Analisa Sentimen Financial Technology Peer To Peer Lending Pada Aplikasi Koinworks

Bertambahnya jumlah perusahaan financial technology (fintech) yang terdaftar di Otoritas Jasa Keuangan mengartikan bahwa industri ini semakin dilirik karena  dibutuhkan dalam sistem perekonomian di Indonesia. Namun perkembangan Fintech P2PL telah menimbulkan beberapa risiko. Pertama, ada risiko gagal bayar, karena tidak ada jaminan atau persyaratan kontak fisik. Kedua, ada risiko yang terkait dengan keamanan data (risiko cyber), tata kelola, dan privasi pelanggan dan juga karena kerentanan sistem dan penyalahgunaan data, baik sengaja atau tidak sengaja. Ulasan yang terdapat pada kolom komentar Google Play dapat dimanfaatkan sebagai sumber data yang dapat di oleh dengan data mining. Penelitian ini akan menganalisis mengenai permasalahan yang berkaitan dengan beberapa ulasan tentang  Fintech P2PL  apikasi Koinworks pada ulasan di Google Play Store serta menentukan hasil akurasi analisis sentimen yang dihasilkan algoritma Decision Tree, K-Nearest Neigbor dan Support Vector Machine. Adapun manfaat dari penelitian ini adalah untuk membantu manajemen aplikasi Koinworks mengenai opini positif atau negatif dari pengguna aplikasi serta dapat  memberikan bukti  secara  empiris  untuk  teori  yang  berkaitan sehingga  dapat  dijadikan  sumbangan  pemikiran untuk pengembangan teori berikutnya. Algoritma SVM dengan Cross Validation + Parameter Optimization menghasilkan Accuracy 91,03% precision tertinggi yaitu dengan 96,73%% , recall 85,34% dan AUC  tertinggi yaitu 0,986 yang termasuk dalam excellent classification. AbstractThe increasing number of financial technology (fintech) companies registered with the Financial Services Authority means that this industry is increasingly being looked at because it is needed in the economic system in Indonesia. However, the development of Fintech P2PL has created several risks. First, there is a risk of default, because there are no guarantees or physical contact requirements. Second, there are risks associated with data security (cyber risk), governance, and customer privacy and also because of system vulnerabilities and data abuse, whether intentionally or unintentionally. Reviews contained in the Google Play comments column can be used as a data source that can be shared with data mining. This research will analyze the problems related to some reviews about the Fintech P2PL Koinworks application on reviews on the Google Play Store and determine the results of the accuracy of sentiment analysis produced by the Decision Tree algorithm, K-Nearest Neigbor and Support Vector Machine. The benefits of this research are to help the management of Koinworks applications regarding positive or negative opinions of application users and can provide empirical evidence for related theories so that they can be contributed to the development of subsequent theories. SVM algorithm with Cross Validation + Parameter Optimization produces Accuracy 91.03% of the highest precision with 96.73 %%, 85.34% recall and the highest AUC of 0.986 which is included in excellent classification

Research on Credit Risk Assessment of P2P Network Platform: Based on the Logistic Regression Model of Evidence Weight

As an emerging credit model, P2P network credit has been developing rapidly in recent years. At the same time, it also faces many credit risk problems. This paper focuses on the credit risk of borrowers, and constructs a model of WOE and logistic regression to evaluate the risk assessment of China’s P2P network platform, Hong ling Venture. The research results show that the main factors that affect the loan success rate of P2P lending platform include loan amount, annual interest rate, bidding transaction amount and proportion of repayment on time and so on. By constructing the model of combination of the logistic regression with weight of evidence, this paper provides an appropriate method to manipulate the borrowing information of loan borrowers and evaluates the borrowing behavior of borrowers simultaneously, so that P2P credit platform can reduce the credit risk caused by borrower

Does Borrower Domicile Influence the Credit Default in P2P Lending? Preliminary Analysis from Indonesia

Purpose: Credit risk is one of the most fundamental risks that P2P lending platforms have. The magnitude of information asymmetry, consumer behavior, and the unequal distribution of financial literacy make credit risk in P2P lending more vulnerable in several parts of Indonesia. The purpose of this study was to determine the domicile of the borrower on the credit risk in P2P lending Methodology: We use time series data from January 2018-December 2021 for analysis. Vector Error Correction Model (VECM) is used to analyze the data. Findings: The results show that borrowers domiciled outside Java influence the credit default significantly positively, while borrowers domiciled in Java influence credit default significantly negatively. Moreover, interest rate influences positively significant on P2P lending default, while inflation influences positively on P2P lending default. Novelty: this paper is the first paper to analyze the P2P credit default in Indonesia using time series analysis.

