Forecasting creditworthiness in retail banking: a comparison of cascade correlation neural networks, CART and logistic regression scoring models

The preoccupation with modelling credit scoring systems including their relevance to forecasting and decision making in the financial sector has been with developed countries whilst developing countries have been largely neglected. The focus of our investigation is the Cameroonian commercial banking sector with implications for fellow members of the Banque des Etats de L’Afrique Centrale (BEAC) family which apply the same system. We investigate their currently used approaches to assessing personal loans and we construct appropriate scoring models. Three statistical modelling scoring techniques are applied, namely Logistic Regression (LR), Classification and Regression Tree (CART) and Cascade Correlation Neural Network (CCNN). To compare various scoring models’ performances we use Average Correct Classification (ACC) rates, error rates, ROC curve and GINI coefficient as evaluation criteria. The results demonstrate that a reduction in terms of forecasting power from 15.69% default cases under the current system, to 3.34% based on the best scoring model, namely CART can be achieved. The predictive capabilities of all three models are rated as at least very good using GINI coefficient; and rated excellent using the ROC curve for both CART and CCNN. It should be emphasised that in terms of prediction rate, CCNN is superior to the other techniques investigated in this paper. Also, a sensitivity analysis of the variables identifies borrower’s account functioning, previous occupation, guarantees, car ownership, and loan purpose as key variables in the forecasting and decision making process which are at the heart of overall credit policy

Maximizing Intellectual Property and Intangible Assets: Case Studies in Intangible Asset Finance

As innovative companies struggle to raise funds, intellectual property and intangible assets are providing alternative ways of financing innovation. But greater awareness of them as an asset class is needed. Raising that awareness is the focus of a new report from Athena Alliance, Maximizing Intellectual Property and Intangible Assets: Case Studies in Intangible Asset Finance by Ian Ellis, a former U.S. Department of Commerce official specializing in intellectual property and international trade. The report outlines increasing, but still nascent, means of financing innovation based on these assets in public, private and venture capital markets. As industry has invested capital in research and development to develop new technology and advance other creative activities, intellectual capital has become a valuable asset class, according to the paper. In response, firms specializing in intangible-based financing are springing up, using them to raise capital for the next round of innovation.The paper details equity, equity-debt, debt, and sale-leaseback transactions, both private and public, that have helped companies raise capital, based on careful, rigorous analysis and conservative underwriting standards. For example, the author notes that in 2000, there were two public deals using royalty securitization, raising $145 million. In 2007-08,$3.3 billion was raised in 19 deals.Unlike some of the exotic financial vehicles, however, the financial products discussed in this paper are some of the most basic financing mechanisms in business. The innovation is in recognizing the value of intangible assets for corporate finance. These new financial firms are using traditional financial techniques in new ways to help innovative companies.But more should be done.One important step would be developing sound, industry-wide, underwriting standards, according to the report. For example, Small Business Administration (SBA) rules permit its loans to be used for acquisition of intangible assets when buying on-going businesses. Rules are unclear on whether those assets can be used as collateral. The paper recommends that SBA work with commercial lenders to develop standards for using intangible assets as collateral.The report builds on earlier Athena Alliance papers, notably Intangible Asset Monetization: The Promise and the Reality

Improving regulations and supervision of pension funds : are there lessons from the Banking Sector?

The main objective of this paper is to review the regulatory framework for pension funds, and examine whether there is scope for improvements in pension regulation, particularly in light of regulatory and supervisory developments in the banking industry. The report is structured as follows: The second section summarizes the literature on banking regulation and supervision, identifying the areas of consensus and the trends in regulation and supervision across countries. The third section summarizes the literature on the regulation of pension funds. The fourth section examines the scope for improvements in pension regulation, identifying possible lessons from the banking sector to the pension industry. The fifth section provides a summary and concludes.Banks&Banking Reform,Financial Intermediation,Financial Crisis Management&Restructuring,Insurance&Risk Mitigation,Environmental Economics&Policies

ADB–OECD Study on Enhancing Financial Accessibility for SMEs: Lessons from Recent Crises

