Assessing the impact of derived behavior information on customer attrition in the financial service industry

The value of the customer has been widely recognized in terms of financial planning and efficient resource allocation including the financial service industry. Previous studies have shown that directly observable information can be used in order to make reasonable predictions of customer attrition probabilities. However, these studies do not take full account of customer behavior information. In this paper, we demonstrate that efficient use of information can add value to financial services industry and improve the prediction of customer attrition. To achieve this, we apply an orthogonal polynomial approximation analysis to derive unobservable information, which is then used as explanatory variables in a probit–hazard rate model. Our results show that derived information can help our understanding of customer attrition behavior and give better predictions. We conclude that both researchers and the financial service industry should gather and use derived financial information in addition to directly observable information.     

Stress testing credit card portfolios: an application in South Africa

Motivated by a real problem, this study aims to develop models to conduct stress testing on credit card portfolios. Two modelling approaches were extended to include the impact of lenders’ actions within the model. The first approach was a regression model of the aggregate losses based on economic variables with autocorrelations of the errors. The second approach was a set of vintage-level models that highlighted the months-on-book effect on credit losses. A case study using the models was described using South African credit card data. In this case, the models were used to stress test the credit card portfolio under several economic scenarios.     

The economy and loss given default: evidence from two UK retail lending data sets

Loss given default (LGD) models predict losses as a proportion of the outstanding loan, in the event a debtor goes into default. The literature on corporate sector LGD models suggests LGD is correlated to the economy and so changes in the economy could translate into different predictions of losses. In this work, the role of macroeconomic variables in loan-level retail LGD models is examined by testing the inclusion of macroeconomic variables in two different retail LGD models: a two-stage model for a residential mortgage loans data set and an ordinary least squares model for an unsecured personal loans data set. To improve loan-level predictions of LGD, indicators relating to the macroeconomy are considered with mixed results: the selected macroeconomic variable seemed able to improve the predictive performance of mortgage loan LGD estimates, but not for personal loan LGD. For mortgage loan LGD, interest rate was most beneficial but only predicted better during downturn periods, underestimating LGD during non-downturn periods. For personal loan LGD, only net lending growth is statistically significant but including this variable did not bring any improvement to R².     

Electronic transition dipole moment functions for NaK

Phase-consistent electronic transition dipole moment functions were calculated (as a function of internuclear separation) for the 80 dipole selection rule allowed transitions in the manifold of states of NaK treated previously [W. J. Stevens, D. D. Konowalow, and L. B. Ratcliff, (1984) J. Chem. Phys. 80, 1215–1224]. Agreement was found with experimental determinations of the 2¹Π-1¹Σ⁺ transition. Agreement of the asymptotic values with experimental and previous theoretical determinations of corresponding atomic transition moments suggests that the present computations may be relatively reliable. The positions of a number of satellite bands are predicted from the potential energy difference curves.     

Effect of sample handling on alkaloid and mineral content of aqueous extracts of greater celandine (Chelidonium majus L.)

The authors examined the extraction of alkaloids from the greater celandine (Chelidonium majus L.) by different methods (traditional pressing and tea making, microwave and supercritical fluid extraction). The extractants were water and propylene glycol. For comparison of the extraction methods, the yield was evaluated according to total alkaloid content measured by spectroscopy. The highest alkaloid yield was obtained by microwave extraction and by making tea. Distribution of the components was studied by thin-layer chromatography and densitometry. The concentration and the ratio of alkaloid components in extracts are significantly different depending on the extraction method. The solution obtained by supercritical fluid extraction contains coptisine and chelidonine, while berberine could be obtained by microwave extraction only. Extracts with high coptisine content were obtained by supercritical fluid extraction, followed by pressing and microwave extraction. Mineral element content of the drug and extracts was also determined by inductively coupled plasma atomic emission spectrometry. Element content (Na, Ca, Fe) was found to be highest in microwave extracts.     

Measurement uncertainty from physical sample preparation: estimation including systematic error

