Mining Bad Credit Card Accounts from OLAP and OLTP

Credit card companies classify accounts as a good or bad based on historical data where a bad account may default on payments in the near future. If an account is classified as a bad account, then further action can be taken to investigate the actual nature of the account and take preventive actions. In addition, marking an account as "good" when it is actually bad, could lead to loss of revenue - and marking an account as "bad" when it is actually good, could lead to loss of business. However, detecting bad credit card accounts in real time from Online Transaction Processing (OLTP) data is challenging due to the volume of data needed to be processed to compute the risk factor. We propose an approach which precomputes and maintains the risk probability of an account based on historical transactions data from offline data or data from a data warehouse. Furthermore, using the most recent OLTP transactional data, risk probability is calculated for the latest transaction and combined with the previously computed risk probability from the data warehouse. If accumulated risk probability crosses a predefined threshold, then the account is treated as a bad account and is flagged for manual verification.Comment: Conference proceedings of ICCDA, 201

Operational Plan for HMIS Rollout to be Read in Conjunction with the MoH&SW Document of October 2007

The MoH&SW, with a consortium of partners, in October 2007, developed a Proposal to Strengthen the HMIS in Tanzania. This document builds on that proposal to develop a budgeted 6‐month plan to kick‐start implementation of the Revised MTUHA in one region and at national level, to develop a replicable model that can be scaled up to other regions as additional funds become available. The overall HMIS revision process will ensure that, within a period of five years the HMIS will be functional in all 21 regions of the country, in a phased manner Six months intensive systems and database development in Mtwara region Eighteen months implementation in one region in each of the six zones Within 5 years, National rollout to every region The initial six months implementation process, described in depth in this document, will use action research and participatory development methodology that will integrate the six work packages in the HMIS document, in line with the HSSP III proposals for strengthening M&E. A number of dedicated teams will roll out the HMIS, develop a toolkit for implementation in other regions and produce a modern web based data warehouse. The project logframe aims to provide quality routine data for monitoring MDGs and the NHSSPIII by producing five outputs – HMIS revision, HMIS implementation, Capacity development, the DHIS software and action research. Terms of reference are developed for each of the HMIS teams, based on the activities in the logframe – Indicator and dataset revision, HMIS design, Database development and training team. An action‐based budget of US$15 million is provided for three years that envisages The model region will cost$1,25 million for the first year, including the rollout activities, the development of training material, adaptation of software etc. The other six regions will cost 1,05million for first year; all regions will reduce to $500,000 for the second year and$300,000 in the third year. National level costs will reduce from $700,000 to 500,000 a year as local consultants replace international technical assistance and Ministry takes over running expenses. Rollout for the other 14 regions will need a separate budgeting process after the six regions, but should be in the range of 1,8 million a year (or less if costs can be reduced). The activities in the model initiation region will cost$1,2 million for the first year, including the rollout activities, the development of training material, adaptation of software et

Regional Warehouse Trip Production Analysis: Chicago Metro Area

This research report provides primary research data and analysis on heavy truck trip generation and characteristics from regional distribution centers (RDC) and similar facilities in an effort to facilitate future public policy making regarding roadway transportation needs as well as land-use and economic development decisions. The report also provides secondary data and information on intermodal freight transportation - its growth and its economic impacts ??? to provide a regional, national, and international context for the research. The primary data was obtained from a field survey of 12 distribution centers of various scales (7 of them regional) in Northeast Illinois. The 12 facilities and their supervisory personnel were visited by the research team and analyzed in depth for their general business characteristics (e.g. type of goods, number of employees, hours of operation etc.), property characteristics (e.g. location, facility size, ceiling height) and their truck trip productions (e.g. number of arrivals-departures, geographic distribution of inbound-outbound movement, volume per quarter etc.). The findings of this research project in reference to the 12 facilities indicate the uniqueness and significant complexity of the distribution centers. There is clear evidence of an increase in size (sq. ft & ceiling) and automation (racking systems) of the newer facilities as well as 24-hour operations. The comparison of daily heavy truck movement shows significant arrival concentration between 8am-10am and 8pm- 6am. In contrast the heaviest departure activity is between 4-6pm. The majority of originating freight is from the Midwest with the outbound distributions also being allocated regionally then nationally and internationally (minimal allocation). Another result was the increased volume concentration in the third quarter of each year between July and September. The above results along with the significant expansions of RDC facilities in the last few years indicate the additional need for studying the locations of the various facilities and the heavy truck traffic volume they generate. The results should also be useful in determining the economics benefits/costs and impacts of these facilities for purposes of making infrastructure investment, economic incentive, and land use decisions.Illinois Center for Transportation R27-15published or submitted for publicationis peer reviewe