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The purpose of the study is to examine the use of alternative information in bank lending to small and medium enterprises (SMEs). Understanding alternative information and its use in bank lending to SMEs is important because it has become a growing part of the future of SME finance. The results and findings of my study not only enrich the finance literature but, more importantly, also address the use of Fintech in the risk management of SME lending, a new and complex problem that is specific to both the information technology and finance field.

To answer the research question, the author used a case study approach that relies upon qualitative data and analysis. By iterating between the existing literature, theoretical pieces and empirical findings, the author explain and interpret in detail how the use of alternative information impacts loan outcomes and develop insights to guide future research.

The case is outlined in two time periods including the prepartnership period and the postpartnership period. It highlights the establishment of a partnership between LoanBank and FintechInc (pseudonym), aimed at SME-focused Fintech lending. The findings underscore how the partnership has enabled a mutually beneficial situation where LoanBank and FintechInc leverage each other’s strengths to provide efficient and effective lending services. The adoption of alternative information in the risk management Fintech (RMF) platform of FintechInc has transformed LoanBank’s lending processes, showcasing how technological innovations can enhance SME lending practices.

The study’s originality mainly lies in the three detailed insights regarding alternative information’s impact on SME lending: information, platform properties and financial inclusion. The information part demonstrates that RMF platforms expand the information used for lending decisions, shifting from traditional hard and soft data to incorporating various alternative information sources. The platform properties part suggests that location, openness and technology also play a pivotal role in shaping lending outcomes. Finally, the financial inclusion part proposes that the use of alternative information has the potential to improve financial inclusion and offer better credit terms to previously underserved borrowers.

Alternative data is a term describing the data exhaust that organizations, especially asset managers, are using to develop insights about companies to give them a trading edge. As the use of this data becomes more prevalent, it is critical that business leaders understand how this kind of data can be used against their organizations. This viewpoint articulates some of the steps they will need to take to do this.

The methodology used in this viewpoint is a review of recent literature covering alternative data and its uses.

This paper describes the different ways in which alternative data is being used and cites surprising examples of how this can make companies vulnerable or threaten their reputation.

As an overview of selected examples from secondary sources, this paper is not a comprehensive treatment of the subject.

By studying the issues raised in the paper, business leaders can arm themselves with insights into the use of alternative data and mitigate reputational fallout from its use against their companies.

A better understanding of how alternative data is being used can help protect both individuals and social organizations from being treated inequitably and increase transparency in the use of large and hidden data sets.

To the best of the author’s knowledge, this is the first treatment of the use of alternative data from the perspective of corporate reputation.

As one of the increasingly important alternative property sectors, data centres are a technology-focused property sector that is taking advantage of the growing investment intensity in technology-related infrastructure, against the backdrop of constant innovation and advancement in technology. The purpose of this paper is to assess the preliminary risk-adjusted performance and portfolio diversification benefits of data centre Real Estate Investment Trusts (REITs) in the USA, Australia and Singapore. The strategic implications going forward for data centres as an innovative property sector in the property investment space are also highlighted.

Using monthly total returns, the average annual return, annual risk, risk-adjusted performance and portfolio diversification benefits of data centre REITs in the USA, Australia and Singapore over 2016–2018 are assessed. Optimal asset allocation analysis is performed to investigate the value-added role of data centre REITs in a mixed-asset portfolio.

Data centre REITs delivered strong average annual return performance, outperforming the composite REITs in all three markets. This also sees data centre REITs being riskier than the overall REIT sector due to the non-traditional and maturing status of the data centre property sector. On a risk-adjusted basis, competitive performance was recorded for data centre REITs, with data centre REITs in the USA and Singapore outperforming their respective composite REITs. This performance is also delivered with significant portfolio diversification benefits with the stock market, resulting in data centre REITs contributing to the US mixed-asset portfolios across a diverse risk spectrum.

Institutional investors are now giving increased emphasis to alternative property sectors with better risk-return trade-offs. Improved performance and diversification benefits are achieved by supplementing existing property portfolios with non-traditional property sectors with counter-cyclical risk-return profiles, one of which is the data centre property sector. This sees data centres as an important alternative property sector, having technology-based drivers and being recognised as having a clear path towards institutionalisation with the major investors in the near future.

This paper is the first published empirical research analysis that specifically assessed the preliminary performance and diversification benefits of data centre REITs in the USA, Australia and Singapore. This research enables empirically validated, more informed and practical property investment decision making by institutional investors regarding the future strategic role of the data centre property sector as an innovative sector in the institutional property investment space.

Present study aimed to integrate Geographic Information Systems (GIS) and Building Information Modeling (BIM) in conjunction with multicriteria decision-making (MCDM) to enhance infrastructure investment planning.

This analysis combines GIS databases with BIM simulations for a novel highway project. Around 150 potential alternatives were simulated, narrowed to 25 more effective routes and 3 options underwent in-depth analysis using PROMETHEE method for decision-making, based on environmental, cost and safety criteria, allowing for comprehensive cross-perspective comparisons.

