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LOREM IPSUM DOLOR
ABOUT 110 Laude
Spin-off of the University of Molise Unimol 110 Laude is an innovative start up in the fintech sector based in Campobasso, Molise The idea, born in 2020 took shape with the establishment in July 2021 at the end of the university authorization process.
Our main goal is to grant the most appropriate financial instruments for MSMEs, ensure control over the
relevant KPIs and avoid crisis or even default scenarios Our AI powered decision support system ( allows MSMEs (and banks indirectly) to avoid waste of time, reduce the gap between the credit demand and
supply, and mitigate the inefficient allocation of money Overall, the impact of our innovation contributes to reducing the information asymmetry between borrowers and lenders, and, therefore, to fostering the financial inclusion of MSMEs, especially the unbanked and underbanked ones
We are working to make our technological solutions available to increase the strategic financial autonomy of MSMEs by simplifying daily financial management We do this in four steps
Where MSMEs get access to their credit scores and reports with daily updates from our DSS
How We use our data to analyze MSMEs’ credit profiles and make recommendations that could help
When We continuously provide forward looking credit ratings under different scenarios
Why Thanks to our solutions MSMEs save time and excessive amounts of money and receive support
before decisions are made
Research LabResearch Lab
R&SR&S
The innovative elements that characterise 110 Laude’s activities concern both product/service and process aspects. These derive from more than twenty-year research activities, carried out on a
personal and team basis in prestigious national and international universities and research centers. The research interests mainly concern the management of the investment and financing
techniques with a focus on the relationship between banks and firms, particularly micro small and medium-sized (MSMEs), in the light of the introduction of new assessment models credit (i.e.
internal rating systems). Our laboratory aims to validate the use of new approach in predicting the probability of default of MSMEs. 110 Laude integrates existing credit risk predictive models, which
mainly use accounting reports, with multiple hard and soft information through the use of machine and deep learning techniques.The innovative elements that characterise 110 Laude’s activities concern both product/service and process aspects. These derive from more than twenty-year research activities, carried out on a
personal and team basis in prestigious national and international universities and research centers. The research interests mainly concern the management of the investment and financing
techniques with a focus on the relationship between banks and firms, particularly micro small and medium-sized (MSMEs), in the light of the introduction of new assessment models credit (i.e.
internal rating systems). Our laboratory aims to validate the use of new approach in predicting the probability of default of MSMEs. 110 Laude integrates existing credit risk predictive models, which
mainly use accounting reports, with multiple hard and soft information through the use of machine and deep learning techniques.
More recent PublicationsMore recent Publications
Research Lab
Modina M., Zedda V. (in press). “Diagnosing default syndromes: Early symptoms of entrepreneurial venture insolvency”, Journal of Small Business and Enterprise Development, JSBED-02-2022-
0088.R1
Gallucci C., Santulli R., Modina M., Formisano V. (2022). “Financial ratios, corporate governance and bank-firm information: A Bayesian approach to predict SMEs’ default”, Journal of Management and Governance, ISSN 1385-3457, DOI: https://doi.org/10.1007/s10997-021-09614-5
Alessandro Bitetto, Paola Cerchiello, Charilaos Mertzanis. (2021). “A data‐driven approach to measuring epidemiological susceptibility risk around the world”, Scientifc Reports, DOI: https://doi.org/10.1038/s41598-021-03322-8
Alessandro Bitetto, Paola Cerchiello, Stefano Filomeni, Alessandra Tanda, Barbara Tarantino. (2021). “Machine Learning and Credit Risk: Empirical Evidence from SMEs”, DEM Working Papers Series, University of Pavia, ISSN: 2281-1346
LICENSING
Our current solution is based on software developed by Prof. Michele Modina, founder of 110 Laude, which has been subjected to continuous enrichments over time. The software is used, through a
license agreement, by up to 500 Italian MSMEs and networks of credit brokers.Our current solution is based on software developed by Prof. Michele Modina, founder of 110 Laude, which has been subjected to continuous enrichments over time. The software is used, through a
license agreement, by up to 500 Italian MSMEs and networks of credit brokers.
Grants and awards
ESRC Impact Acceleration Account, 2021. Project title: SME credit risk assessment: The role of market and accounting information
Partnership with research center and university spin-off
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Team
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