Risk Assessment Approaches in Banking Sector –A Survey

Prediction analysis is a method that makes predictions based on the data currently available. Bank loans come with a lot of risks to both the bank and the borrowers. One of the most exciting and important areas of research is data mining, which aims to extract information from vast amounts of accumulated data sets. The loan process is one of the key processes for the banking industry, and this paper examines various prior studies that used data mining techniques to extract all served entities and attributes necessary for analytical purposes, categorize these attributes, and forecast the future of their business using historical data, using a model, banks\u27 business, and strategic goals

Machine learning for personal credit evaluation: A systematic review

The importance of information in today's world as it is a key asset for business growth and innovation. The problem that arises is the lack of understanding of knowledge quality properties, which leads to the development of inefficient knowledge-intensive systems. But knowledge cannot be shared effectively without effective knowledge-intensive systems. Given this situation, the authors must analyze the benefits and believe that machine learning can benefit knowledge management and that machine learning algorithms can further improve knowledge-intensive systems. It also shows that machine learning is very helpful from a practical point of view. Machine learning not only improves knowledge-intensive systems but has powerful theoretical and practical implementations that can open up new areas of research. The objective set out is the comprehensive and systematic literature review of research published between 2018 and 2022, these studies were extracted from several critically important academic sources, with a total of 73 short articles selected. The findings also open up possible research areas for machine learning in knowledge management to generate a competitive advantage in financial institutions.Campus Lima Centr

Analisis Perbandingan Optimasi berbasis Evolutionary pada Algoritma Klasifikasi Penentuan Profile Resiko Nasabah

Penelitian ini mengungkapkan bagaimana pentingnya penanganan deteksi profile nasabah untuk meminimalisir terjadinya penyalahgunaan akun nasabah. Kebutuhan akan nasabah baru dengan berbagai macam produk perbankan membuat layanan screening awal begitu penting dilakukan oleh pihak perbankan agar mendapatkan informasi profile resiko nasabah sejak dini. Oleh karena itu, tujuan dari penelitian ini adalah menentukan model terbaik dari klasifikasi data profile nasabah dengan cara membandingkan model klasifikasi data mining Naïve Bayes, Decision Tree, Random Forest, KNN, SVM. Model dengan akurasi terbaik inilah yang nantinya akan direkomedasikan sebagai model aternatif untuk melakukan prediksi profile resiko nasabah. Pada penelitian ini juga dilakukan proses optimasi dengan menggunakan Optimize Selection (Evolutionary) pada setiap teknik klasifikasi yang digunakan. Dimana dihasilkan bahwa model algoritma Random Forest mendapatkan hasil total akurasi paling baik yaitu sebesar 82.55% dengan angka kenaikan optimasi sebesar 2.51%. Proses training dan testing pada penelitian ini menggunakan komposisi 80% dataset training dan 20% dataset untuk testing sedangkan metodologi yang digunakan pada penelitian ini adalah dengan menggunakan CRISP-DM

Towards a balanced contribution of household credit to the economy. CEPS-ECRI Task Force Report, May 2015

While policy-makers are creating conditions to strengthen recovery, the debate on the role that retail finance should play in this respect focuses on corporate loans rather than on household credit. The improvement of financing conditions for firms in order to support further investment spending is certainly essential to ensuring sustainable growth. However, a significant part of EU growth will depend on the behaviour of households and on their ability to secure funding for their consumption and investment. It is therefore essential to place further emphasis on the different options available to stimulate household credit, in particular consumer loans. Nevertheless, in order to avoid past mistakes, regulators should continue to develop a framework where consumer loans (and by extension household credit) contributes to the economy in a balanced way. To achieve this, five main issues need to be addressed further

Prediction of Cross-Platform and Native Apps Technology Opportunities for Beginner Developers Using C 4.5 and Naive Bayes Algorithms

