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Although access to financing in Pakistan is expanding quickly, it is two to four times lower than regional benchmarks. Half of Pakistani adults, mostly women, do not engage with the financial system at all, and only 14 percent have access to formal services. Credit for small- and medium-size enterprises is rationed by the financial system. The formal microfinance sector reaches less than 2 percent of the poor, as opposed to more than 25 percent in neighboring countries. Yet it is the micro- and small businesses, along with remittances, that help families escape the poverty trap and participate in the economy. 'Bringing Finance to Pakistan's Poor' is based on a pioneering and comprehensive survey and dataset that measures the access to financial products by Pakistani households. The survey included 10,305 households in all areas of the country, excluding the tribal regions. The accompanying CD contains summary statistics. The authors develop a picture of access to and usage of financial services across the country and across different population groups, and they identify policy and regulatory priorities. Reform measures in Pakistan have been timely, but alone are not enough; financial institutions have lagged behind in adopting technology, segmenting customer bases, diversifying products, and simplifying processes and procedures. Gender bias and low levels of financial literacy remain barriers, as is geographical remoteness. However, the single strongest cause of low financial access is lack of income—not location, education, or even gender. 'Bringing Finance to Pakistan's Poor' will be of great interest to readers working in the areas of business and finance, economic policy, gender and rural development, and microfinance.

In the spring of 2012, the Royal Monetary Authority of Bhutan and the World Bank commissioned a diagnostic assessment of financial practices and strategies among urban and rural Bhutanese. The resulting survey, the Bhutan financial inclusion focus group survey, represents one of the first efforts to capture household financial management practices in the country. The assessment, undertaken at the request of a government working group led by the Royal Monetary Authority, was designed to inform Bhutan's Financial Inclusion Policy by providing information about households' use of and demand for financial services. Since the research mainly captures the perspectives of Bhutanese households, this report does not present recommendations. Instead, its findings from the field research provide qualitative evidence that has informed the financial inclusion policy by highlighting opportunities and challenges in increasing financial inclusion.

This report brings together the results of an eight-part series of presentations by leading experts in issues directly related to microfinance institutional sustainability. It is intended for microfinance institution (MFI) board members, managers, and staff members as well as for government regulators, supervisors, and donor staff members. The first four chapters include topics in risk management: (1) risk management systems, (2) good governance, (3) interest rates, and (4) micro-insurance. The last four chapters include four topics in new product development and efficient delivery methodologies: (5) housing microfinance, (6) micro-leasing, (7) disaster preparedness products and systems, and (8) new technologies. The objectives of the series were as follows: i) to strengthen MFIs by disseminating innovative approaches in risk management, cost control, governance, and new technologies; ii) to promote a South-South exchange of experiences and lessons learned; iii) to promote greater ties among the MFIs in the region and between MFIs and government supervisors and regulators; and iv) to highlight the Bank's ability to mobilize international technical expertise in microfinance.

The long-awaited, comprehensive guide to practical credit risk modeling Credit Risk Analytics provides a targeted training guide for risk managers looking to efficiently build or validate in-house models for credit risk management. Combining theory with practice, this book walks you through the fundamentals of credit risk management and shows you how to implement these concepts using the SAS credit risk management program, with helpful code provided. Coverage includes data analysis and preprocessing, credit scoring; PD and LGD estimation and forecasting, low default portfolios, correlation modeling and estimation, validation, implementation of prudential regulation, stress testing of existing modeling concepts, and more, to provide a one-stop tutorial and reference for credit risk analytics. The companion website offers examples of both real and simulated credit portfolio data to help you more easily implement the concepts discussed, and the expert author team provides practical insight on this real-world intersection of finance, statistics, and analytics. SAS is the preferred software for credit risk modeling due to its functionality and ability to process large amounts of data. This book shows you how to exploit the capabilities of this high-powered package to create clean, accurate credit risk management models. * Understand the general concepts of credit risk management * Validate and stress-test existing models * Access working examples based on both real and simulated data * Learn useful code for implementing and validating models in SAS Despite the high demand for in-house models, there is little comprehensive training available; practitioners are left to comb through piece-meal resources, executive training courses, and consultancies to cobble together the information they need. This book ends the search by providing a comprehensive, focused resource backed by expert guidance. Credit Risk Analytics is the reference every risk manager needs to streamline the modeling process.

