An academic review: applications of data mining techniques in finance industry

With the development of Internet techniques, data volumes are doubling every two years, faster than predicted by Moore’s Law. Big Data Analytics becomes particularly important for enterprise business. Modern computational technologies will provide effective tools to help understand hugely accumulated data and leverage this information to get insights into the finance industry. In order to get actionable insights into the business, data has become most valuable asset of financial organisations, as there are no physical products in finance industry to manufacture. This is where data mining techniques come to their rescue by allowing access to the right information at the right time. These techniques are used by the finance industry in various areas such as fraud detection, intelligent forecasting, credit rating, loan management, customer profiling, money laundering, marketing and prediction of price movements to name a few. This work aims to survey the research on data mining techniques applied to the finance industry from 2010 to 2015.The review finds that Stock prediction and Credit rating have received most attention of researchers, compared to Loan prediction, Money Laundering and Time Series prediction. Due to the dynamics, uncertainty and variety of data, nonlinear mapping techniques have been deeply studied than linear techniques. Also it has been proved that hybrid methods are more accurate in prediction, closely followed by Neural Network technique. This survey could provide a clue of applications of data mining techniques for finance industry, and a summary of methodologies for researchers in this area. Especially, it could provide a good vision of Data Mining Techniques in computational finance for beginners who want to work in the field of computational finance

The Practice of Investment Appraisal

This case study examines the capital budgeting practices of four companies operating in different industry. The findings indicate that most companies follow decentralised project decision-making. Despite the use of DCF techniques, there is a tendency to combine with the newly crafted value management tools, which shows a trend shift in the capital budgeting methods. In addition, firms are found trying to modify the original DFC tools so as to accommodate their needs. However, firms don't use the same technique from project inception to completion.DCF methods;project;shareholder value analysis;value management techniques;Investment appraisal

ERP implementation methodologies and frameworks: a literature review

Enterprise Resource Planning (ERP) implementation is a complex and vibrant process, one that involves a combination of technological and organizational interactions. Often an ERP implementation project is the single largest IT project that an organization has ever launched and requires a mutual fit of system and organization. Also the concept of an ERP implementation supporting business processes across many different departments is not a generic, rigid and uniform concept and depends on variety of factors. As a result, the issues addressing the ERP implementation process have been one of the major concerns in industry. Therefore ERP implementation receives attention from practitioners and scholars and both, business as well as academic literature is abundant and not always very conclusive or coherent. However, research on ERP systems so far has been mainly focused on diffusion, use and impact issues. Less attention has been given to the methods used during the configuration and the implementation of ERP systems, even though they are commonly used in practice, they still remain largely unexplored and undocumented in Information Systems research. So, the academic relevance of this research is the contribution to the existing body of scientific knowledge. An annotated brief literature review is done in order to evaluate the current state of the existing academic literature. The purpose is to present a systematic overview of relevant ERP implementation methodologies and frameworks as a desire for achieving a better taxonomy of ERP implementation methodologies. This paper is useful to researchers who are interested in ERP implementation methodologies and frameworks. Results will serve as an input for a classification of the existing ERP implementation methodologies and frameworks. Also, this paper aims also at the professional ERP community involved in the process of ERP implementation by promoting a better understanding of ERP implementation methodologies and frameworks, its variety and history

Machine learning applied to banking supervision a literature review

Guerra, P., & Castelli, M. (2021). Machine learning applied to banking supervision a literature review. Risks, 9(7), 1-24. [136]. https://doi.org/10.3390/risks9070136Machine learning (ML) has revolutionised data analysis over the past decade. Like in-numerous other industries heavily reliant on accurate information, banking supervision stands to benefit greatly from this technological advance. The objective of this review is to provide a compre-hensive walk-through of how the most common ML techniques have been applied to risk assessment in banking, focusing on a supervisory perspective. We searched Google Scholar, Springer Link, and ScienceDirect databases for articles including the search terms “machine learning” and (“bank” or “banking” or “supervision”). No language, date, or Journal filter was applied. Papers were then screened and selected according to their relevance. The final article base consisted of 41 papers and 2 book chapters, 53% of which were published in the top quartile journals in their field. Results are presented in a timeline according to the publication date and categorised by time slots. Credit risk assessment and stress testing are highlighted topics as well as other risk perspectives, with some references to ML application surveys. The most relevant ML techniques encompass k-nearest neigh-bours (KNN), support vector machines (SVM), tree-based models, ensembles, boosting techniques, and artificial neural networks (ANN). Recent trends include developing early warning systems (EWS) for bankruptcy and refining stress testing. One limitation of this study is the paucity of contributions using supervisory data, which justifies the need for additional investigation in this field. However, there is increasing evidence that ML techniques can enhance data analysis and decision making in the banking industry.publishersversionpublishe

