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Purpose – This chapter investigates the influence of bank loan underwriting practices on loan losses and identifies potential determinants of lending practices for five categories of loans: business, consumer, commercial real estate, home equity, and construction and land development loans.

Methodology/approach – Using data on the riskiness of lending practices obtained from the U.S. Federal Deposit Insurance Corporation (FDIC) bank examiner surveys from January 1996 to March 2009, I fit a two-step treatment effects model to measure the effects of underwriting practices on loan losses, controlling for the potential endogeneity of lending practices.

Findings – In the selection step, I find that for business loans, the likelihood that bank management will adopt low-risk lending practices increases with bank financial performance and management quality hierarchical complexity and decreases with market competition. Results for the selection of lending practices for consumer loans and three categories of real estate loans are similar to those found for business loans but show weaker statistical relationships to all explanatory variables. In the loss determination step, I find that lower (higher) risk underwriting practices are generally associated with lower (higher) gross loan charge-offs (as percentage of gross loans and leases) for five categories of loans: business, consumer, commercial real estate, home equity, and construction and land development loans.

Originality/value of chapter – This is the first study to model the determinants of loan underwriting practices with the practices being characterized in terms of their risk to the bank. In addition, this is the first study to consider the effects of the riskiness of lending practices on loan losses, controlling for the endogeneity of practices.

Purpose – This chapter examines whether the underwriting processes of takaful operators for health takaful products conform to shariah.

Methodology/approach – A qualitative research method has been used for this study. Data have been collected from primary sources through interviews and from secondary sources by examining relevant documentation. Interviews were conducted with underwriters from takaful operators A, B, C and D. Interviews were also conducted with the shariah executives of these takaful operators and with shariah experts from two institutions – University A and Research Institute B – for their opinions on the underwriting process. This selection of respondents from those responsible for the underwriting process and from experts in shariah has assisted us in locating data for our research, based on their experience and the practices in which they are involved.

Findings – The study found that the underwriting process used by takaful operators was in accordance with Islamic principles.

Research limitations/implications – The study is limited to four takaful operators in Malaysia who have received awards for best operators; this study focuses on health takaful products.

Originality/value – This study provides a view about the process of risk selection which determines the contribution rate and whether a risk will be acceptable or not to the company.

Few issues in business ethics are as polarizing as the practice of risk classification and underwriting in the insurance industry. Theorists who approach the issue from a background in economics often start from the assumption that policy-holders should be charged a rate that reflects the expected loss that they bring to the insurance scheme. Yet theorists who approach the question from a background in philosophy or civil rights law often begin with a presumption against so-called “actuarially fair” premiums and in favor of “community rating,” in which everyone is charged the same price. This paper begins by examining and rejecting the three primary arguments that have been given to show that actuarially fair premiums are unjust. It then considers the two primary arguments that have been offered by those who wish to defend the practice of risk classification. These arguments overshoot their target, by requiring a “freedom to underwrite” that is much greater than the level of freedom enjoyed in most other commercial transactions. The paper concludes by presenting a defense of a more limited right to underwrite, one that grants the legitimacy of the central principle of risk classification, but permits specific deviations from that ideal when other important social goods are at stake.

Chen and Ritter (2000) documented that underwriter spreads for recent US initial public offerings (IPOs) in $20 million range as well as much larger IPOs in the $80 million range are clustered at 7%. This observation has led to a Department of Justice (DOJ) enquiry into potential price fixing by underwriters. We demonstrate through a times series analysis that IPOs have tripled in size and become much riskier over time. A pooled data analysis can therefore mask evidence of competition in the market. We find that spread clustering is not a recent phenomenon. Over time, clustering at 7% has increased as clustering above 7% has declined. IPO spreads have declined significantly over time as the firms going public more recently are riskier, underwriting efforts have increased and recent IPOs are much larger than IPOs in the past. Controlling for time trends, larger IPOs have lower average spreads. The market for underwriting IPOs seems to be competitive with entry of new firms during the hot markets.

This chapter empirically investigates how banks evaluate the creditworthiness of small- and medium-sized enterprises (SMEs). Following SME loan underwriting literature that distinguishes among different lending technologies, we test whether the typical SME bank loan is underwritten primarily based on just a single technology. We find that although financial statement lending is the most commonly used and serves as a kind of basic technology, it tends not to be used to the exclusion of other technologies. These findings imply that, at least in Japan, SME lending practice may be inconsistent with academic research on how banks underwrite loans elsewhere.