Currently, in the banking sector of the economically developed countries is possible to monitor the trend of high liquidity and the low volume of credit financing of Small and Medium-sized Enterprises (SMEs). This situation is far from ideal because the banks are losing potential revenue and the SME segment within appropriate extent cannot finance its business objectives through bank loans. The aim of the paper was to propose a model for a comprehensive assessment of the credit worthiness of the client and retrospective evaluation of bank lending in terms of unrealized income and loss prevention, resulting from the application of exit strategies in the SME segment. The aim of this article was to propose a model for comprehensive assessment of the credit worthiness of the client and retrospective evaluation of bank lending in terms of unrealized income and loss prevention, resulting from the application of exit strategies in the SME segment. Our most important finding is that there is potentially quite a large group of clients who have problems with the bank ratings, but despite the fact that the bank does not give them credit, these companies continue to remain on the market. Another important finding is that there is the possibility of improving the loan process in the SME segment. These options are incorporated in our proposal to the loan process.

This book's prime audience is government policy-makers. It provides a policy framework for governments to increase micro, small and medium enterprises' access to financial services?one which is based on empirical evidence from around the world. Financial sector policies in many developing countries often work against the ability of commercial financial institutions to serve this market segment, albeit, often unintentionally. The framework guides governments on how to best focus scarce resources on three things: ? developing an inclusive financial sector policy; ? building healthy financial institutions; and ? investing in information infrastructure such as credit bureaus and accounting standards. The book provides examples and case studies of how such a strategy has helped to build more inclusive financial institutions and systems in many countries.

This edited volume contains eight studies of financial sector challenges in Africa that served as background studies for Financing Africa: Through the Crisis and Beyond. One of the major challenges for African financial systems is to expand financial services to a larger share of the population. The chapters in this area cover microfinance in Africa, the role of technology, reforms of payment infrastructure, and financing agriculture. Two chapters cover challenges in increasing long-term finance; one covers housing finance and the other the role of sovereign wealth fund. The book also contains a detailed discussion of bank regulation and supervision, especially in light of the current regulatory reforms in Europe and North America. The final chapter provides a political economy perspective, discussing the conditions for activist government policies in the financial sector.

We examine whether syndicated loans securitized through collateralized loan obligations (CLOs) have more standardized financial covenants. We proxy for the standardization of covenants using the textual similarity of their contractual definitions. We find that securitized loans are associated with higher covenant standardization than nonsecuritized institutional loans. In addition, we show that CLOs with more diverse or frequently rebalanced portfolios are more likely to purchase loans with standardized covenants, potentially because standardization alleviates information processing costs related to loan monitoring and screening. We also document that covenant standardization is associated with greater loan and CLO note rating agreement between credit rating agencies, further supporting the relation between lower information costs and covenant standardization. Overall, our study provides evidence that loan securitization is related to the design of standardized financial covenants.

Sustaining operational productivity in the completion of repetitive tasks is critical to many organizations' success. Yet research points to two different work-design-related strategies for accomplishing this goal: specialization to capture the benefits of repetition and variety (i.e., working on different tasks) to keep workers motivated and provide them opportunities to learn. In this paper, we investigate how these two strategies may bring different productivity benefits over time. For our empirical analyses, we use two and a half years of transaction data from a Japanese bank's home loan application-processing line. We find that over the course of a single day, specialization, as compared to variety, is related to improved worker productivity. However, when we examine workers' experience across a number of days, we find that variety helps improve worker productivity. Additionally, we show that part of this benefit results from workers' cumulative experience with changeovers. Our results highlight the need for organizations to transform specialization and variety into mutually reinforcing strategies rather than treating them as mutually exclusive. Overall, our paper identifies new ways to improve operational performance through the effective allocation of work. This paper was accepted by Christian Terwiesch, operations management.

Computers are now involved in many economic transactions and can capture data associated with these transactions, which can then be manipulated and analyzed. Conventional statistical and econometric techniques such as regression often work well, but there are issues unique to big datasets that may require different tools. First, the sheer size of the data involved may require more powerful data manipulation tools. Second, we may have more potential predictors than appropriate for estimation, so we need to do some kind of variable selection. Third, large datasets may allow for more flexible relationships than simple linear models. Machine learning techniques such as decision trees, support vector machines, neural nets, deep learning, and so on may allow for more effective ways to model complex relationships. In this essay, I will describe a few of these tools for manipulating and analyzing big data. I believe that these methods have a lot to offer and should be more widely known and used by economists.