Credit Problem !NEW!
If you have a problem with a home equity loan, you should contact the lender first. If you cannot resolve the issue with the lender, file a complaint with the Consumer Financial Protection Bureau (CFPB).
Policy makers need to resist the headlines and focus on the real problem that directly harms millions of Americans: the astounding number of errors in the credit reports that are the result of misaligned economic and legal incentives.
The Equifax data breach provides a key moment for the public and policy makers to focus on the problems as three-quarters of the public recently told pollsters from Morning Consult that they favored new law or regulation to deal with credit bureaus, which is hardly a surprise given the problems described. Here are three simple solutions that would improve the situation:
Credit reports should be proactively made available to all consumers on a free, annual basis. If the system is still going to rely on consumers to identify errors, the system needs to provide that information more easily and freely to consumers. Whether mailed or emailed, credit bureaus know where you live and should send you the information. This would also help solve a real and likely growing problem of consumers who can not access their credit reports because of security freezes or other mismatched personal information which blocks them from obtaining the online version.
Guaranteeing that synaptic plasticity leads to effective learning requires a means for assigning credit to each neuron for its contribution to behavior. The 'credit assignment problem' refers to the fact that credit assignment is non-trivial in hierarchical networks with multiple stages of processing. One difficulty is that if credit signals are integrated with other inputs, then it is hard for synaptic plasticity rules to distinguish credit-related activity from non-credit-related activity. A potential solution is to use the spatial layout and non-linear properties of dendrites to distinguish credit signals from other inputs. In cortical pyramidal neurons, evidence hints that top-down feedback signals are integrated in the distal apical dendrites and have a distinct impact on spike-firing and synaptic plasticity. This suggests that the distal apical dendrites of pyramidal neurons help the brain to solve the credit assignment problem.
For example, suppose you purchased an appliance that stops working after a month. If your state law gives you the right to sue the seller for this problem, you would also have that right against the issuer. So you could dispute the amount due withhold payment, and ask the issuer to investigate the problem. They could not require you to pay the dispute amount without first conducting an investigation. If you are not satisfied with the outcome, you still retain any state rights you may have: If you have the right to sue the seller, you also have the right to sue the issuer for the problem. But to use this federal right you must withhold payment. To take advantage of this protection
Credit problems can make life pretty difficult. A low credit score can keep you from applying for credit cards, loans and mortgages. Credit problems can also make it hard to get an apartment or even a job. Perhaps the most difficult thing about a poor credit score is that it can seem impossible to fix, as credit problems in your past often prevent you from the getting the opportunity to show that you are creditworthy today.
We already knew that biased data and biased algorithms skew automated decision-making in a way that disadvantages low-income and minority groups. For example, software used by banks to predict whether or not someone will pay back credit-card debt typically favors wealthier white applicants. Many researchers and a slew of start-ups are trying to fix the problem by making these algorithms more fair.
Veterans Affairs (VA) will now make it easier and faster for lower-income veterans to get their VA medical debt forgiven. Currently, veterans in financial hardship who need medical debt relief from VA must fill out a complex, paper form with complicated eligibility requirements. The application process is confusing, time-consuming, and as a result, veterans may be deterred from applying for much-needed relief. To address these problems and ensure that veterans get the relief they deserve, VA will streamline the request process, including offering an online option to apply, and set a simple income threshold to qualify for relief.
The CFPB has a wide range of tools available to help patients and their families confronting medical billing and collections, particularly problems relating to debt collection and credit reporting, at consumerfinance.gov. People experiencing aggressive debt collection, coercive credit reporting, or other problems with a consumer financial product or service related to medical billing and collections can submit a complaint to the CFPB at consumerfinance.gov/complaint.
Low Income Taxpayer Clinics (LITCs) are independent from the IRS and TAS. LITCs represent individuals whose income is below a certain level and who need to resolve tax problems with the IRS. LITCs can represent taxpayers in audits, appeals, and tax collection disputes before the IRS and in court. In addition, LITCs can provide information about taxpayer rights and responsibilities in different languages for individuals who speak English as a second language. Services are offered for free or a small fee. For more information or to find an LITC near you, see the LITC page on the TAS website or Publication 4134, Low Income Taxpayer Clinic List.
