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Scoring is a widely used, long-established, and universally applicable method of measuring risks, especially those that are difficult to quantify. Unfortunately, the scoring method is often misused in real estate practice and underestimated in academia. The purpose of this paper is to supplement the literature with general rules under which scoring systems should be designed and validated, so that they can become reliable risk instruments.

The paper combines the rules, or axioms, for coherent risk measures known from the literature with those for scoring instruments. The result is a system of rules that a risk scoring system should fulfil. The approach is theoretical, based on a literature survey and reasoning.

At first, the paper clarifies that a risk score should express the variation of a property’s yield and not of its quality, as it is often done in practice. Then the axioms for a coherent risk scoring are derived, e.g. the independence of the risk factors. Finally, the paper proposes procedures for valid and reliable risk scoring systems, e.g. the out-of-time validation.

Although it is a theoretical work, the paper also focuses on practical applicability. The findings are illustrated with examples of scoring systems.

Rules for risk measures and for scoring systems have been established long ago, but the combination is a first. In this way, the paper contributes to real estate risk research and risk management practice.

This chapter shows how to identify the characteristics of borrowers that are part of a credit scoring model. The credit risk scoring model is an important tool for evaluating credit risk associated with customer characteristics that affect defaults. This research was conducted at a financial institution, a subsidiary of a commercial bank in Indonesia, to answer the challenge of determining the feasibility of providing financing quickly and accurately. This model uses a logistic regression method based on customer data with indicators of demographic characteristics, assets, occupations, and financing payments. This study identifies nine variables that meet the goodness of fit criteria, which consist of WOE, IV, and p-value. The nine variables can be used as predictors of default probability: type of work, work experience, net finance value, tenor, car brand, asset price, percentage of down payment (DP), interest, and income. The results of the study form a risk assessment model to identify variables that have a significant effect on the probability of default.

Lenders in online peer-to-peer (P2P) lending platforms are always non-experts and face severe information asymmetry. This paper aims to achieve the goals of gaining high returns with risk limitations or lowering risks with expected returns for P2P lenders.

This paper used data from a leading online P2P lending platform in America. First, the authors constructed a logistic regression-based credit scoring model and a linear regression-based profit scoring model to predict the default probabilities and profitability of loans. Second, based on the predictions of loan risk and loan return, the authors constructed linear programming model to form the optimal loan portfolio for lenders.

The research results show that compared to a logistic regression-based credit scoring method, the proposed new framework could make more returns for lenders with risks unchanged. Furthermore, compared to a linear regression-based profit scoring method, the proposed new framework could lower risks for lenders without lowering returns. In addition, comparisons with advanced machine learning techniques further validate its superiority.

Unlike previous studies that focus solely on predicting the default probability or profitability of loans, this study considers loan allocation in online P2P lending as an optimization research problem using a new framework based upon modern portfolio theory (MPT). This study may contribute theoretically to the extension of MPT in the specific context of online P2P lending and benefit lenders and platforms to develop more efficient investment tools.

The purpose of this research is to investigate whether inclusion of risk assessment variables in the multiple discriminant analysis (MDA) model improved the banks ability in making correct customer classification, predict firm's performance and credit risk assessment.

The paper reviews literature on the application of financial distress and credit scoring methods, and the use of risk assessment variables in classification models. The study used a sample of 56 performing and non‐performing assets (NPA) of a privatized commercial bank in Tanzania. Financial ratios were used as independent variables for building the MDA model with a variation of five MDA models. Different statistical tests for normality, equality of covariance, goodness of fit and multi‐colinearity were performed. Using the estimation and validation samples, test results showed that the MDA base model had a higher level of predictability hence classifying correctly the performing and NPA with a correctness of 92.9 and 96.4 percent, respectively. Lagging the classification two years, the results showed that the model could predict correctly two years in advance. When MDA was used as a risk assessment model, it showed improved correct customer classification and credit risk assessment.

The findings confirmed financial ratios as good classification and predictor variables of firm's performance. If the bank had used the MDA for classifying and evaluating its customers, the probability of failure could have been known two years before actual failure, and the misclassification costs could have been calculated objectively. In this way, the bank could have reduced its non‐performing loans and its credit risk exposure.

The valiadation sample used in the study was smaller compared to the estimation sample. MDA works better as a credit scoring method in the banking environment two years before and after failure. The study was done on the current financial crisis of 2009.