Peer-to-Peer (P2P) Lending in Europe: Evaluating the Default Risk of Borrowers in the Context of Gender and Education

In recent years, the importance of social lending activities and their effects on consumers have been highlighted by the widespread use of peer-to-peer lending platforms and the global race in fintech. Our study focuses on factors that affect the likelihood that European borrowers on peer-to-peer lending platforms, which are currently based in Estonia, Finland, and Spain, will default on their loans. Starting with the publicly accessible Bondora database, we examine the different economic and social characteristics of the borrowers to analyze the factors that contributed to loan default between 2013 and 2021. We use a Logit model to calculate the ex-post probability of default for factors derived from Principal Component Analysis as well as the original variables supplied by the database. The results show how crucially important education is for borrowers in lowering the risk of default, along with loan characteristics like high debt levels, long loan terms, and high interest rates. In addition, gender plays an important role in determining loan default, with a particular focus on women's conditions within the family. Regarding financial inclusion and its social implications, our findings suggest different ways to improve financial literacy and promote peer-to-peer lending. Future research could develop on the findings by applying them to other lending platforms and countries

The Role of Gender and Education in Peer-to-peer Lending Activities: Evidence from a European Cross-country Study

The wide use of peer-to-peer lending platforms coupled with the Fintech global race has emphasized the role of social lending activities and their impact on consumers in recent years. Starting from the publicly available Bondora database, we analyse determinants of loan default during the 2013-2021 period by studying individual economic and social factors of borrowers. We apply a Logit model to estimate the ex-post probability of default on both original variables provided by the database and factors obtained by Principal Component Analysis. Results show the fundamental role of borrowers’ education in reducing the probability of default, as with financial awareness obtained by loan characteristics. In addition, gender plays an important role in determining loan default, with a particular focus on women's conditions within the family. Regarding financial inclusion and its social implications, our findings suggest different ways to improve financial literacy and promote peer-to-peer lending

The Role of Gender and Education in Peer-to-peer Lending Activities: Evidence from a European Cross-country Study

The wide use of peer-to-peer lending platforms coupled with the Fintech global race has emphasized the role of social lending activities and their impact on consumers in recent years. Starting from the publicly available Bondora database, we analyse determinants of loan default during the 2013-2021 period by studying individual economic and social factors of borrowers. We apply a Logit model to estimate the ex-post probability of default on both original variables provided by the database and factors obtained by Principal Component Analysis. Results show the fundamental role of borrowers’ education in reducing the probability of default, as with financial awareness obtained by loan characteristics. In addition, gender plays an important role in determining loan default, with a particular focus on women's conditions within the family. Regarding financial inclusion and its social implications, our findings suggest different ways to improve financial literacy and promote peer-to-peer lending

Are microfinance institutions gender driven? A clinical study

Programa de Doctorado en Administración y Dirección de EmpresasLínea de Investigación: Control de Gestión y FinanzasClave Programa: DAECódigo Línea: 102Esta Tesis Doctoral tiene como objetivo general determinar las variables predictoras del desempeño financiero de las instituciones microfinancieras (IMFs). Habida cuenta que las IMFs son organizaciones híbridas con la ¿misión dual¿ de sostenibilidad financiera, como lo establece la Escuela Institucional, y el propósito social de aliviar la pobreza, como lo establece la Escuela de Bienestar, nos enfocamos particularmente en esclarecer si determinadas variables sociales, medidas a través de diversos ratios de género, son significativas para predecir dicho rendimiento financiero, y contribuir, de esta forma, a cumplir con la doble misión de las IMFs. Para llevarlo a cabo, se corre un algoritmo Random Forest dando como resultado una relación positiva entre el rendimiento de las IMFs y dos variables de género como son la proporción de mujeres prestatarias y las oficiales de crédito.Universidad Pablo de Olavide de Sevilla. Departamento de Economía Financiera y Contabilida