A comprehensive framework proposed was validated through a case study. Demonstrating its adaptability with customizable parameters. It aids decision-making, cost estimation, environmental impact analysis and outcome prediction. Considering these critical factors, this study holds the potential to advance new techniques for assessment and planning railways, power lines, gas and water.

The study acknowledges limitations in GIS data quality, particularly in underdeveloped areas or regions with limited technology access. It also overlooks other pertinent variables, like social, economic, political and cultural issues. Thus, conclusions from these simulations may not entirely represent reality or diverse potential scenarios.

The proposed method automates decision-making, reducing subjectivity, aids in selecting effective alternatives and considers environmental criteria to mitigate negative impacts. Additionally, it minimizes costs and risks while demonstrating adaptability for assessing diverse infrastructures.

By integrating GIS and BIM data to support a MCDM workflow, this study proposes to fill the existing research gap in decision-making prioritization and mitigate subjective biases.

Presents an investigation of the perennial problem of collecting valid empirical data on police vehicle pursuits, an organizationally sensitive and often controversial behavior, through a new data collection strategy using police emergency radio transmissions. Analyzes taped vehicle pursuits recorded on the Illinois State Police Emergency Radio Network and codes them for content as a data source on police vehicle pursuits. Compares these radio‐transmission data with more conventional pursuit data collection methods, e.g. administrative/official data, elicited‐pursuit‐reporting‐form data, and officer‐self‐report data. Evaluates these as an alternative or supplemental data window for empirically studying the incidence and content of police vehicle pursuits. While some differences appear, results from emergency‐channel radio transmission data largely converge with earlier findings from more conventional data collections. Divergent findings, which are few, appear to be largely the artifacts of different samplings of pursuits that the different data collection methods yield, rather than a result of differential validity.

We investigate the Bayesian approach to model comparison within a two-country framework with nominal rigidities using the workhorse New Keynesian open-economy model of Martínez-García and Wynne (2010). We discuss the trade-offs that monetary policy – characterized by a Taylor-type rule – faces in an interconnected world, with perfectly flexible exchange rates. We then use posterior model probabilities to evaluate the weight of evidence in support of such a model when estimated against more parsimonious specifications that either abstract from monetary frictions or assume autarky by means of controlled experiments that employ simulated data. We argue that Bayesian model comparison with posterior odds is sensitive to sample size and the choice of observable variables for estimation. We show that posterior model probabilities strongly penalize overfitting, which can lead us to favor a less parameterized model against the true data-generating process when the two become arbitrarily close to each other. We also illustrate that the spillovers from monetary policy across countries have an added confounding effect.

The purpose of this paper is to discuss the results of an effort to use social media generated data for measuring patient satisfaction with medical care services. Traditionally, scientifically designed patient satisfaction surveys are used to provide such measurements. The goal here is to evaluate the possibility of supplementing patient satisfaction surveys with social media generated patient satisfaction measurements such that the later can be used either as validation or replacement for the former. Although surveys are scientifically designed to yield dependable results, recent studies have revealed multiple factors relating to the methods currently used for survey data collection, that may be contributing to the limitations of many survey results. In light of such criticisms, this study explored the possibility of using the increasing popular and proactively generated consumer ratings through the pervasive social media as data source for satisfaction measurement. The average satisfaction scores created from such data are then used to compare levels of satisfaction among five types of health service businesses.

The data used in this research are garnered from the consumer review social media site called “Yelp!”. Ratings and reviews that are related to health and medical services were extracted from the “Yelp!” database. The types of services that are identified by consumers are standardized to typologies that are traditionally used in health service research. Five types of services were targeted – general practice physician offices, physician specialty services, dentists, hospitals and physical therapy services. The “five-star” rating systems were re-coded to form a five-point ordinal scale variable to represent “satisfaction score”.

The Yelp! data-based measurement of patient satisfaction produced an overall satisfaction score of 3.8 (SD=1.7) for the sampled services. The average satisfaction score per type of service ranged from 3.16 (SD=1.83) for specialty physicians to 4.52 (SD=1.57) for physical therapists. In general, dentists and physical therapists received higher average satisfaction scores as compared to the other medical services.

Because this study was meant to evaluate the utility of social media generated data to measure satisfaction, in general, the estimates cannot be construed as representative of any underlying geographically defined population. They, however, do have a “cohort” interpretability. This limitation is not inherent to the use of the data source. If some geographically identifiable representation of the measurement data is desired, identifiable business data can be generated from the Yelp! system to specifically target relevant populations following the method that are tested in this study.

Under certain circumstances, such as the size and maturity of the gathered data, social media generated data can be a useful as a “fortuitous” alternative to satisfaction surveys for evaluating patient satisfaction with medical care. This is propitious as there have been some indication by studies that the advent of communication media in the twenty-first century may be undermining the reliability of scientifically designed surveys.

The use of social media generated data as “alternative” or “secondary” data source for research use is currently being widely investigated. To the author’s knowledge, this is the only paper that evaluated the use of “Yelp!” data as a possible source for population-based formal satisfaction measurement for healthcare services.