Debt Recovery Research Articles

PREDICTIVE TECHNIQUES AND ARTIFICIAL INTELLIGENCE MODELS IN THE MANAGEMENT OF DEFAULTY PUBLIC DEBTS: COMPARISON BETWEEN LINEAR REGRESSION AND DECISION TREES

The study analyzed focuses on the application of predictive models, specifically Linear Regression and Decision Trees, for the management of defaulted debts in the public context of the United States. The main objective of the work is to compare the effectiveness of these models in predicting the compliance of debts with more than 120 days, assisting in directing these debts to the Treasury Offset Program (TOP), an essential initiative for the government's financial recovery. The problem that the study addresses is the need for effective management of defaulted public debts, seeking to ensure compliance with public financial policies that promote compliance and the adequate redirection of financial resources to the government. This is particularly important to ensure fiscal transparency and accountability of federal agencies. The methodology used in the study was quantitative, based on the analysis of data on eligible debts extracted from reports of the US Treasury. Linear Regression and Decision Tree models were applied, with performance metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE) and Coefficient of Determination (R²). The study addressed financial and temporal variables to analyze the behavior of these debts and their compliance. The main results show that both models presented high accuracy in predictions, with Linear Regression showing a perfect fit (R² = 1) and Decision Trees standing out in capturing non-linear nuances of the data. The variable "Compliance Rate Amount" was identified as the most significant in the Decision Tree model, suggesting that the amount of the compliance rate is one of the most important factors in predicting the compliance of defaulted debts. This study offers valuable contributions to the field of public management, by demonstrating that the use of predictive models can help optimize debt recovery, improve fiscal transparency and contribute to more informed decision-making.

Investing in health workers: a retrospective cost analysis of a cohort of return-of-service bursary recipients in Southern Africa

BackgroundReturn-of-service (RoS) schemes are investment strategies that governments use to increase the pool of health professionals through the issuing of bursaries and scholarships to health sciences students in return for...

No right of prescription. debt recovery for luxury goods in Moldova at the end of the 16th century and the beginning of the 17th century

Political elites in Moldavia have always had a fondness for luxury items. Even from the beginning of Moldavia’s political existence the merchants who passed through these territories conducted extensive trade-work in this area and among those targeted in such activities were also Moldavian boyars. Having large dominion lands, Moldavian boyars managed to achieve significant profits by selling the goods they had obtained on their fields. Often, the commercial activities were performed on the basis of credit and the goods were purchased under further obligations. The study examines such a case during the reign of Iancu Sasul when Lupul Stroici bought several luxury items from some Greek merchants. The payment for them would be claimed in a few decades from his sons by Bernardo Borisi. In the process will also be involved Moldavia’s incumbent Metropolitan bishop Anastasie Crimca, who would redeem and pay down debt villages for the Moldavian boyar.

Enhancing Debt Recovery in Financial Institutions: A Delinquency String - Based Approach for Customer Risk Segmentation

Enhancing Debt Recovery in Financial Institutions: A Delinquency String - Based Approach for Customer Risk Segmentation

Automated debt recovery systems: Harnessing AI for enhanced performance

Amidst an upsurge in the quantity of delinquent loans, the financial industry is experiencing a fundamental transformation in the approaches utilised for debt recovery. The debt collection process is presently undergoing automation and improvement through the utilisation of Artificial Intelligence (AI), an emergent technology that holds the potential to revolutionise this sector. By leveraging machine learning, natural language processing, and predictive analytics, automated debt recovery systems analyse vast quantities of data, generate forecasts regarding the likelihood of recovery, and streamline operational processes. Debt collection systems powered by AI are anticipated to be compliant, precise, and effective. On the other hand, conventional approaches are linked to increasing expenditures and inefficiencies in operations. These solutions facilitate efficient resource allocation, customised communication, and rapid data analysis, all while minimising the need for human intervention. Significant progress has been made in data analytics, predictive modelling, and decision-making through the application of artificial intelligence (AI) in debt recovery; this has the potential to revolutionize the financial sector’s approach to debt management. The findings of the research underscore the criticality of artificial intelligence (AI) in attaining efficacy and precision, in addition to the imperative of a data-centric framework to fundamentally reshape approaches to debt collection. In conclusion, artificial intelligence possesses the capacity to profoundly transform the existing approaches utilized in debt management, thereby guaranteeing financial institutions’ sustained profitability and efficacy. The application of machine learning methodologies, including predictive modelling and logistic regression, signifies the potential of the system.

