Big Data Applications the Banking Sector: A Bibliometric Analysis Approach

This study aims to review the existing literature on big data applications in banking using a bibliometric analysis approach. This approach describes citation rates, research outputs, and their implementations, along with current streams in the field and future research agenda. The articles were selected from 2012 to 2020 and sorted by the citation rate in results and analysis. We have discovered 60 papers related to big data in banking, although the applications of big data in the banking sector are growing rapidly, the number of research output in this field is limited. Several themes are extracted from the studies that are reviewed, analyzed, and presented in this report. This review covered the themes that include investment, profit, competition, credit risk analysis, banking crime, and fintech. This report also signifies the importance, use of big data, and its function in the banking and financial sector. This study has also discussed the future research scope in the banking industry’s big data analytics.

PENGARUH RISIKO KREDIT DAN TINGKAT KECUKUPAN MODAL TERHADAP PROFITABILITAS PERBANKAN YANG LISTING DI BEI PERIODE 2015-2019

Abstrak: Pengaruh Risiko Kredit Dan Tingkat Kecukupan Modal Terhadap Profitabilitas Perbankan Yang Listing Di BEI Periode 2015-2019. Penelitian bertujuan untuk mengetahui pengaruh Risiko Kredit dan Tingkat Kecukupan Modal Terhadap Profitabilitas Perbankan yang Listing Di BEI Periode 2015-2019 dan ada 43 perbankan listing di BEI. Sample diambil 10 perusahaan perbankan. Teknik sampling yang digunakan purposive sampling. Data di analisis dengan analisis risiko kredit, analisis tingkat kecukupan modal, analisis profitabilitas, uji asumsi klasik, uji parsial dan uji serempak. Secara parsial dan serempak hasil menunjukan resiko kredit dan tingkat kecukupan modal berpengaruh signifikan terhadap profitabilitas. sedangkan secara serempak menyatakan bahwa ada pengaruh yang signifikan resiko kredit dan tingkatkan kecukupan modal terhadap profitabilitas.Kata kunci: Profitabilitas, Risiko Kredit, Tingkat Kecukupan Modal.Abstract: Effect of Credit Risk and Capital Adequacy Levels Profitability of Banks Listing on the IDX for the 2015-2019 Period. This study aims to determine the effect of Credit Risk and Capital Adequacy Level on the Profitability of Banks Listed on the IDX for the 2015-2019 period, and there are 43 banks listed on the IDX. Samples were taken from 10 banking companies. The sampling technique used was purposive sampling. The data were analyzed by credit risk analysis, capital adequacy level analysis, profitability analysis, classical assumption test, partial test and simultaneous test. Partially and simultaneously the results show that credit risk and the level of capital adequacy have a significant effect on profitability. while simultaneously stating that there is a significant effect of credit risk and increasing capital adequacy on profitability.Keywords: Profitabilitas, Credit Risk, Capital Adequacy Level.

Financial Inclusion in Emerging Economies: The Application of Machine Learning and Artificial Intelligence in Credit Risk Assessment

In banking and finance, credit risk is among the important topics because the process of issuing a loan requires a lot of attention to assessing the possibilities of getting the loaned money back. At the same time in emerging markets, the underbanked individuals cannot access traditional forms of collateral or identification that is required by financial institutions for them to be granted loans. Using the literature review approach through documentary and conceptual analysis to investigate the impact of machine learning and artificial intelligence in credit risk assessment, this study discovered that artificial intelligence and machine learning have a strong impact on credit risk assessments using alternative data sources such as public data to deal with the problems of information asymmetry, adverse selection, and moral hazard. This allows lenders to do serious credit risk analysis, to assess the behaviour of the customer, and subsequently to verify the ability of the clients to repay the loans, permitting less privileged people to access credit. Therefore, this study recommends that financial institutions such as banks and credit lending institutions invest more in artificial intelligence and machine learning to ensure that financially excluded households can obtain credit.

Peer-to-Peer Lending and EU Credit Laws: A Creditworthiness Assessment, Credit-Risk Analysis or … Neither of the Two?

The Article deals with the protection of consumer borrowers and lending investors in peer-to-peer lending within the legal framework provided by EU credit laws. This is the legal framework for EU Member States in the area of loans to consumers. In particular, the article analyses the business model of taking lending decisions on financial technologies (“Fintech”) and big data vis-à-vis the legal obligation of the creditworthiness assessment by lenders. At the same time, it extends the applicability of such a business model to the credit-risk analysis undertaken in the interest of lenders. Ultimately, it questions to what extent EU law caters for peer-to-peer lending, and and to what extent consumers and lenders can find protection. It hints that peer-to-peer lending presents risks for both consumers and lenders, falling short of legal obligations and established practices for their protection.