FinTech

Economic disinformation causes significant harm, resulting in substantial losses for the global economy. Each year, it is estimated that around USD 78 billion is lost due to the spread of false or misleading information, with a major share stemming from stock market fluctuations and misguided decisions. In Croatia, the rapid spread of economic misinformation further threatens decision-making and institutional credibility. The EkonInfoChecker project was established to address this issue by combining human fact-checking with AI-based detection. This paper presents the project’s AI component, which adapts English-language datasets (FakeNews Corpus 1.0 and WELFake) into Croatian, yielding over 170,000 articles in economics, finance, and business. We trained and evaluated six models—FastText, NBSVM, BiGRU, BERT, DistilBERT, and the Croatian-specific BERTić—using precision, recall, F1-score, and ROC-AUC. Results show that transformer-based models consistently outperform traditional approaches, with BERTić achieving the highest accuracy, reflecting its advantage as a language-specific model. The study demonstrates that AI can effectively support fact-checking by pre-screening economic content and flagging high-risk items for human review. However, limitations include reliance on translated datasets, reduced performance on complex categories such as satire and pseudoscience, and challenges in generalizing to real-time Croatian media. These findings underscore the need for native datasets, hybrid human-AI workflows, and governance aligned with the EU AI Act.

In this study, we review recent research on how digital payment technologies (DPTs) promote financial inclusion (FI) across the world. Drawing on empirical studies from the past decade, we show that digital payment systems have helped reduce financial exclusion—particularly in developing economies—by expanding access to essential financial services for underserved groups. The paper also highlights the role of demographic factors such as age and gender, with evidence of higher adoption among youth and women. We identify the main indicators used to measure digital payment adoption and FI, providing a foundation for future empirical analysis. To deepen understanding, we call for combining macroeconomic data with rigorous econometric approaches to better capture how DPTs contribute to inclusive financial systems. The paper further discusses how emerging innovations—including blockchain, artificial intelligence, cloud computing, and biometric authentication—are improving the efficiency, security, and accessibility of digital payments. Together, these technologies are likely to accelerate the transition toward fully digital financial ecosystems and expand the potential for inclusive and sustainable growth.

In this study, we empirically examine the determinants of fundraising success in Japan’s lending-based crowdfunding (LBCF), with a focus on the financial literacy of investors. Using 465 campaigns on the LBCF platform “Bankers” (December 2020–September 2024), we test two predictions derived from the lack of financial literacy hypothesis: (H1) investors are influenced by peripheral signals; (H2) diagnostic signals are not properly evaluated. Both are rejected. In cross-sectional tests, peripheral cues such as “Perks” are negatively associated with success, and the effects of “Title length” and “Purple highlighted text” observed in simpler models vanish when analyzed jointly. By contrast, diagnostic information is consistently informative: “Domestic campaign” and “Co-investment” are positive, while “Investment term” is negative; “Investment capital” is also negative, contrary to prior expectations. The results are robust to controls for the campaign sector and to alternative specifications (probit; OLS on achievement rate). Overall, investors in Japan’s LBCF appear to rely on diagnostic rather than peripheral signals, indicating financially literate, rational decision-making.