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Introduction Hello, this is my first post after joining today. I am college educated at the Bachelor of Science level in science, business and computers. I have worked with computers since 1979. Low Income And Financial Data Management My first topics are about low income legal assistance and financial data quality/integrity. Low income here is defined as being eligible for public benefits (disability, energy assistance, food stamps, free cell phone, gas cards, etc). Reasons for lack of income can include completed work with no pay, employment application scams, fake interviews, serious distractions due to phone/email scams and deceptive work at home offers. Examples of known types of scams: Green Dot Loan, Medical/Surgery Calls, Unsolicited Debt Relief Calls, Windows Tech Support Calls, Threatening Emails (ACS, Inc.). Note: Skype landline/VOIP service does not work well with robocalling in my experience. Calls in my experience are usually rejected and dropped for technical reasons. A debtor with less income has fewer options and will guard (if smart) every dollar before paying anything. A certified mail send task must be worth it to justify the cost for example. Data integrity refers to if financial/personal data is maintained according to professional standards. Choices of software, methods of structuring the data, data entry methods and functionality of entry applications all play a part in data integrity. Data security is more about keeping data safe from being stolen, hacked or viewed by wrong people. Specific Topics (perhaps addressed in new threads) 1. How does a low income target of debt collection action prioritize multiple debt claims and current ongoing payment commitments? 2. Can a debtor use successfully paid off accounts (such as a car loan) as proof of willingness to pay if able to pay? 3. Is a business with an aggressive model with no regard to data integrity really that tenable? How is it different from a Green Dot Loan scammer business model? Junk debt buyers use cheap data solution Excel to store the data as I have read. Can anyone verify this? I have observed poor merchant data quality in a business loan lead data purchase in the form of blacklisted spammers in the Excel file I received. No one at the supplier cared. But I did get my money back from Mastercard when the purchase was disputed. Summary It is very disheartening for a computer professional to observe poor data quality practices. We should work together to provide suitable solutions to debt collection issues while focusing on keeping our personal data safe from misuse by anyone.