During the era of global financial uncertainty, stable access to appropriate funding sources has been much harder for small and medium-sized enterprises (SMEs). The global financial crisis impacted SMEs and entrepreneurs disproportionately, exacerbating their traditional financing constraints. The financial conditions of many SMEs were weakened by the drop in demand for goods and services and the credit tightening. The sovereign debt crisis that hit several European countries contributed to further deterioration in bank lending activities, which negatively affected private sector development. The global regulatory response to financial crises, such as the Basel Capital Accord, while designed to reduce systemic risks may also constrain bank lending to SMEs. In particular, Basel III requires banks to have tighter risk management as well as greater capital and liquidity. Resulting asset preference and deleveraging of banks, particularly European banks with significant presence in Asia, could limit the availability of funding for SMEs in Asia and the Pacific. Lessons from the recent financial crises have motivated many countries to consider SME access to finance beyond conventional bank credit and to diversify their national financial system. Improving SME access to finance is a policy priority at the country and global level. Poor access to finance is a critical inhibiting factor to the survival and growth potential of SMEs. Financial inclusion is thus key to the development of the SME sector, which is a driver of job creation and social cohesion and takes a pivotal role in scaling up national economies. The Asian Development Bank (ADB) and the Organisation for Economic Co-operation and Development (OECD) have recognized that it is crucial to develop a comprehensive range of policy options on SME finance, including innovative financing models. With this in mind, sharing Asian and OECD experiences on SME financing would result in insightful discussions on improving SME access to finance at a time of global financial uncertainty. Based on intensive discussions in two workshops organized by ADB in Manila on 6–7 March 2013 and by OECD in Paris on 21 October 2013, the two organizations together compiled this study report on enhancing financial accessibility for SMEs, especially focusing on lessons from the past and recent crises in Asia and OECD countries. The report takes a comparative look at ADB and OECD experiences, and aims to identify promising policy solutions for creating an SME base that is resilient to crisis, from a viewpoint of access to finance, and which can help drive growth and development

Would credit scoring work for Islamic finance? A neural network approach

Purpose – The main aim of this paper is to distinguish whether the decision making process of the Islamic financial houses in the UK can be improved through the use of credit scoring modeling techniques as opposed to the currently used judgmental approaches. Subsidiary aims are to identify how scoring models can reclassify accepted applicants who later are considered as having bad credit and how many of the rejected applicants are later considered as having good credit; and highlight significant variables that are crucial in terms of accepting and rejecting applicants which can further aid the decision making process. Design/methodology/approach – A real data-set of 487 applicants are used consisting of 336 accepted credit applications and 151 rejected credit applications make to an Islamic finance house in the UK. In order to build the proposed scoring models, the data-set is divided into training and hold-out sub-set. The training sub-set is used to build the scoring models and the hold-out sub-set is used to test the predictive capabilities of the scoring models.70 percent of the overall applicants will be used for the training sub-set and 30 percent will be used for the testing sub-set. Three statistical modeling techniques namely Discriminant Analysis (DA), Logistic Regression (LR) and Multi-layer Perceptron (MP) neural network are used to build the proposed scoring models. Findings – Our findings reveal that the LR model has the highest Correct Classification (CC) rate in the training sub-set whereas MP outperforms other techniques and has the highest CC rate in the hold-out sub-set. MP also outperforms other techniques in terms of predicting the rejected credit applications and has the lowest Misclassification Cost (MC) above other techniques. In addition, results from MP models show that monthly expenses, age and marital status are identified as the key factors affecting the decision making process. Research limitations/implications – Although our sample is small and restricted to an Islamic Finance house in the UK the results are robust. Future research could consider enlarging the sample in the UK and also internationally allowing for cultural differences to be identified. The results indicate that the scoring models can be of great benefit to Islamic finance houses in regards to their decision making processes of accepting and rejecting new credit applications and thus improve their efficiency and effectiveness. Originality/value –Our contribution is the first to apply credit scoring modeling techniques in Islamic Finance. Also in building a scoring model our application applies a different approach by using accepted and rejected credit applications instead of good and bad credit histories. This identifies opportunity costs of misclassifying credit applications as rejected

The capital structure of banks and practice of bank restructuring : eight case studies on current bank restructurings in Europe ; final report

