For government agencies managing public utility programs, quality data equates to increased efficiency and effectiveness in collecting unpaid utility bills. The bottom line is a substantial decrease in losses from debts considered to be “uncollectable”—and an increase in overall program integrity and profitability. Learn one strategic tactic that can help your agency in their efforts to recover unpaid utilities from debtors.
Identity fraud has become a major contributor to the $5.6 billion lost to improper payments. Learn how your agency can take a multi-layered identity fraud prevention approach to prevent fraud in Unemployment Insurance.
We recently announced the results of a research study on identity theft-related tax refund fraud in the states. The study, commissioned by LexisNexis and conducted by the Governing Institute, found that 86 percent of state government tax administration officials view identity fraud as a ‘major problem’ within the state tax refund process. What is critical to understand is that the problem is growing-and quickly. Learn more about this growing problem and new ways to combat it.
The data that government agency debt collectors use to identify debtors and their whereabouts erodes and becomes quickly obsolete as individuals change names, jobs, phone numbers and addresses. Given so much movement and change, government agency debt collectors must adapt as well. They need the most recent and accurate identity data available in order to find and identify debtors, and they need analytics and scoring to prioritize their debt collections efforts. If agencies want to bring new life to their debt offset program, then identities matter.
What if unpaid tolls considered “uncollectable” due to obsolete data, suddenly became collectable debt linked to verified individuals? What if your debtor contact data never eroded or became inaccurate—even when the debtors’ identification information changed over time? Examine powerful new identity analytics helping toll authorities boost debt collections and revenues.
Medicare and Medicaid fraud costs consumers hundreds of millions of dollars each year. Eva Velasquez, President and CEO at the Identity Theft Resource Center shares steps consumers can take to help combat this type of crime – and reduce the amount of money spent investigating fraud.
By mapping identity sources, identity paths, and public-facing edges across your system, you can quickly breakdown where identities are agreed upon, the paths they take, and your government agency’s risks.
My last two blog posts discussed the issues that arise when government agencies rely on matched data and owned data risks. The third type of identity risk that affects government programs is identity integration risk.
The horrors of “Franken-data”, or matched identity risk. This type of identity risk stems from an identity so poor a match fails, even though the real person represented in both datasets is the same person.
Agreeing on an identity that is not real or is not useful will cost you. This is identity risk. The fundamental identity risk difference between data matching and identity analysis is the underlying assumption.
Now that you understand how identities are being used to defraud the government, it is important to understand how and why the government’s view of identities must change. Your identity, my identity, everyone’s identity is already compromised and is being relentlessly sought after by criminals.