Use of MDA helps banks to determine objectively the misclassification costs and its expected misclassification errors plus determining the provisions for bad debts. Banks could have reduced the non‐performing loans and their credit risks exposure if they had used the MDA method in the loan‐evaluation and classification process. The study has proved that quantitative credit scoring models improve management decision making as compared to subjective assessment methods. For improved credit and risk assessment, a combination of both qualitative and quantitave methods should be considered.

The findings have shown that using the MDA, commercial banks could have improved their objective decision making by correctly classifying the credit worthiness of a customer, predicting firm's future performance as well as assessing their credit risk. It has also shown that other than financial variables, inclusion of stability measures improves management decision making and objective provisioning of bad debts. The recent financial crisis emphasizes the need for developing objective credit scoring methods and instituting prudent risk assessment culture to limit the extent and potential of failure.

In machine learning applications, and in credit risk modeling in particular, model performance is usually measured by using cumulative accuracy profile (CAP) and receiving operating characteristic curves. The purpose of this paper is to use the statistics of the CAP curve to provide a new method for credit PD curves calibration that are not based on arbitrary choices as the ones that are used in the industry.

The author maps CAP curves to a ball–box problem and uses statistical physics techniques to compute the statistics of the CAP curve from which the author derives the shape of PD curves.

This approach leads to a new type of shape for PD curves that have not been considered in the literature yet, namely, the Fermi–Dirac function which is a two-parameter function depending on the target default rate of the portfolio and the target accuracy ratio of the scoring model. The author shows that this type of PD curve shape is likely to outperform the logistic PD curve that practitioners often use.

This paper has some practical implications for practitioners in banks. The author shows that the logistic function which is widely used, in particular in the field of retail banking, should be replaced by the Fermi–Dirac function. This has an impact on pricing, the granting policy and risk management.

Measuring credit risk accurately benefits the bank of course and the customers as well. Indeed, granting is based on a fair evaluation of risk, and pricing is done accordingly. Additionally, it provides better tools to supervisors to assess the risk of the bank and the financial system as a whole through the stress testing exercises.

The author suggests that practitioners should stop using logistic PD curves and should adopt the Fermi–Dirac function to improve the accuracy of their credit risk measurement.

To construct a risk score using both clinical and intra-oral variables and to determine a risk score to screen individuals according to their risk of hyperglycemia.

A cross-sectional study was carried out among 690 Thai dental patients who visited the Special Clinic, Faculty of Dentistry, Mahidol University and a mobile dental unit of His Majesty the King of Thailandss Dental Service Unit. Participants aged ≥25 years without a previous history of type 2 diabetes mellitus were included in the study. Participants diagnosed with severe anemia and polycythemia were excluded. Questionnaires were used to collect demographic data. Point-of-care HbA1c, body mass index (BMI), blood pressure and periodontal status were analyzed.

A total of 690 participants were included in the study. A risk scoring system including five variables was developed. It exhibited fair discrimination (area under the curve = 0.72, 95%CI 0.68–0.71). The risk score value of 9 was used as the cut-off point for increased risk of abnormal HbA1c. Subjects that had a total risk score of 9 or more had a high probability of having abnormal HbA1c and were identified for referral to physicians for further investigation and diagnosis.

A risk score to predict hyperglycemia using a dental parameter was developed for convenient evaluation in dental clinics.

Aims to examine financial risk management. The UK valuation profession has been criticised for inconsistencies and failures to reflect risk and uncertainty in certain valuation assignments such as the pricing of urban regeneration land. Also the Investment Property Forum/Investment Property Databank specifically concluded that a new approach is needed which combines conventional analysis of returns uncertainty with a more comprehensive survey of business risks. This debate has been brought into sharper focus by the publication of the Carsberg Report, which emphasised the need for more acceptable methods of expressing uncertainty, particularly when pricing in thin markets.

The paper commences with an examination of risk analysis within investment decision making and the property industry, drawing on the findings of the most recent literature that assesses the utilisation of risk management approaches.

Financial risk management is examined and the workings of the D&B credit rating model illustrated. The paper explains the decision‐making framework within which the property risk score is applied.

The aim of this paper is to present an alternative paradigm for the reporting of risk based on techniques utilised within business applications. In particular it applies a standard credit‐rating technique, based on the D&B model, to report the level of risk within property pricing – property risk scoring (PRS).

Considered the largest investment for most households, buying a house requires careful and transparent analysis by all parties involved in the transaction. The aim of this paper is to propose a methodological framework allowing for the readjustment of trade-offs among risk evaluation criteria, considered of extreme importance in the lending decision process of mortgage loans.

