Dynamic Risk Model of Money Laundering

2014 ◽  
pp. 3-20 ◽  
Author(s):  
Murad Mehmet ◽  
Murat Güneştaş ◽  
Duminda Wijesekera
Keyword(s):  
Money Laundering ◽  
Risk Model ◽  
Dynamic Risk

Dynamic Risk Model of Money Laundering

2014 ◽  
pp. 3-20
Author(s):  
Murad Mehmet ◽  
Murat Güneştaş ◽  
Duminda Wijesekera
Keyword(s):  
Money Laundering ◽  
Risk Model ◽  
Dynamic Risk

scholarly journals Dynamic risk model for Rift Valley fever outbreaks in Kenya based on climate and disease outbreak data

2016 ◽  
Vol 11 (2) ◽  
Author(s):  
David Gikungu ◽  
Jacob Wakhungu ◽  
Donald Siamba ◽  
Edward Neyole ◽  
Richard Muita ◽  
...  
Keyword(s):  
Rift Valley ◽  
Rift Valley Fever ◽  
Risk Model ◽  
Early Warning Systems ◽  
Sub Saharan Africa ◽  
Weather Data ◽  
Brier Score ◽  
Valley Fever ◽  
Dynamic Risk ◽  
Normalised Difference Vegetation Index

Rift Valley fever (RVF) is a mosquito-borne viral zoonotic disease that occurs throughout sub-Saharan Africa, Egypt and the Arabian Peninsula, with heavy impact in affected countries. Outbreaks are episodic and related to climate variability, especially rainfall and flooding. Despite great strides towards better prediction of RVF epidemics, there is still no observed climate data-based warning system with sufficient lead time for appropriate response and mitigation. We present a dynamic risk model based on historical RVF outbreaks and observed meteorological data. The model uses 30-year data on rainfall, temperature, relative humidity, normalised difference vegetation index and sea surface temperature data as predictors. Our research on RVF focused on Garissa, Murang’a and Kwale counties in Kenya using a research design based on a correlational, experimental, and evaluational approach. The weather data were obtained from the Kenya Meteorological Department while the RVF data were acquired from International Livestock Research Institute, and the Department of Veterinary Services. Performance of the model was evaluated by using the first 70% of the data for calibration and the remaining 30% for validation. The assessed components of the model accurately predicted already observed RVF events. The Brier score for each of the models (ranging from 0.007 to 0.022) indicated high skill. The coefficient of determination (R2) was higher in Garissa (0.66) than in Murang’a (0.21) and Kwale (0.16). The discrepancy was attributed to data distribution differences and varying ecosystems. The model outputs should complement existing early warning systems to detect risk factors that predispose for RVF outbreaks.

Dynamic risk analysis and risk model evaluation

2008 ◽  
pp. 149-168
Author(s):  
Günter Schwarz ◽  
Christoph Kessler
Keyword(s):  
Risk Analysis ◽  
Model Evaluation ◽  
Risk Model ◽  
Dynamic Risk

A dynamic risk model for inpatient falls

2018 ◽  
Vol 75 (17) ◽  
pp. 1293-1303
Author(s):  
Yoonyoung Choi ◽  
Benjamin Staley ◽  
Carl Henriksen ◽  
Dandan Xu ◽  
Gloria Lipori ◽  
...  
Keyword(s):  
Risk Model ◽  
Dynamic Risk ◽  
Inpatient Falls

A Stocking CUM Pricing Model under the Effect of Consumer Behaviour

2003 ◽  
Vol 54 (1-2) ◽  
pp. 71-80
Author(s):  
Suddha Sankar Dutta ◽  
Manisha Pal
Keyword(s):  
Consumer Behaviour ◽  
Risk Model ◽  
Past Experience ◽  
The Other ◽  
Selling Price ◽  
Pricing Model ◽  
Dynamic Risk ◽  
Unmet Demand ◽  
Procurement Cost ◽  
Optimal Values

The paper considers a dynamic risk model with periods of equal length for stocking and selling products where the selling price of the stocked item is under control of the management. A preset price ( y) may appear unsatisfactory to a fraction (1- ψ( y)) of the arrived demand and this fraction of demand is lost irrespective of the stock on hand. The other fraction ( ψ( y)) of the demand is retained if there is stock on hand and in case of stockout a fixed fraction ( π) of the demand, is backlogged whereas (1 - π) of the unmet demand is lost. ψ( y) is decreasing in y. where y is at least as high as the procurement cost, and both ψ( y) and π are either known or can be estimated from past experience. Arrival of demand over a reorder interval is continuous, following some known probabilistic Jaw and is independent of price. Optimal values of stock height and selling price have been obtained through maximization of profit, given that both of them are to be set at the beginning of a reorder interval.

A dynamic risk model to analyze hydrogen infrastructure

2021 ◽  
Vol 46 (5) ◽  
pp. 4626-4643
Author(s):  
Esmaeil Zarei ◽  
Faisal Khan ◽  
Mohammad Yazdi
Keyword(s):  
Risk Model ◽  
Dynamic Risk ◽  
Hydrogen Infrastructure
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