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Riskalyze Campaign Attacks Orion's HiddenLevers, Rixtrema


Riskalyze Campaign Attacks Orion s HiddenLevers, RiXtrema
Citing fiduciary responsibilities, Riskalyze CEO Aaron Klein is publicly targeting the wildly inaccurate and predictive guesswork of his competitors.
A long-simmering confrontation in the advisor technology space is spilling into public view.
Riskalyze co-founder and CEO Aaron Klein is launching a highly visible campaign to discredit the risk methodologies used by competitors. Squarely in the crosshairs are risk analytics platform HiddenLevers, acquired by Orion earlier this year, and RiXtrema, which offers a portfolio stress-testing tool called Portfolio Crash Testing PRO.
The marketing campaign, launched from the Riskalyze-controlled website Unhiddenlevers.com, has been in planning for roughly six weeks, according to Klein, and includes a white paper, messaging for “industry influencers” and a video criticizing a 2020 coronavirus pandemic-related webinar series hosted by HiddenLevers c ....

Aaron Klein , Praveen Ghanta , Jamie Hopkins , Raj Udeshi , Market Research , Carson Group , Orion Advisor Services , Portfolio Crash Testing , Hidden Levers , Advisor Services , Soon Klein , Risk Model Validation , Research Survey , Carson Coaching , ஆரோன் க்ளீன் , ப்ரவீன் கண்ட , ஜேமி ஹாப்கின்ஸ் , ராஜ் உதேஷி , சந்தை ஆராய்ச்சி , கார்சன் குழு , ஆபத்து மாதிரி சரிபார்த்தல் , ஆராய்ச்சி கணக்கெடுப்பு , கார்சன் பயிற்சி ,

Beyond the contract: client behavior from origination to default as the new set of the loss given default risk drivers


Journal of Risk Model Validation
24 February 2021
Beyond the contract: client behavior from origination to default as the new set of the loss given default risk drivers
Parametric and non-parametric models are prepared for the recovery rate estimation.
Adding client related variables reduces the errors and improve discrimination.
The effect is more visible for fractional regression rather than regression tree.
Abstract
Modeling loss given default has increased in popularity as it has become a crucial parameter for establishing capital buffers under Basel II and III and for calculating the impairment of financial assets under the International Financial Reporting Standard 9. The most recent literature on this topic focuses mainly on estimation methods and less on the variables used to explain the variability in loss given default. In this paper, we expand this part of the modeling process by constructing a set of client-behavior-based pre ....

International Financial Reporting Standard , Credit Risk , Retail Banking , Recovery Rates , Loss Given Default Lgd , Risk Model Validation , Original Research , சர்வதேச நிதி புகாரளித்தல் தரநிலை , கடன் ஆபத்து , சில்லறை வங்கி , மீட்பு ரேட்ஸ் , இழப்பு கொடுக்கப்பட்டது இயல்புநிலை எல்ஜிடி , ஆபத்து மாதிரி சரிபார்த்தல் , ஒரிஜிநல் ஆராய்ச்சி ,

Bifractal receiver operating characteristic curves: a formula for generating receiver operating characteristic curves in credit-scoring contexts


Need to know
The model proposed in this paper enables the drawing of ROC curves for credit scoring without underlying data.
The bifractal ROC function fits well with the empirical curves.
The results show that the Gini coefficient of a scorecard will almost always drop if computed only above the cutoff.
Abstract
This paper formulates a mathematical model for generating receiver operating characteristic (ROC) curves without underlying data. Credit scoring practitioners know that the Gini coefficient usually drops if it is only calculated on cases above the cutoff. This fact is not a mathematical necessity, however, as it is theoretically possible to get an ROC curve that keeps the same Gini coefficient no matter how big a share of lowest score cases are excluded from the calculation (a “right-hand” fractal ROC curve). Analogously, a left-hand fractal ROC curve would be a curve that keeps its Gini coefficient constant below any cutoff point. The model propo ....

Credit Scoring , Corporate Lending , Risk Model Validation , Original Research , கடன் மதிப்பெண் , பெருநிறுவன கடன் , ஆபத்து மாதிரி சரிபார்த்தல் , ஒரிஜிநல் ஆராய்ச்சி ,

A hybrid model for credit risk assessment: empirical validation by real-world credit data


This study evaluates the efficiency of the hybrid classifier.
The study found that the clustering based LR + MLP hybrid classifier efficient across all of the criteria.
Among the other classifiers, MLP based hybrid classifiers also achieved superior performance.
Abstract
This paper examines which hybridization strategy is more suitable for credit risk assessment in the dynamic financial world. As such, we use extensive new data sets and develop different hybrid models by combining traditional statistical and modern artificial intelligence methods based on classification and clustering feature selection approaches. We find that a multilayer perceptron (MLP) combined with discriminant analysis or logistic regression (LR) can significantly improve classification accuracy compared with other single and hybrid classifiers. In particular, the findings of our empirical analysis, statistical significance test and expected cost of misclassification test confirm the superior ....

Statistical Analysis , Artificial Intelligence , Hybrid Products , Credit Risk , Risk Model Validation , Original Research , புள்ளிவிவர பகுப்பாய்வு , செயற்கை உளவுத்துறை , கலப்பு ப்ராடக்ட்ஸ் , கடன் ஆபத்து , ஆபத்து மாதிரி சரிபார்த்தல் , ஒரிஜிநல் ஆராய்ச்சி ,