Fintech Trends Relationships Research: A Bibliometric Citation Meta-Analysis

This paper presents a review among how scholarly research on Fintech trends relationships has evolved over the past years by conducting a bibliometric citation. This literature analysis was based on the publication journals and articles in the ISI Web of Science databases. We show the impact of cited journals, key articles and outline possible future research avenues. Also, we map how the top publications are related in terms of their citation relationships and identify six different research fields, or lines of enquiry: (1) Payments, (2) Insurance, (3) Deposit & Lending, (4) Capital Raising, (5) Investment Management, (6) Market Provisioning. The study explores rankings of fintech-related journals list the first six journals had contributed eighty percentage of published papers, and concerned with the roles of information and communication technologies in the economy and society. Focusing on the research frontiers in finance, our paper identifies emerging research trends. We highlight possible pathways for researchers to build on existing knowledge and pursue opportunities for innovative and exciting new research contributing to an expansion of the research frontiers

Penyuluhan tentang Financial Technology di Desa Kerinjing, Kecamatan Tanjung Raja, Kabupaten Ogan Ilir

oai:ojs.jscs.ejournal.unsri.ac.id:article/4Fintech (financial technology) adalah inovasi di bidang jasa keuangan yang sedang tren di indonesia. Fintech memberikan pengaruh kepada masyarakat secara luas dengan memberikan akses terhadap produk keuangan sehingga transaksi menjadi lebih praktis dan efektif. Tujuan dari kegiatan pengabdian ini untuk memberikan pemahamaan tentang manfaat dana elektronik non tunai dan simpan pinjam financial technology, memberikan pemahamaan tentang terjadinya beberapa kasus penipuan melalui pinjaman financial technology, memberikan pengetahuan tentang beberapa aplikasi dana elektronik non tunai kepada masyarakat. Metode kegiatan penyuluhan dimana terdapat ceramah, tanya-jawab dan evaluasi. Hasil kegiatan adalah pemberian pemahaman kepada masyarakat atas kemajuan teknologi termasuk teknologi transaksi keuangan sangat membantu masyarakat untuk menggunakannya secara bijak, sehingga kehadiran teknologi ini dapat memberikan manfaat yang besar dan terhindari dari risiko yang dapat menyebabkan kerugian bagi masyarakat

The Differential Role of Alternative Data in SME-Focused Fintech Lending

The rapid emergence of risk management Fintech has led to increasing use of alternative data in personal and business financing. Yet there are significant risks and concerns resulting from using alternative data. We therefore seek to examine the differential role of alternative data in SME-focused Fintech lending. We compare the credit evaluation and fraud detection contexts and examine the circumstances under which alternative data are useful for both contexts. Our goal is to find a parsimonious set of traditional and alternative data types that can help facilitate risk management in SME-focused Fintech lending. In this short paper, we report some preliminary results and findings from the first phase of our data collection and analysis. We then discuss the potential contributions and future plans of the study

ANALISIS FAKTOR YANG MEMPENGARUHI PERSETUJUAN PENGAJUAN KREDIT USAHA MIKRO KECIL DAN MENENGAH PADA LEMBAGA PEMBIAYAAN PEER TO PEER LENDING DI MASA COVID-19

Usaha Mikro Kecil dan Menengah merupakan penguat perekenomian Indonesia, karena memiliki kontribusi yang besar dalam pertumbuhan ekonomi di Indoensia, namun semenjak munculnya COVID-19 UMKM mengalami penurunan penjualan. Menurut Badan Pusat Statistik, ekonomi Indonesia menunjukkan angka minus 5,3 persen. Hal tersebut mempengaruhi penurunan modal UMKM yang saat ini masih dalam permasalahan internal UMKM.Tujuan dari penelitian ini untuk menganalisis faktor apa saja yang mempengaruhi persetujuan pengajuan kredit UMKM pada lembaga pembiayan peer to peer lending di masa COVID-19. Penelitian ini menggunakan data sekunder yang bersumber dari website peer to peer lending, kementrian koperasi dan usaha mikro kecil menengah, laporan berita perkembangan fintech dengan menggunakan teknik analisis deskriptif dan regresi probit yang dibantu olah data melalui aplikasi IBM SPSS Statistik 25. Variabel dependen yang diguanakan peneliti yaitu persetujuan pengajuan kredit yang termasuk variable dummy sedangkan variabel independen yang digunakan dalam penelitian yaitu jumlah pinjaman, lama usaha dan jangka waktu kredit. Hasil penelitian ini ditemukan bahwa jumlah pinjaman dan jangka waktu kredit memiliki pengaruh negatif dan tidak signifikan dengan persetujuan pengajuan kredit, sedangkan lama usaha memiliki pengaruh positif dan signifikan terhadap persetujuan pengajuan kredit. Melalui penelitian ini diharapkan dapat membantu UMKM untuk mengajukan pembiayaan di lembaga peer to peer lending