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The best way to protect yourself from fraud, scams, or problems is to be aware of potential pitfalls ahead of time. If that isn't enough, you should also know what you can do to resolve complaints. Here are some tips for both:
Unfortunately, your product (or service) has not performed well (or the service was inadequate) because (state the problem). I am disappointed because (explain the problem; for example, the product does not work properly, the service was not performed correctly, I was billed the wrong amount, something was not disclosed clearly or was misrepresented, etc.).
I look forward to your reply and a resolution to my problem I will wait until (set a time limit) before seeking help from a consumer protection agency or the Better Business Bureau. Please contact me at the address or phone shown above.
In most everyday situations, the rewards are not immediate consequences of behavior, but instead appear after substantial delays. To influence future choices, the teaching signal conveyed by DA release needs to reinforce synaptic events occurring on a millisecond timescale, frequently seconds before the outcomes of decisions are revealed (Izhikevich, 2007; Fisher et al., 2017). This apparent difficulty in linking preceding behaviors caused by transient neuronal activity to a delayed feedback has been termed the distal reward or temporal credit assignment problem (Hull, 1943; Barto et al., 1983; Sutton and Barto, 1998; Dayan and Abbott, 2001; Wörgötter and Porr, 2005). Credit for the reward delayed by several seconds can frequently be assigned by establishing an eligibility trace, a molecular memory of the recent neuronal activity, allowing modification of synaptic connections that participated in the behavior (Pan et al., 2005; Fisher et al., 2017). On longer timescales, or when multiple actions need to be performed sequentially to reach a final goal, intermediate steps themselves can acquire motivational significance and subsequently reinforce preceding decisions, such as in temporal-difference (TD) learning models (Sutton and Barto, 1998).
Several excellent reviews have summarized the accumulated knowledge on mechanisms that link choices and their outcomes through time, highlighting the advantages of eligibility traces and TD models (Wörgötter and Porr, 2005; Barto, 2007; Niv, 2009; Walsh and Anderson, 2014). Yet these solutions to the distal reward problem can impede learning in multi-choice tasks, or when an animal is presented with many irrelevant stimuli prior to or during the delay. Here, I only briefly overview the work on the distal reward problem to highlight potential complications that can arise in credit assignment based on eligibility traces when learning in multi-cue environments. Instead, I focus on the structural (or spatial) credit assignment problem, requiring animals to select and learn about the most meaningful features in the environment and ignore irrelevant distractors. Collectively, the reviewed evidence highlights a critical role for the prefrontal cortex (PFC) in such contingent learning.
Example tasks highlighting the challenge of credit assignment and learning strategies enabling animals to solve this problem. (A) An example of a distal reward task that can be successfully learned with eligibility traces and TD rules, where intermediate choices can acquire motivational significance and subsequently reinforce preceding decisions (ex., Pasupathy and Miller, 2005; Histed et al., 2009). (B) In this version of the task, multiple cues are present at the time of choice, only one of which is meaningful for obtaining rewards. After a brief presentation, the stimuli disappear, requiring an animal to solve a complex structural and temporal credit assignment problem (ex., Noonan et al., 2010, 2017; Niv et al., 2015; Asaad et al., 2017; while the schematic of the task captures the challenge of credit assignment, note that in some experimental variants of the behavioral paradigm stimuli disappeared before an animal revealed its choice, whereas in others the cues remained on the screen until the trial outcome was revealed). Under such conditions, learning based on eligibility traces is suboptimal, as non-specific reward signals can reinforce visual cues that did not meaningfully contribute, but occurred close, to beneficial outcomes of behavior. (C) On reward tasks, similar to the one shown in (B), the impact of previous decisions and associated rewards on current behavior can be assessed by performing regression analyses (Jocham et al., 2016; Noonan et al., 2017). Here, the color of each cell in a matrix represents the magnitude of the effect of short-term choice and outcome histories, up to 4 trials into the past (red-strong influence; blue-weak influence on the current decision). Top: an animal learning based on the causal relationship between outcomes and choices (i.e., contingent learning). Middle: each choice is reinforced by a combined history of rewards (i.e., decisions are repeated if beneficial outcomes occur frequently). Bottom: the influence of recent rewards spreads to unrelated choices. 041b061a72