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Debt Assignment: How They Work, Considerations and Benefits

Daniel Liberto is a journalist with over 10 years of experience working with publications such as the Financial Times, The Independent, and Investors Chronicle.

Charlene Rhinehart is a CPA , CFE, chair of an Illinois CPA Society committee, and has a degree in accounting and finance from DePaul University.

Katrina Ávila Munichiello is an experienced editor, writer, fact-checker, and proofreader with more than fourteen years of experience working with print and online publications.

Investopedia / Ryan Oakley

What Is Debt Assignment?

The term debt assignment refers to a transfer of debt, and all the associated rights and obligations, from a creditor to a third party. The assignment is a legal transfer to the other party, who then becomes the owner of the debt . In most cases, a debt assignment is issued to a debt collector who then assumes responsibility to collect the debt.

Key Takeaways

• Debt assignment is a transfer of debt, and all the associated rights and obligations, from a creditor to a third party (often a debt collector).
• The company assigning the debt may do so to improve its liquidity and/or to reduce its risk exposure.
• The debtor must be notified when a debt is assigned so they know who to make payments to and where to send them.
• Third-party debt collectors are subject to the Fair Debt Collection Practices Act (FDCPA), a federal law overseen by the Federal Trade Commission (FTC).

How Debt Assignments Work

When a creditor lends an individual or business money, it does so with the confidence that the capital it lends out—as well as the interest payments charged for the privilege—is repaid in a timely fashion. The lender , or the extender of credit , will wait to recoup all the money owed according to the conditions and timeframe laid out in the contract.

In certain circumstances, the lender may decide it no longer wants to be responsible for servicing the loan and opt to sell the debt to a third party instead. Should that happen, a Notice of Assignment (NOA) is sent out to the debtor , the recipient of the loan, informing them that somebody else is now responsible for collecting any outstanding amount. This is referred to as a debt assignment.

The debtor must be notified when a debt is assigned to a third party so that they know who to make payments to and where to send them. If the debtor sends payments to the old creditor after the debt has been assigned, it is likely that the payments will not be accepted. This could cause the debtor to unintentionally default.

When a debtor receives such a notice, it's also generally a good idea for them to verify that the new creditor has recorded the correct total balance and monthly payment for the debt owed. In some cases, the new owner of the debt might even want to propose changes to the original terms of the loan. Should this path be pursued, the creditor is obligated to immediately notify the debtor and give them adequate time to respond.

The debtor still maintains the same legal rights and protections held with the original creditor after a debt assignment.

Special Considerations

Third-party debt collectors are subject to the Fair Debt Collection Practices Act (FDCPA). The FDCPA, a federal law overseen by the Federal Trade Commission (FTC), restricts the means and methods by which third-party debt collectors can contact debtors, the time of day they can make contact, and the number of times they are allowed to call debtors.

If the FDCPA is violated, a debtor may be able to file suit against the debt collection company and the individual debt collector for damages and attorney fees within one year. The terms of the FDCPA are available for review on the FTC's website .

Benefits of Debt Assignment

There are several reasons why a creditor may decide to assign its debt to someone else. This option is often exercised to improve liquidity  and/or to reduce risk exposure. A lender may be urgently in need of a quick injection of capital. Alternatively, it might have accumulated lots of high-risk loans and be wary that many of them could default . In cases like these, creditors may be willing to get rid of them swiftly for pennies on the dollar if it means improving their financial outlook and appeasing worried investors. At other times, the creditor may decide the debt is too old to waste its resources on collections, or selling or assigning it to a third party to pick up the collection activity. In these instances, a company would not assign their debt to a third party.

Criticism of Debt Assignment

The process of assigning debt has drawn a fair bit of criticism, especially over the past few decades. Debt buyers have been accused of engaging in all kinds of unethical practices to get paid, including issuing threats and regularly harassing debtors. In some cases, they have also been charged with chasing up debts that have already been settled.

Federal Trade Commission. " Fair Debt Collection Practices Act ." Accessed June 29, 2021.

Federal Trade Commission. " Debt Collection FAQs ." Accessed June 29, 2021.

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Credit Assignment

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• First Online: 01 January 2017
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• Claude Sammut 3  

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Structural credit assignment ; Temporal credit assignment

When a learning system employs a complex decision process, it must assign credit or blame for the outcomes to each of its decisions. Where it is not possible to directly attribute an individual outcome to each decision, it is necessary to apportion credit and blame between each of the combinations of decisions that contributed to the outcome. We distinguish two cases in the credit assignment problem. Temporal credit assignment refers to the assignment of credit for outcomes to actions. Structural credit assignment refers to the assignment of credit for actions to internal decisions. The first subproblem involves determining when the actions that deserve credit were taken and the second involves assigning credit to the internal structure of actions (Sutton  1984 ).

Consider the problem of learning to balance a pole that is hinged on a cart (Michie and Chambers  1968 ; Anderson and Miller  1991 ). The cart...