A methodology is proposed, which employs duplicated primary sampling and subsequent duplicated physical preparation coupled with duplicated chemical analyses. Sample preparation duplicates should be prepared under conditions that represent normal variability in routine laboratory practice. The proposed methodology requires duplicated chemical analysis on a minimum of two of the sample preparation duplicates. Data produced from the hierarchical design is treated with robust analysis of variance (ANOVA) to generate uncertainty estimates, as standard uncertainties ('u' expressed as standard deviation), for primary sampling (ssamp), physical sample preparation (sprep) and chemical analysis (sanal). The ANOVA results allow the contribution of the sample preparation process to the overall uncertainty to be assessed. This methodology has been applied for the first time to a case study of pesticide residues in retail strawberry samples. Duplicated sample preparation was performed under ambient conditions on two consecutive days. Multi-residue analysis (quantification by GC-MS) was undertaken for a range of incurred pesticide residues including those suspected of being susceptible to loss during sample preparation procedures. Sampling and analytical uncertainties dominated at low analyte concentrations. The sample preparation process contributed up to 20% to the total variability and had a relative uncertainty (Uprep%) of up to 66% (for bupirimate at 95% confidence). Estimates of systematic errors during physical sample preparation were also made using spike recovery experiments. Four options for the estimation of measurement uncertainty are discussed, which both include and exclude systematic error arising from sample preparation and chemical analysis. A holistic approach to the combination and subsequent expression of uncertainty is advised.     

Optimising uncertainty in physical sample preparation

Uncertainty associated with the result of a measurement can be dominated by the physical sample preparation stage of the measurement process. In view of this, the Optimised Uncertainty (OU) methodology has been further developed to allow the optimisation of the uncertainty from this source, in addition to that from the primary sampling and the subsequent chemical analysis. This new methodology for the optimisation of physical sample preparation uncertainty (u(prep), estimated as s(prep)) is applied for the first time, to a case study of myclobutanil in retail strawberries. An increase in expenditure (+7865%) on the preparatory process was advised in order to reduce the s(prep) by the 69% recommended. This reduction is desirable given the predicted overall saving, under optimised conditions, of 33,000 pounds Sterling per batch. This new methodology has been shown to provide guidance on the appropriate distribution of resources between the three principle stages of a measurement process, including physical sample preparation.     

Multi-analyte optimisation of uncertainty in infant food analysis

The Optimised Uncertainty (OU) methodology has been developed to optimise multi-analyte situations. It has then been applied to a retail survey of infant food for trace elements, classifying the food as compliant or non-compliant with the regulatory thresholds or specification limits that are appropriate for each element. The large-scale survey of infant foods was successfully adapted to allow the estimation of uncertainties, from both primary sampling and chemical analysis, for elemental concentrations in infant formula (milk) and wet meals. The analytes included in this investigation comprised both contaminants (Pb and Cd) and elements essential for child development (Zn and Cu). Optimisation of the measurement process for a 'single analyte' demonstrated the potential financial benefits of optimising future surveys for a false compliance scenario. Uncertainty estimates for the measurement of elemental concentrations in infant formula were dominated by uncertainty from the analytical method. Large potential savings (up to pounds 575,000 per batch) are predicted for both Pb and Zn by increasing the expenditure on chemical analysis to the optimal level. In comparison the uncertainty estimates for elemental concentration in wet meals showed a dominance of sampling as a source of uncertainty for Cd and Cu due to the increased heterogeneity. The feasibility of 'multi-analyte' optimisation is demonstrated for the case study of infant milk. Single analyte optimisation of the four analytes for a false compliance scenario indicated a decrease in expectations of financial loss of between 99% and 8%. An overall decrease in the total expectation of financial loss of 99% is indicated following multi-analyte optimisation.     

Probing Single Nanometer-scale Particles with Scanning Tunneling Microscopy and Spectroscopies

Scanning tunneling microscopy can be used to isolate single particles on surfaces for further study. Local optical and electronic properties coupled with topographic information collected by the scanning tunneling microscope (STM) give insight into the intrinsic properties of the species under study. Since each spectroscopic measurement is done on a single particle, each sample is monodisperse, regardless of the degree of heterogeneity of the original preparation. We illustrate this with three example systems – a metal cluster of known atomic structure, metal nanoparticles dispersed from colloid suspensions, and metallocarbohedrenes (Met-Cars) deposited with other reaction products. Au and Ag nanoparticles were imaged using a photon emission STM. The threshold voltage, the lowest bias voltage at which photons are produced, was determined for Au nanoparticles. Electronic spectra of small clusters of Ni atoms on MoS₂ were recorded. Preliminary images of Zr-based Met-Car-containing soot were obtained on Au and MoS₂ substrates and partial electronic spectra were recorded of these possible Met-Car particles.     

Mechanisms of reactions of halogenated compounds: Part 7. Effects of fluorine and other groups as substituents on nucleophilic aromatic substitution

The effect of fluorine as a substituent group on nucleophilic aromatic substitution is discussed, where a fluorine atom located ortho to the point of substitution may be of variable activating influence, whereas fluorine located para is slightly deactivating and meta is activating. A rationale of these effects is presented and evidence to support polar influences by ortho fluorine is advanced. The influence of CN, CF₃, CF₂H and CFH₂ is also established by comparison of appropriate measured rate constants and compared with the activation effects of ring nitrogen.