Multiple criteria decision analysis (MCDA) has proved over the years to be effective and versatile in handling compensations among criteria. Measuring attractiveness is applied by a categorical based evaluation technique (MACBETH) to a pre-established structure of credit-scoring criteria for mortgage lending risk evaluation. This pre-established structure is currently used by one of the largest banks in Portugal.

The framework allowed the authors to provide the credit experts who participated in the study with a more informed, transparent and accurate mortgage-lending risk-evaluation system. The sensitivity and robustness analyses carried out also helped in promoting discussion and supporting the readjustments made.

The study shows the usefulness of using the MACBETH approach to assist credit analysts in making better informed decisions, and opens avenues for further research. However, due to the dependence on the participants involved, extrapolations without proper caution are discouraged.

The credit analysts who participated in this study considered the framework more discerning in terms of Basel directives.

The integration of MACBETH and credit-scoring mechanisms holds great potential for risk assessment and decision support. No prior work reporting the application of MACBETH in terms of mortgage-lending risk-evaluation is known.

A good decision support system for credit scoring enables telecom operators to measure the subscribers' creditworthiness in a fine-grained manner. This paper aims to propose a robust credit scoring system by leveraging latent information embedded in the telecom subscriber relation network based on multi-source data sources, including telecom inner data, online app usage, and offline consumption footprint.

Rooting from network science, the relation network model and singular value decomposition are integrated to infer different subscriber subgroups. Employing the results of network inference, the paper proposed a network-aware credit scoring system to predict the continuous credit scores by implementing several state-of-art techniques, i.e. multivariate linear regression, random forest regression, support vector regression, multilayer perceptron, and a deep learning algorithm. The authors use a data set consisting of 926 users of a Chinese major telecom operator within one month of 2018 to verify the proposed approach.

The distribution of telecom subscriber relation network follows a power-law function instead of the Gaussian function previously thought. This network-aware inference divides the subscriber population into a connected subgroup and a discrete subgroup. Besides, the findings demonstrate that the network-aware decision support system achieves better and more accurate prediction performance. In particular, the results show that our approach considering stochastic equivalence reveals that the forecasting error of the connected-subgroup model is significantly reduced by 7.89–25.64% as compared to the benchmark. Deep learning performs the best which might indicate that a non-linear relationship exists between telecom subscribers' credit scores and their multi-channel behaviours.

This paper contributes to the existing literature on business intelligence analytics and continuous credit scoring by incorporating latent information of the relation network and external information from multi-source data (e.g. online app usage and offline consumption footprint). Also, the authors have proposed a power-law distribution-based network-aware decision support system to reinforce the prediction performance of individual telecom subscribers' credit scoring for the telecom marketing domain.

This study aims to investigate the level of reporting compliance in terms of content elements, measure to what extent each content element of the integrated reporting (IR) framework is linked to value creation and demonstrate the relationship between the level of compliance and value creation linkages.

The sample for this study consists of 12 companies, 11 of which are public and 1 is non-public. The data is obtained from the Integrated Reporting Turkey Network founded in 2015 in Turkey. This study applies a holistic approach integrating two different content analysis methods. First, a multi-weighted scoring system is constructed by using the IR content elements and the previously developed indexes in the literature. Second, in-depth, sentence-by-sentence content analysis is used to determine the relation between the content elements and value creation.

The results of the multi-weighted scoring system indicate a high level of compliance in the banking sector. On the other hand, the scores of the content analysis demonstrate higher scores in the disclosures of “basis of preparation and presentation”, “organizational overview and external environment”, “strategy and resource allocation”, “performance” and “business model” elements, while lower scores in the elements of “risk and opportunities” and “outlook.” The lowest compliance level associated with lower content analysis scores may indicate a low level of value creation potential. Consequently, this two-stage scoring is critical, as it clarifies the relation between compliance level and the explanatory power of each content element from a value creation perspective.

This study aims to support the policymakers and regulators in highlighting the importance of measuring and reporting value. Furthermore, it intends to encourage companies to produce reports that increase the value relevance of accounting information to contribute to the development of capital markets. The current literature includes research that mainly concentrates only on the quality or extent of IR disclosure practices. This study offers a combined analysis that helps to determine at what level a company has accomplished the expectations of the International Integrated Reporting Council in terms of both the content and the value creation potential.