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Recommended Reading

Albus JS (1975) A new approach to manipulator control: the cerebellar model articulation controller (CMAC). J Dyn Syst Measur Control Trans ASME 97(3):220–227

Article   MATH   Google Scholar  

Anderson CW, Miller WT (1991) A set of challenging control problems. In: Miller W, Sutton RS, Werbos PJ (eds) Neural networks for control. MIT Press, Cambridge

Google Scholar  

Atkeson C, Schaal S, Moore A (1997) Locally weighted learning. AI Rev 11:11–73

Banerjee B, Liu Y, Youngblood GM (eds) (2006) Proceedings of the ICML workshop on “structural knowledge transfer for machine learning, Pittsburgh

Barto A, Sutton R, Anderson C (1983) Neuron-like adaptive elements that can solve difficult learning control problems. IEEE Trans Syst Man Cybern SMC-13:834–846

Article   Google Scholar  

Benson S, Nilsson NJ (1995) Reacting, planning and learning in an autonomous agent. In: Furukawa K, Michie D, Muggleton S (eds) Machine intelligence, vol 14. Oxford University Press, Oxford

Bertsekas DP, Tsitsiklis J (1996) Neuro-dynamic programming. Athena Scientific, Nashua

MATH   Google Scholar  

Caruana R (1997) Multitask learning. Mach Learn 28:41–75

Dejong G, Mooney R (1986) Explanation-based learning: an alternative view. Mach Learn 1:145–176

Goldberg DE (1989) Genetic algorithms in search, optimization and machine learning. Addison-Wesley Longman Publishing, Boston

Grefenstette JJ (1988) Credit assignment in rule discovery systems based on genetic algorithms. Mach Learn 3(2–3):225–245

Hinton G, Rumelhart D, Williams R (1985) Learning internal representation by back-propagating errors. In: Rumelhart D, McClelland J, Group TPR (eds) Parallel distributed computing: explorations in the microstructure of cognition, vol 1. MIT Press, Cambridge, pp 31–362

Holland J (1986) Escaping brittleness: the possibilities of general-purpose learning algorithms applied to parallel rule-based systems. In: Michalski RS, Carbonell JG, Mitchell TM (eds) Machine learning: an artificial intelligence approach, vol 2. Morgan Kaufmann, Los Altos

Laird JE, Newell A, Rosenbloom PS (1987) SOAR: an architecture for general intelligence. Artif Intell 33(1):1–64

Mahadevan S (2009) Learning representation and control in Markov decision processes: new frontiers. Found Trends Mach Learn 1(4):403–565

Michie D, Chambers R (1968) Boxes: an experiment in adaptive control. In: Dale E, Michie D (eds) Machine intelligence, vol 2. Oliver and Boyd, Edinburgh

Minsky M (1961) Steps towards artificial intelligence. Proc IRE 49:8–30

Article   MathSciNet   Google Scholar  

Mitchell TM, Keller RM, Kedar-Cabelli ST (1986) Explanation based generalisation: a unifying view. Mach Learn 1:47–80

Mitchell TM, Utgoff PE, Banerji RB (1983) Learning by experimentation: acquiring and refining problem-solving heuristics. In: Michalski R, Carbonell J, Mitchell T (eds) Machine kearning: an artificial intelligence approach. Tioga, Palo Alto

Moore AW (1990) Efficient memory-based learning for robot control. Ph.D. thesis, UCAM-CL-TR-209, Computer Laboratory, University of Cambridge, Cambridge

Niculescu-mizil A, Caruana R (2007) Inductive transfer for Bayesian network structure learning. In: Proceedings of the 11th international conference on AI and statistics (AISTATS 2007), San Juan

Reid MD (2004) Improving rule evaluation using multitask learning. In: Proceedings of the 14th international conference on inductive logic programming, Porto, pp 252–269

Reid MD (2007) DEFT guessing: using inductive transfer to improve rule evaluation from limited data. Ph.D. thesis, School of Computer Science and Engineering, The University of New South Wales, Sydney

Rosenblatt F (1962) Principles of neurodynamics: perceptrons and the theory of Brain mechanics. Spartan Books, Washington, DC

Samuel A (1959) Some studies in machine learning using the game of checkers. IBM J Res Develop 3(3):210–229

Silver D, Bakir G, Bennett K, Caruana R, Pontil M, Russell S et al (2005) NIPS workshop on “inductive transfer: 10 years later”, Whistler

Sutton R (1984) Temporal credit assignment in reinforcement learning. Ph.D. thesis, Department of Computer and Information Science, University of Massachusetts, Amherst

Sutton R, Barto A (1998) Reinforcement learning: an introduction. MIT Press, Cambridge

Taylor ME, Stone P (2009) Transfer learning for reinforcement learning domains: a survey. J Mach Learn Res 10:1633–1685

MathSciNet   MATH   Google Scholar  

Wang X, Simon HA, Lehman JF, Fisher DH (1996) Learning planning operators by observation and practice. In: Proceedings of the second international conference on AI planning systems (AIPS-94), Chicago, pp 335–340

Watkins C (1989) Learning with delayed rewards. Ph.D. thesis, Psychology Department, University of Cambridge, Cambridge

Watkins C, Dayan P (1992) Q-learning. Mach Learn 8(3–4):279–292

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The University of New South Wales, Sydney, NSW, Australia

Claude Sammut

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Faculty of Information Technology, Monash University, Melbourne, VIC, Australia

Geoffrey I. Webb

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Sammut, C. (2017). Credit Assignment. In: Sammut, C., Webb, G.I. (eds) Encyclopedia of Machine Learning and Data Mining. Springer, Boston, MA. https://doi.org/10.1007/978-1-4899-7687-1_185

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DOI : https://doi.org/10.1007/978-1-4899-7687-1_185

Published : 14 April 2017

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