In this study, we seek to broaden the research focus in the strategic alliance literature from a firm's “partner strategy” to its “network strategy” by linking a firm's partnering choices to changes in its network position over time. Using data on all underwriting syndicates in Canada over nearly 40 years, we conceptualize and model the interplay between an investment bank's own and its partners’ syndicate participation. Our findings indicate that the lead banks, which have greater discretion in choosing syndicate partners than co-lead banks, are more likely to make partner selections that create bridging positions that provide access to timely and non-redundant information as well as opportunities to play a broker role across unconnected others. We also find, however, that lead banks’ bridging positions deteriorate when they form ties with other lead banks. Network-based competitive advantages are thus influenced by network opportunities and constraints as well as partner-specific concerns, suggesting that new insights into the dynamics of interfirm networks and competitive advantage of firms are possible within this broader view.

This chapter investigates whether non venture-backed, venture-backed and bridge financed companies going public on Germany’s Neuer Markt differ with regard to issuer characteristics, balance sheet data or offering characteristics. Moreover, this chapter contributes to the underpricing literature by focusing on the role of venture capitalists and underwriters in certifying the quality of a company. Companies backed by a prestigious venture capitalist and/or underwritten by a top bank are expected to show less underpricing at the Initial Public Offering (IPO) due to reduced ex-ante uncertainty. This analysis provides evidence to the contrary: VC-backed IPOs appear to be more underpriced than non VC-backed IPOs.

California is no stranger to disaster.

Introduction: Big data is that disruptive force that affects businesses, industries, and the economy. In 2021, insurance analytics will include more than simply analysing statistics. According to current trends, new insurance big data analytics (BDA) methods will enable firms to do more with their data. The insurance business has traditionally been conservative, but adopting new technology is no longer only a current trend; it must be competitive. Big data technologies aid in processing a huge amount of data, improve workflow efficiency, and lower operating costs.

Purpose: Some of the most recent developments in big data for insurance and how insurers may use the information to stay ahead of their competitors are discussed in this chapter. This chapter’s prime purpose is to analyse how artificial intelligence (AI), blockchain, and mobile technology change the outlook and working of the insurance sector.

Methodology: To achieve our research purpose, we analyse case studies and literature that emphasise how BDA revolutionises the insurance market. For this purpose, various articles and studies on BDA in the insurance market will be selected and studied.

Findings: From the analysis, we find that the use of big data in the insurance business is growing. The development of BDA has proven to be a game-changing technology in insurance, with a slew of benefits. The insurance sector is now grappling with the risks and opportunities that modern technology presents. Big data offers opportunities that every company must avail of. We can safely argue that big data has transformed the insurance sector for the better. The BDA’s consequences have enabled insurers to target clients more accurately. This chapter highlights that new tools and technologies of big data in the insurance market are increasing. AI is emerging as a powerful technology that can alter the entire insurance value stream. The transmission of any type of digital proof for underwriting, including the use of digital health data, might be a blockchain use case (electronic health record (EHR)). As digital forensics becomes easier to include in underwriting, it must expect price and product design changes in the future. In the future, the internet of things (IoT) and AI will combine to automate insurance processes, causing our sector to transform dramatically. We highlight that these technologies transformed insurance practices and revolutionalised the insurance market.

Interorganizational partner selection decisions are plagued with uncertainty. When making partnering decisions, firms strive to answer two questions: does the prospective partner have resources which can be used to generate value in the relationship; and will the partner be willing to actively share these resources and cooperate in good faith? Answers to these questions help reduce three types of uncertainty – partner capability uncertainty, partner competitiveness uncertainty and partner reliability uncertainty. For a relationship to benefit both partners, they have to possess complimentary resources of comparable quality, avoid explicit competition as well as be willing to engage in the cooperative behaviors within the confines of their relationship. In this paper, we examine the importance of prospective partners’ characteristics (differences in size, status and specialization) as well as their network characteristics (existence of a common partner and membership in the same clique) to the formation and longevity of their social relationships, as these characteristics reduce firms’ value generation and partner reliability uncertainty.