Debt Collection Model for Mass Receivables Based on Decision Rules—A Path to Efficiency and Sustainability

Debt collection companies buy overdue debts on the market in order to collect them and recover the highest possible amount of a debt. The pursuit of debt recovery by employees of collection agencies is a very demanding task. The aim of the article is to propose a rule-based model for managing the process of mass debt collection in a debt collection company, which will make the debt collection process more efficient. To achieve this, we have chosen a decision tree as a machine learning technique best suited for creating rules based on extensive data from the debt collection company. The classification accuracy of the decision tree, regardless of the possibility of acquiring rule-based knowledge, proved to be the highest among the tested machine learning methods, with an accuracy rate of 85.5%. Through experiments, we generated 16 stable rules to assist in the debt collection process. The proposed approach allows for the elimination of debts that are difficult to recover at the initial stage of the recovery process and to decide whether to pursue amicable debt collection or to escalate the debt recovery process to legal action. Our approach also enables the determination of specific actions during each stage of the proceedings. Abandoning certain actions or reducing their frequency will alleviate the burden on collection agency employees and help to avoid the typical burnout associated with this line of work. This is the path to making the organizational culture of a collection agency more sustainable. Our model also confirms the possibility of using data from debt collection companies to automatically generate procedural rules and automate the process of purchasing and collecting debts. However, this would require a larger set of attributes than what we currently possess.

A NEWCOMER TO MARITIME LAW: THE BEIJING CONVENTION ON THE INTERNATIONAL EFFECTS OF JUDICIAL SALES OF SHIPS

AbstractOn 7 December 2022, the United Nations General Assembly adopted a treaty known as the Beijing Convention governing the recognition abroad of the international effects of judicial sales of ships. This article explores the rationale for a specific Convention on this issue, and its interaction with other relevant international conventions. It then analyses the recognition approach adopted by the Convention and evaluates its potential strengths and weaknesses. While some provisions might need further consideration, the overall conclusion is that the Beijing Convention has the potential to strengthen the legal certainty of the purchaser's title obtained via this sale mechanism and thus help debt recovery. Although common law jurisdictions might find it at odds with some of their features, the expected benefits from ratifying it seem to outweigh these issues and are already prompting a steady number of signatures, including those of China, the European Union and Singapore.

Modeling credit risk with mixed fractional Brownian motion: An application to barrier options

Abstract This article aims to examine the pricing of debt and equity in the context of credit risk structural models, where the value of a company’s assets is influenced by mixed fractional Brownian motion. Three distinct scenarios are analyzed, including when the assets are trade-able, fixed, and subject to partial recovery of debt. The study culminates with the evaluation of debt pricing under the barrier model, where a bankruptcy threshold is established for the company’s asset value.

Indentured: Benefit deductions, debt recovery and welfare disciplining

AbstractThe UK social security system performs an important role as a creditor and debt collector for many benefit claimants, with more affected by deductions than formal welfare conditionality or sanctions. Deductions, then, are central to understanding low‐income life in the UK. With that in mind, this paper draws on a mixed‐methods project to explore the policy rationale, administration and effects of benefit deductions at a particular moment of crisis. Through new analysis of statistical releases, I evidence increasing indebtedness and an Inverse Care Law, whereby UK social security performs worst for those who need it most. Drawing on qualitative longitudinal ethnographic fieldwork conducted at the height of the cost‐of‐living crisis, I also evidence how deductions affect the lives and trajectories of low‐income claimants over time. The analysis offered details how deductions weaponize debt, often in ways that financialise benefit claimants and their entitlements that prove counter‐productive to the stated policy objectives of deductions: worsening the poverty‐debt trap and pushing people (further) away from the labour market.

Protecting Borrowers and Securing Stability: A Critical View of Debt Recovery Processes in India

India's lending landscape is marked by its central role in economic growth, so it is important to look closely at how debt is recovered. This study goes into detail about the laws that govern collecting debts, which is important for keeping the economy stable. Loans are often backed by collateral or financial assets, so strict supervision is needed to make sure the terms are followed. For example, financial institutions use internal debt recovery teams and legal options like the Debt Recovery Tribunal (DRT) set up by the "Recovery of Debt Due To Banks and Financial Institutions Act, 1993" to lower the risk of default. By looking at the reasons why lenders take these extreme steps, this study sheds light on the levels that require legal action and the complicated steps that are needed. It also looks at things from the point of view of borrowers, pointing out the consequences of not paying and possible ways to ease debt loads. This study shows how lending practices, regulatory frameworks, and debt recovery mechanisms all work together in a complex way by looking at legal safeguards and borrower protections. In the end, it aims to help people understand better how to handle debt in today's financial world.