The competition between native and cross-platform app development makes application development simpler, safer, and more scalable. However, developers must have sufficient fundamentals, and the industry must conduct good research to shorten development time and minimize expenses. In order to solve these problems, this study made a prediction that discusses the technology that has a chance to survive in the industry so as not to be left behind in technology. Using Naïve Bayes and C 4.5 algorithms into a dataset with nine programming languages related to mobile app development. Results obtained in This research show Dart as a programming language that supports cross-platform frameworks and Kotlin as a programming language that supports native app frameworks is a technology that would have the opportunity in the future with an accuracy level above 90% with Naïve Bayes and C 4.5 algorithms. These results are obtained by testing an algorithm model using MAPE, consistent dataset sharing, and careful data processing. This research Can help entry-level developers learn and deepen the fundamentals of technology and can add knowledge to the industry in choosing a technology

Reimagining peer-to-peer lending sustainability: unveiling predictive insights with innovative machine learning approaches for loan default anticipation

Peer-to-peer lending, a novel element of Internet finance that links lenders and borrowers via online platforms, has generated large profits for investors. However, borrowers’ missed payments have negatively impacted the industry’s sustainable growth. It is imperative to create a system that can correctly predict loan defaults to lessen the damage brought on by defaulters. The goal of this study is to fill the gap in the literature by exploring the feasibility of developing prediction models for P2P loan defaults without relying heavily on personal data while also focusing on identifying key variables influencing borrowers’ repayment capacity through systematic feature selection and exploratory data analysis. Given this, this study aims to create a computational model that aids lenders in determining the approval or rejection of a loan application, relying on the financial data provided by applicants. The selected dataset, sourced from an open database, contains 8578 transaction records and includes 14 attributes related to financial information, with no personal data included. A loan dataset is first subjected to an in-depth exploratory data analysis to find behaviors connected to loan defaults. Subsequently, diverse and noteworthy machine learning classification algorithms, including Random Forest, Support Vector Machine, Decision Tree, Logistic Regression, Naïve Bayes, and XGBoost, were employed to build models capable of discerning borrowers who repay their loans from those who do not. Our findings indicate that borrowers who fail to comply with their lenders’ credit policies, pay elevated interest rates, and possess low FICO ratings are at a higher likelihood of defaulting. Furthermore, elevated risk is observed among clients who obtain loans for small businesses. All classification models, including XGBoost and Random Forest, successfully developed and performed satisfactorily and achieved an accuracy of over 80%. When the decision threshold is set to 0.4, the best performance for predicting loan defaulters is achieved using logistic regression, which accurately identifies 83% of the defaulted loans, with a recall of 83%, precision of 21% and f1 score of 33%

Social Investment Landscape in Asia: Insights from Southeast Asia

AVPN has identified the need for a comprehensive overview of the Asian philanthropy and social investment landscape to offer social investors a guide to the opportunities for social investment in Asia. The Social Investment Landscape in Asia will be an invaluable resource for funders and resource providers as they assess the opportunities and challenges for philanthropy and social investment in the region. It is designed to be a guide for both new social investors looking toenter the Asian market and existing social investors exploring cross-border or cross-sector opportunities within the region. The Landscape is another way to further AVPN's mission to increase the flow of financial, human and intellectual capital to the Asian social sector

Data Science for Finance: Targeted Learning from (Big) Data to Economic Stability and Financial Risk Management