Banks, local credit markets and credit supply

The volume collects the papers presented at the Conference on "Banks, Local Credit Markets and Credit Supply" held in Milan, on 24 March 2010. The papers presented at the two sessions of the Conference analyse how banks' lending activities are organized and how this affects the supply of credit to small and medium-sized enterprises (SMEs). The first session focuses on new lending technologies and banking organization. The second session studies how these organizational variables affect the lending activity to SMEs. The papers draw on the results of a sample survey of more than 300 Italian banks conducted by the Bank of Italy in 2007.banking organization, credit scoring, relationship lending, soft information

The Impact of Disruptive Technologies in Finance and Accounting: A Systematic Literature Review

Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Knowledge Management and Business IntelligenceThe digital transition era, marked by a strong evolution of Information Technologies, and its massive expansion towards all products, services, and sectors, has changed all known methods for carrying out and conducting all sorts of professional practices. Within the scope of accounting activities and transactions related to accounting, various tasks have started to be automatized with the help of Artificial Intelligence and Machine Learning. Hence, no longer existing the need of spending time on some of the repetitive day-to-day tasks, professionals in these areas will have more time and freedom to perform predictive business analysis, to collect and report financial data, which will most likely become vital to assist decision-making and possible attraction of new investments. As such, there is a clear link between accounting and the emergence of disruptive technologies, which indicates an interesting research area for accounting information systems researchers. What is the impact of disruptive technologies in accounting practices? What is the role played by accountants to work alongside their digital colleagues? What are the skills that accountants may have to be future proof in an ever-changing digital environment? This dissertation aims to answer these questions by following a qualitative and exploratory approach, through a systematic literature review. The analysis reveals that the impact of disruptive technologies in finance and accounting can be summarized in four main domains, Strategic Management, Technology Innovation, Business Acumen and Operations and Accounting Provision. We review the content of recent academic literature regarding the relationship between disruptive technologies and accounting and highlight research gaps and opportunities for future research

The AI Revolution: Opportunities and Challenges for the Finance Sector

This report examines Artificial Intelligence (AI) in the financial sector, outlining its potential to revolutionise the industry and identify its challenges. It underscores the criticality of a well-rounded understanding of AI, its capabilities, and its implications to effectively leverage its potential while mitigating associated risks. The potential of AI potential extends from augmenting existing operations to paving the way for novel applications in the finance sector. The application of AI in the financial sector is transforming the industry. Its use spans areas from customer service enhancements, fraud detection, and risk management to credit assessments and high-frequency trading. However, along with these benefits, AI also presents several challenges. These include issues related to transparency, interpretability, fairness, accountability, and trustworthiness. The use of AI in the financial sector further raises critical questions about data privacy and security. A further issue identified in this report is the systemic risk that AI can introduce to the financial sector. Being prone to errors, AI can exacerbate existing systemic risks, potentially leading to financial crises. Regulation is crucial to harnessing the benefits of AI while mitigating its potential risks. Despite the global recognition of this need, there remains a lack of clear guidelines or legislation for AI use in finance. This report discusses key principles that could guide the formation of effective AI regulation in the financial sector, including the need for a risk-based approach, the inclusion of ethical considerations, and the importance of maintaining a balance between innovation and consumer protection. The report provides recommendations for academia, the finance industry, and regulators

Banking consolidation and small business lending

The paper investigates small business lending as an information problem. It models the effects of information asymmetries within the bank combined with fixed wages. Two kinds of inefficiencies arise in equilibrium: the credit officer either sometimes shirks or he is occasionally fired. In both cases lending falls below the first-best level. The solution, when the bank accepts the information asymmetries, is called the centralized structure. Under decentralized structure the bank employs additional supervisors to mitigate the information asymmetries within its organization. Decentralized banks manage to finance more small firms, but incur higher costs than centralized ones. Small banks are interpreted as a bank with relatively few credit officers, whom can be monitored without information asymmetries. The specification allows for investigating the effects of banking consolidation and technological change on small business lending. The model suggests that not banking size, but organizational structure is decisive in small business lending. JEL Classification: G21, G34, J30banking, Corporate governance, efficiency wage, small business lending

Updated discussions on ‘Hybrid multiple criteria decisionmaking methods: a review of applications for sustainability issues’

A recent review discussed a variety of hybrid multiple criteria decision-making (H.M.C.D.M.) methods on the subject of sustainability issues. Some soft computing techniques, such as the fuzzy set, have contributed significantly to H.M.C.D.M. studies, emulating the imprecise or uncertain judgments of experts/decision makers in a complex environment. Nevertheless, a new rising trend in H.M.C.D.M., known as multiple rule-based decision-making (M.R.D.M.), which has the advantage of revealing understandable knowledge for supporting systematic improvements based on influential network relation maps (I.N.R.M.), was not discussed in the review. This study therefore attempts to extend the review by introducing recent developments and the associated work on M.R.D.M. for solving practical problems, updating the discussion