary outcomes include cycle time from eligibility to action, manual-touch rate, exception/rework incidents, compliance deviations, and team Net Promoter Score (NPS). For audit support, the primary outcome is export completeness and time-to-delivery; secondary outcomes include re-run rate, checksum mismatches, and analyst time saved. We pre-specify heterogeneity by invoice complexity (lines, attachments), supplier risk, demand peaks (month/quarter-end), and team tenure. To test mechanisms, we analyze agent trace logs (plan length, tool choices, retries), human-in-the-loop (HITL) rates, and policy-rule violations to distinguish gains from earlier eligibility detection vs. faster routing and fewer errors. The study provides practice-oriented effect sizes that translate into incremental discounts captured, labor minutes saved, and avoided exceptions—offering managers a transferable design, metric framework, and analysis plan for agentic automation in finance operations.},
	keywords = {accounts payable; Reverse factoring; finance and operations; Language Large models; agentic AI},
	year = {2026},
	pages = {44}
}

@inproceedings{MTMT:36253764,
	title = {On-Prem vs Cloud LLMS For GDPR-Compliant Customer-Service Chatbots in the Hotel Industry},
	url = {https://m2.mtmt.hu/api/publication/36253764},
	author = {Nagy, Zsombor and Szabó, László},
	booktitle = {15th International Symposium Engineering Management and Competitiveness 2025 ( EMC 2025)},
	unique-id = {36253764},
	abstract = {Mid-size hotels in Central Europe are increasingly adopting AI chatbots to enhance customer service. They must,
		however, balance innovation with strict GDPR requirements.This study compares a cloud-based large language
		model (OpenAI’s GPT-4 API) against an on-premises deployment of an open-source LLM (Llama 3–13B) for
		hotel customer-service chatbots. We evaluate both solutions in the context of Cogniforce Labs deploying
		chatbots for regional hotels, focusing on GDPR compliance, operational cost, response latency, and customer
		satisfaction. Using three common use cases (booking modification, late check-in, and local recommendations),
		we simulate chatbot interactions and measure performance. The results show that the on-premises LLM offers
		superior data privacy (all guest data remains in-house, aiding GDPR compliance) and lower latency (up to ~35%
		faster responses), along with a more predictable cost structure. The cloud GPT-4 solution, however, delivers
		slightly higher answer quality, yielding greater customer satisfaction scores, at the expense of transmitting
		personal data to a third-party and incurring usage-based fees. Our findings suggest a trade-off between
		compliance/cost and service quality. Hotels prioritizing privacy may favor on-premise LLMs, while those
		emphasizing customer experience might opt for cloud AI with proper safeguards. We discuss hybrid strategies
		and provide recommendations for hospitality businesses navigating this choice.},
	keywords = {GDPR; Hospitality; chatbots; Cloud AI; Language Large models; On-Premises AI},
	year = {2025},
	pages = {16-21},
	orcid-numbers = {Szabó, László/0000-0003-4153-8432}
}

@misc{MTMT:36892546,
	title = {Agentic AI in Finance Operations. Causal Effects on Discount Capture and Audit Delivery},
	url = {https://m2.mtmt.hu/api/publication/36892546},
	author = {Nagy, Zsombor and Dobák, Dóra Éva},
	unique-id = {36892546},
	year = {2025}
}

@article{MTMT:34571730,
	title = {Folyamatautomatizációs trendek Magyarországon – mire kell felkészülnie egy cégvezetőnek?},
	url = {https://m2.mtmt.hu/api/publication/34571730},
	author = {Nagy, Zsombor and Szabó, Károly and Kováts, Dániel},
	doi = {10.14267/GIKOF.2024.01},
	journal-iso = {GIKOF JOURNAL},
	journal = {GIKOF JOURNAL: A NEUMANN JÁNOS SZÁMÍTÓGÉP-TUDOMÁNYI TÁRSASÁG GAZDASÁGINFORMATIKAI KUTATÁSI ÉS OKTATÁSI FÓRUM SZAKMAI SZERVEZET SZAKFOLYÓIRATA},
	volume = {2024},
	unique-id = {34571730},
	issn = {1588-9130},
	abstract = {Az utóbbi éveket úgy írhatjuk le talán a legjobban, mint egy technológiailag turbulens időszak kezdete. A vállalkozások mindennapi életébe olyan fejlesztések jelentek meg, mint a mesterséges intelligencia, az RPA vagy a különféle drónos technológiák. Az Ipar 4.0 egyértelműen átformálja a korábbi évtizedek vállalati működését, a következményekről pedig jelenleg csak sejtéseink lehetnek. A fent említett eszközök közül az egy legdinamikusabban terjedő megoldás az RPA, azaz a szoftverrobotok használata. Ennek megfelelően jelen kutatás ennek a technológiának a hatásait fogja mélyebben feltérképzeni. A tanulmány elsőként a szakirodalom-kutatás segítségével mutatja be a hazai és nemzetközi trendeket, valamint a korábbi kutatások eredményeit. Ezt követően kvalitatív eszközök segítségével vizsgáljuk meg a hazai RPA használat jelenlegi állapotát, illetve a jövőben várható hatásokat. Ezen belül főleg arra vagyunk kíváncsiak, hogy milyen előnyei és hátrányai vannak az RPA használatnak, valamint milyen hatással lehet a foglalkoztatottság alakulására a technológia alkalmazása. Valós veszély lehet-e, hogy bizonyos munkafolyamatok vagy teljes munkakörök alkalmazásával a vállalkozás olyan mértékű élő munkaerőt vált ki, amely tényleges leépítéshez vezethet? A fent megfogalmazott kérdéseket hazai környezetben vizsgáljuk, de a téma kiemelt aktualitása miatt a jövőben további kutatásokat szeretnénk lefolytatni nemzetközi szinten is.},
	keywords = {Automatizálás; RPA; Ipar 4.0; Financial technology (FinTech)},
	year = {2024},
	eissn = {1589-1348},
	pages = {1-10}
}

@{MTMT:34258727,
	title = {Digital Transformation. The Gaining of RPA in the Financial Sector},
	url = {https://m2.mtmt.hu/api/publication/34258727},
	author = {Nagy, Zsombor and Szabó, Károly and Kováts, Dániel and Szabó, László},
	booktitle = {2023 14th IEEE International Conference on Cognitive Infocommunications (CogInfoCom)},
	doi = {10.1109/CogInfoCom59411.2023.10397484},
	unique-id = {34258727},
	abstract = {In actual days business offices are also affected by the evolution of technology, demanding an upgrade to Industry 4.0. One of the most popular solutions that corporations are implementing is RPA, and this study will represent how this technology is present in business daily operations. With the help of action research methodology, we mainly want to determine that what can be the effects the application of RPA technology in the financial sector.},
	year = {2023},
	pages = {91-92},
	orcid-numbers = {Szabó, László/0000-0003-4153-8432}
}