Development and application of consumer credit scoring models using profit-based classification measures

This paper presents a new approach for consumer credit scoring, by tailoring a profit-based classification performance measure to credit risk modeling. This performance measure takes into account the expected profits and losses of credit granting and thereby better aligns the model developers' objectives with those of the lending company. It is based on the Expected Maximum Profit (EMP) measure and is used to find a trade-off between the expected losses -- driven by the exposure of the loan and the loss given default -- and the operational income given by the loan. Additionally, one of the major advantages of using the proposed measure is that it permits to calculate the optimal cutoff value, which is necessary for model implementation. To test the proposed approach, we use a dataset of loans granted by a government institution, and benchmarked the accuracy and monetary gain of using EMP, accuracy, and the area under the ROC curve as measures for selecting model parameters, and for determining the respective cutoff values. The results show that our proposed profit-based classification measure outperforms the alternative approaches in terms of both accuracy and monetary value in the test set, and that it facilitates model deployment

Development and application of consumer credit scoring models using profit-based classification measures

This paper presents a new approach for consumer credit scoring, by tailoring a profit-based classification performance measure to credit risk modeling. This performance measure takes into account the expected profits and losses of credit granting and thereby better aligns the model developers' objectives with those of the lending company. It is based on the Expected Maximum Profit (EMP) measure and is used to find a trade-off between the expected losses -- driven by the exposure of the loan and the loss given default -- and the operational income given by the loan. Additionally, one of the major advantages of using the proposed measure is that it permits to calculate the optimal cutoff value, which is necessary for model implementation. To test the proposed approach, we use a dataset of loans granted by a government institution, and benchmarked the accuracy and monetary gain of using EMP, accuracy, and the area under the ROC curve as measures for selecting model parameters, and for determining the respective cutoff values. The results show that our proposed profit-based classification measure outperforms the alternative approaches in terms of both accuracy and monetary value in the test set, and that it facilitates model deployment

Identification and characterization of nanobodies targeting the EphA4 receptor

The ephrin receptor A4 (EphA4) is one of the receptors in the ephrin system that plays a pivotal role in a variety of cell-cell interactions, mostly studied during development. In addition, EphA4 has been found to play a role in cancer biology as well as in the pathogenesis of several neurological disorders such as stroke, spinal cord injury, multiple sclerosis, amyotrophic lateral sclerosis (ALS), and Alzheimer's disease. Pharmacological blocking of EphA4 has been suggested to be a therapeutic strategy for these disorders. Therefore, the aim of our study was to generate potent and selective Nanobodies against the ligand-binding domain of the human EphA4 receptor. Weidentified two Nanobodies, Nb 39 and Nb 53, that bind EphA4 with affinities in the nanomolar range. These Nanobodies were most selective for EphA4, with residual binding to EphA7 only. Using Alphascreen technology, we found that both Nanobodies displaced all known EphA4-binding ephrins from the receptor. Furthermore, Nb39 andNb53 inhibited ephrin-induced phosphorylationoftheEphA4proteininacell-basedassay. Finally, in a cortical neuron primary culture, both Nanobodies were able to inhibit endogenous EphA4-mediated growth-cone collapse induced by ephrin-B3. Our results demonstrate the potential of Nanobodies to target the ligand-binding domain of EphA4. These Nanobodiesmaydeservefurtherevaluationaspotentialtherapeutics in disorders in which EphA4-mediated signaling plays a role

Colloidal aggregation in microgravity by critical Casimir forces

By using the critical Casimir force, we study the attractive strength dependent aggregation of colloids with and without gravity by means of Near Field scattering. Significant differences were seen between microgravity and ground experiments, both in the structure of the formed fractal aggregates as well as the kinetics of growth. Ground measurements are severely affected by sedimentation resulting in reaction limited behavior. In microgravity, a purely diffusive behavior is seen reflected both in the measured fractal dimensions for the aggregates as well as the power law behavior in the rate of growth. Formed aggregates become more open as the attractive strength increases.Comment: 4 pages, 3 figure

Prediction of impending type 1 diabetes through automated dual-label measurement of proinsulin:C-peptide ratio

Background : The hyperglycemic clamp test, the gold standard of beta cell function, predicts impending type 1 diabetes in islet autoantibody-positive individuals, but the latter may benefit from less invasive function tests such as the proinsulin: C-peptide ratio (PI:C). The present study aims to optimize precision of PI:C measurements by automating a dual-label trefoil-type time-resolved fluorescence immunoassay (TT-TRFIA), and to compare its diagnostic performance for predicting type 1 diabetes with that of clamp-derived C-peptide release. Methods : Between-day imprecision (n = 20) and split-sample analysis (n = 95) were used to compare TT-TRFIA (Auto Delfia, Perkin-Elmer) with separate methods for proinsulin (in-house TRFIA) and C-peptide (Elecsys, Roche). High-risk multiple autoantibody-positive firstdegree relatives (n = 49; age 5-39) were tested for fasting PI:C, HOMA2-IR and hyperglycemic clamp and followed for 20-57 months (interquartile range). Results : TT-TRFIA values for proinsulin, C-peptide and PI:C correlated significantly (r(2) = 0.96-0.99; P<0.001) with results obtained with separate methods. TT-TRFIA achieved better between-day % CV for PI:C at three different levels (4.5-7.1 vs 6.7-9.5 for separate methods). In high-risk relatives fasting PI:C was significantly and inversely correlated ( r(s) = -0.596; P<0.001) with first-phase C-peptide release during clamp ( also with second phase release, only available for age 12-39 years; n = 31), but only after normalization for HOMA2-IR. In ROC- and Cox regression analysis, HOMA2-IR-corrected PI:C predicted 2-year progression to diabetes equally well as clamp-derived C-peptide release. Conclusions : The reproducibility of PI:C benefits from the automated simultaneous determination of both hormones. HOMA2-IR-corrected PI:C may serve as a minimally invasive alternative to the more tedious hyperglycemic clamp test