This study presents an empirical analysis of capital and liability management in eight cases of bank restructurings and resolutions from eight different European countries. It can be read as a companion piece to an earlier study by the author covering the specific bank restructuring programs of Greece, Spain and Cyprus during 2012/13. The study portrays for each case the timelines between the initial credit event and the (last) restructuring. It proceeds to discuss the capital and liability management activity before restructuring and the restructuring itself, launches an attempt to calibrate the extent of creditor participation as well as expected loss by government, and engages in a counterfactual discussion of what could have been a least cost restructuring approach. Four of the eight cases are resolutions, i.e. the original bank is unwound (Anglo Irish Bank, Amagerbanken, Dexia, Laiki), while the four other banks have de-facto or de-jure become nationalized and are awaiting re-privatization after the restructuring (Deutsche Pfandbriefbank/Hypo Real Estate, Bankia, SNS Reaal, Alpha Bank). The case selection follows considerations of their model character for the European bank restructuring and resolution policy discussion while straddling both the U.S. (2007 - 2010) and the European (2010 - ) legs of the financial crisis, which each saw very different policy responses...

Capital adequacy regulation of financial conglomerates in the European Union

Over the past few decades, changes in market conditions such as globalisation and deregulation of financial markets as well as product innovation and technical advancements have induced financial institutions to expand their business activities beyond their traditional boundaries and to engage in cross-sectoral operations. As combining different sectoral businesses offers opportunities for operational synergies and diversification benefits, financial groups comprising banks, insurance undertakings and/or investment firms, usually referred to as financial conglomerates, have rapidly emerged, providing a wide range of services and products in distinct financial sectors and oftentimes in different geographic locations. In the European Union (EU), financial conglomerates have become part of the biggest and most active financial market participants in recent years. Financial conglomerates generally pose new problems for financial authorities as they can raise new risks and exacerbate existing ones. In particular, their cross-sectoral business activities can involve prudentially substantial risks such as the risk of regulatory arbitrage and contagion risk arising from intra-group transactions. Moreover, the generally large size of financial conglomerates as well as the high complexity and interconnectedness of their corporate structures and risk exposures can entail substantial systemic risk and can therefore threaten the stability of the financial system as a whole. Until a few years ago, there was no supervisory framework in place which addressed a financial conglomerate in its entirety as a group. Instead, each group entity within a financial conglomerate was subject to the supervisory rules of its pertinent sector only. Such silo supervisory approach had the drawback of not taking account of risks which arise or aggravate at the group level. It also failed to consider how the risks from different business lines within the group interrelate with each other and affect the group as a whole. In order to address this lack of group-wide prudential supervision of financial conglomerates, the European legislator adopted the Financial Conglomerates Directive 2002/87/EC8 (‘FCD’) on 16 December 2002. The FCD was transposed into national law in the member states of the EU (‘Member States’) by 11 August 2004 for application to financial years beginning on 1 January 2005 and after. The FCD primarily aims at supplementing the existing sectoral directives to address the additional risks of concentration, contagion and complexity presented by financial conglomerates. It therefore provides for a supervisory framework which is applicable in addition to the sectoral supervision. Most importantly, the FCD has introduced additional capital requirements at the conglomerate level so as to prevent the multiple use of the same capital by different group entities. This paper seeks to examine to what extent the FCD provides for an adequate capital regulation of financial conglomerates in the EU while taking into account the underlying sectoral capital requirements and the inherent risks associated with financial conglomerates. In Part 1, the definition and the basic corporate models of financial conglomerates will be presented (I), followed by an illustration of the core motives behind the phenomenon of financial conglomeration (II) and an overview of the development of the supervision over financial conglomerates in the EU (III). Part 2 begins with a brief elaboration on the role of regulatory capital (I) and gives a general overview of the EU capital requirements applicable to banks and insurance undertakings respectively. A delineation of the commonalities and differences of the banking and the insurance capital requirements will be provided (II). It continues to further examine the need for a group-wide capital regulation of financial conglomerates and analyses the adequacy of the FCD capital requirements. In this context, the technical advice rendered by the Joint Committee on Financial Conglomerates (JCFC) as well as the currently ongoing legislative reforms at the EU level will be discussed (III). The paper finally closes with a conclusion and an outlook on remaining open issues (IV)

Does Non-linearity Matter in Retail Credit Risk Modeling?

In this research we propose a new method for retail credit risk modeling. In order to capture possible non-linear relationships between credit risk and explanatory variables, we use a learning vector quantization (LVQ) neural network. The model was estimated on a dataset from Slovenian banking sector. The proposed model outperformed the benchmarking (LOGIT) models, which represent the standard approach in banks. The results also demonstrate that the LVQ model is better able to handle the properties of categorical variables.retail banking, credit risk, logistic regression, learning vector quantization