An application of Blockchain for small size loans Bálint Molnár 2[0000000150158883] , Ayda Bransia 1[0009000171540536] , Galena Pisoni 4[00000001−−32661773] , and Simon Thompson 2,3[000000022350301X] 1 Doctoral School of Informatics, Faculty of Informatics,Eötvös Loránd University of Budapest, ELTE, Pázmány Péter sétány 1/C, 1117 Budapest, Hungary gdgcum@inf.elte.hu 2 Information Systems Department, Faculty of Informatics, Eötvös Loránd University of Budapest, ELTE, Pázmány Péter sétány 1/C, 1117 Budapest, Hungary molnarba@inf.elte.hu 3 University of Kent, Canterbury, Kent, CT2 7NZ, UK, thompson@inf.elte.hu/ s.j.thompson@kent.ac.uk 4 York Business School, York St John University, Lord Mayor’s Walk, YO31 7EX, York, UK g.pisoni@yorksj.ac.uk Abstract. This scientific research aims to revolutionise the conventional lending process by providing a thorough framework for the creation of a blockchain-based lending platform. The framework provides improved transparency, automation, and risk assessment capabilities by merging Marlowe smart contracts, data science analytics, and transparent trans- action recording on the Cardano blockchain. The article discusses and evaluates in detail the benefits, difficulties, and factors to be taken into account while putting such a framework into practice. In the conclu- sion, the suggested framework has the potential to expedite loan proce- dures, enhance decision-making, and promote confidence among financial ecosystem participants. Keywords: Blockchain · Marlowe · insurance. 1 Introduction Business process management can be seen as an organization’s core skill to man- age all business processes... [6, p.4]. Since the birth of the field, this branch of management science has tried to consider the "business process as a whole", rather than optimizing individual tasks, in an answer to the pressing needs of industry to deliver services the fastest possible and in the best way possible. As the branch developed, it started using more and more sophisticated math- ematical models to improve and analyze processes, including a wide range of problem-solving techniques, to improve decision-making performance. This research was supported the Thematic Excellence Programme TKP2021-NVA- 29 (National Challenges Subprogramme) funding scheme, and by the COST Action CA19130 - ”Fintech and Artificial Intelligence in Finance Towards a transparent financial industry” (FinAI)