A thesis submitted in partial fulfillment of the requirements for the degree of Doctor in Information Management, specialization in Statistics and EconometricsThe modelling, measurement, and management of systemic financial stability remains a critical issue in most countries. Policymakers, regulators, and managers depend on complex models for financial stability and risk management. The models are compelled to be robust, realistic, and consistent with all relevant available data. This requires great data disclosure, which is deemed to have the highest quality standards. However, stressed situations, financial crises, and pandemics are the source of many new risks with new requirements such as new data sources and different models. This dissertation aims to show the data quality challenges of high-risk situations such as pandemics or economic crisis and it try to theorize the new machine learning models for predictive and longitudes time series models. In the first study (Chapter Two) we analyzed and compared the quality of official datasets available for COVID-19 as a best practice for a recent high-risk situation with dramatic effects on financial stability. We used comparative statistical analysis to evaluate the accuracy of data collection by a national (Chinese Center for Disease Control and Prevention) and two international (World Health Organization; European Centre for Disease Prevention and Control) organizations based on the value of systematic measurement errors. We combined excel files, text mining techniques, and manual data entries to extract the COVID-19 data from official reports and to generate an accurate profile for comparisons. The findings show noticeable and increasing measurement errors in the three datasets as the pandemic outbreak expanded and more countries contributed data for the official repositories, raising data comparability concerns and pointing to the need for better coordination and harmonized statistical methods. The study offers a COVID-19 combined dataset and dashboard with minimum systematic measurement errors and valuable insights into the potential problems in using databanks without carefully examining the metadata and additional documentation that describe the overall context of data. In the second study (Chapter Three) we discussed credit risk as the most significant source of risk in banking as one of the most important sectors of financial institutions. We proposed a new machine learning approach for online credit scoring which is enough conservative and robust for unstable and high-risk situations. This Chapter is aimed at the case of credit scoring in risk management and presents a novel method to be used for the default prediction of high-risk branches or customers. This study uses the Kruskal-Wallis non-parametric statistic to form a conservative credit-scoring model and to study its impact on modeling performance on the benefit of the credit provider. The findings show that the new credit scoring methodology represents a reasonable coefficient of determination and a very low false-negative rate. It is computationally less expensive with high accuracy with around 18% improvement in Recall/Sensitivity. Because of the recent perspective of continued credit/behavior scoring, our study suggests using this credit score for non-traditional data sources for online loan providers to allow them to study and reveal changes in client behavior over time and choose the reliable unbanked customers, based on their application data. This is the first study that develops an online non-parametric credit scoring system, which can reselect effective features automatically for continued credit evaluation and weigh them out by their level of contribution with a good diagnostic ability. In the third study (Chapter Four) we focus on the financial stability challenges faced by insurance companies and pension schemes when managing systematic (undiversifiable) mortality and longevity risk. For this purpose, we first developed a new ensemble learning strategy for panel time-series forecasting and studied its applications to tracking respiratory disease excess mortality during the COVID-19 pandemic. The layered learning approach is a solution related to ensemble learning to address a given predictive task by different predictive models when direct mapping from inputs to outputs is not accurate. We adopt a layered learning approach to an ensemble learning strategy to solve the predictive tasks with improved predictive performance and take advantage of multiple learning processes into an ensemble model. In this proposed strategy, the appropriate holdout for each model is specified individually. Additionally, the models in the ensemble are selected by a proposed selection approach to be combined dynamically based on their predictive performance. It provides a high-performance ensemble model to automatically cope with the different kinds of time series for each panel member. For the experimental section, we studied more than twelve thousand observations in a portfolio of 61-time series (countries) of reported respiratory disease deaths with monthly sampling frequency to show the amount of improvement in predictive performance. We then compare each country’s forecasts of respiratory disease deaths generated by our model with the corresponding COVID-19 deaths in 2020. The results of this large set of experiments show that the accuracy of the ensemble model is improved noticeably by using different holdouts for different contributed time series methods based on the proposed model selection method. These improved time series models provide us proper forecasting of respiratory disease deaths for each country, exhibiting high correlation (0.94) with Covid-19 deaths in 2020. In the fourth study (Chapter Five) we used the new ensemble learning approach for time series modeling, discussed in the previous Chapter, accompany by K-means clustering for forecasting life tables in COVID-19 times. Stochastic mortality modeling plays a critical role in public pension design, population and public health projections, and in the design, pricing, and risk management of life insurance contracts and longevity-linked securities. There is no general method to forecast the mortality rate applicable to all situations especially for unusual years such as the COVID-19 pandemic. In this Chapter, we investigate the feasibility of using an ensemble of traditional and machine learning time series methods to empower forecasts of age-specific mortality rates for groups of countries that share common longevity trends. We use Generalized Age-Period-Cohort stochastic mortality models to capture age and period effects, apply K-means clustering to time series to group countries following common longevity trends, and use ensemble learning to forecast life expectancy and annuity prices by age and sex. To calibrate models, we use data for 14 European countries from 1960 to 2018. The results show that the ensemble method presents the best robust results overall with minimum RMSE in the presence of structural changes in the shape of time series at the time of COVID-19. In this dissertation’s conclusions (Chapter Six), we provide more detailed insights about the overall contributions of this dissertation on the financial stability and risk management by data science, opportunities, limitations, and avenues for future research about the application of data science in finance and economy