Handling Uncertainty in Social Lending Credit Risk Prediction with a Choquet Fuzzy Integral Model

As one of the main business models in the financial technology field, peer-to-peer (P2P) lending has disrupted traditional financial services by providing an online platform for lending money that has remarkably reduced financial costs. However, the inherent uncertainty in P2P loans can result in huge financial losses for P2P platforms. Therefore, accurate risk prediction is critical to the success of P2P lending platforms. Indeed, even a small improvement in credit risk prediction would be of benefit to P2P lending platforms. This paper proposes an innovative credit risk prediction framework that fuses base classifiers based on a Choquet fuzzy integral. Choquet integral fusion improves creditworthiness evaluations by synthesizing the prediction results of multiple classifiers and finding the largest consistency between outcomes among conflicting and consistent results. The proposed model was validated through experimental analysis on a real- world dataset from a well-known P2P lending marketplace. The empirical results indicate that the combination of multiple classifiers based on fuzzy Choquet integrals outperforms the best base classifiers used in credit risk prediction to date. In addition, the proposed methodology is superior to some conventional combination techniques

How much should I invest? The influence of reputable investors and platform investors in online lending

This paper draws on signaling theory to examine the joint effects of platform investment and investment of reputable investors on the investment behavior of ordinary investors in online lending. We tested our hypotheses with a dataset of 2,276,380 bidding records pertaining to 46,140 loans posted on an online lending platform. Our results show that (1) The investment of reputable investors as a quality signal can increase the investment amount of subsequent investors. (2) Platform investment signal and reputable investment signal are complementary. In the loan projects with platform investment (compared to those without), investment of reputable investors exerts greater influence on the investment amount of subsequent investors. (3) Focusing on loan projects with platform investment, the investment of reputable investors has greater impact on the investment amount of subsequent investors after the platform investment. This paper offers important theoretical and practical implications

The Role of Financial Literacy in Online Peer-to-Peer Lending: An Empirical Approach

With the development of FinTech, more and more individuals are entitled to participate in financial activities, but little is known about whether they are well-equipped to make the right decisions and thus benefit from financial services. Especially, financial products and services are often perceived to be complex. Therefore, financial literacy (i.e. individuals’ ability to process economic information and make informed decision) plays a key role. In this study, we collaborate with a large P2P platform and innovate the measurement of financial literacy. Rather than assessing financial literacy through survey questions, we measure it by observing individuals’ actual decisions made on personal credit on the platform. Our preliminary results demonstrate the importance of financial literacy for both borrowing and investment in online P2P lending. Our study contributes to the nascent IS literature on FinTech, and provides practical insights for P2P lending platform owners and policy makers

Deep Learning Model Implementation Using Convolutional Neural Network Algorithm for Default P2P Lending Prediction

Peer-to-peer (P2P) lending is one of the innovations in the field of fintech that offers microloan services through online channels without intermediaries. P2P  lending facilitates the lending and borrowing process between borrowers and lenders, but on the other hand, there is a threat that can harm lenders, namely default.  Defaults on  P2P  lending platforms result in significant losses for lenders and pose a threat to the overall efficiency of the peer-to-peer lending system. So it is essential to have an understanding of such risk management methods. However, designing feature extractors with very complicated information about borrowers and loan products takes a lot of work. In this study, we present a deep convolutional neural network (CNN) architecture for predicting default in P2P lending, with the goal of extracting features automatically and improving performance. CNN is a deep learning technique for classifying complex information that automatically extracts discriminative features from input data using convolutional operations. The dataset used is the Lending Club dataset from P2P lending platforms in America containing 9,578 data. The results of the model performance evaluation got an accuracy of 85.43%. This study shows reasonably decent results in predicting p2p lending based on CNN. This research is expected to contribute to the development of new methods of deep learning that are more complex and effective in predicting risks on P2P lending platforms