Impact of Financial Liberalisation and Deregulation on Banking Sector in Pakistan

The paper analyses the market perception about the performance of Pakistani commercial banks in the wake of financial liberalisation and deregulation measures taken by the central bank over the last two decades. For this purpose, it uses the Survey approach. Out of 35 commercial banks, 15 banks have been chosen for analysis purposes. The paper mainly finds that key banking reforms have helped in removing the flaws in the banking sector of Pakistan. In particular, privatisation of banks, the deregulation and institutional strengthening measures, switching towards market-based monetary and credit management systems were instrumental in streamling banking operations. Similarly, the removal of restrictions on entry of private banks and operational restrictions on foreign banks also significantly contributed to the provision of quality banking services. The resultant competitive environment is the key factor behind improved efficiency of banks in Pakistan. The banks devised a number of strategies to respond to the competitive market conditions in the country. However, they faced various constraints in this regard, especially lack of managerial skills, and in hiring IT professionals, etc. As a part of future strategy, banks are likely to form more strategic alliances with foreign banks and undertake the expansion of business through widening of branch network. Despite a number of successful stories on different fronts, the central bank could not succeed in narrowing down the banking spread which is one of the key measures of banking performance. High domestic inflation, large non performing loans (NPLs) of banks, high administrative expenses, larger intermediation costs, and poor debt recovery are identified as important factors behind widening of banking spread. 1. INTRODUCTION The banking sector of an economy generally performs three primary functions: the facilitation of payment system, mobilisation of savings, and allocation of funds to stakeholders like government, investors, consumers, and business community who can utilise them for generation of economic activities.1 By virtue of its pivotal role, the banking sector can exert its positive influence on various segments of an economy. This gives it a privileged position among other sectors. In view of its significant role, economic managers always endeavour to devise such policies which could help provide the sector a level-playing field, thereby enabling it to operate on sound, efficient, and competitive footings. As in other emerging market economies, banks have been playing an important role in Pakistan also. However, the financial landscape of the country changed significantly in the 1970s with the nationalisation of Pakistani commercial banks. The Government also took various regressive steps, which included, among others, the initiation of subsidised bank credit schemes, the introduction of a complex system of credit ceilings, and imposition of controls on interest rates.3 These policy measures severely affected the efficiency of the banks. By the end of the 1980s, it was felt that Pakistan’s banking sector was not able to meet adequately the financial needs of the country

The criticality of credit recovery in banking system stability: A GMM estimation

This study examines the criticality of credit recovery on banking system stability in Nigeria using data from 2007 to 2020. The system generalized method of moments (SYS-GMM) was used to analyze the data and determine the presence of cointegrating relationships among the variables. The findings revealed that credit recovery positively and significantly influences banking system stability. It was discovered that the recovery rate positively and significantly impacts the banking system stability in the short and long terms. In contrast, recovery expense only negatively and significantly affects the banking system in the short term. This implies that credit recovery is critical in business endeavors and operations for banks. Hence, the longer the delay in recovering bad debts, the more banks lose the opportunity to earn income from substitute investments, and collateral may lose value. Therefore, this study recommends that banks' management aggressively pursue the repayment of bad loans and maintain favorable and minimal recovery expenses, particularly with external debt recovery agents, to ensure that the cost of recovery is not higher than the amount recovered.

Enhancing Debt Recovery in Financial Institutions: A Delinquency String - Based Approach for Customer Risk Segmentation

Enhancing Debt Recovery in Financial Institutions: A Delinquency String - Based Approach for Customer Risk Segmentation

Recovery Of Debts Due to Banks and Financial Institutions With Special Emphasis on SARFAESI Act, 2002

Recovery Of Debts Due to Banks and Financial Institutions With Special Emphasis on SARFAESI Act, 2002

Credit Management as a Predictor to the Sustainability of Microfinance Institutions

Purpose: This study was carried out to understand the relationship between credit management and the financial performance of Micro Finance Institutions, taking a sample of 43 MFIs administrators in the towns of Dschang, Mbouda and Bafoussam of Cameron.
 Method: The study applied a quantitative research design, where questionnaires were used. Data was collected from both primary and secondary sources. Data was processed and analyzed using formal tables, pie charts, multiple linear regressions, and Anova, chi-squared, Cronbash’s Alpha, Pearson tests were carried out to understand the relationship between credit management and financial performance Microfinance Institution in Cameroon. A total of 43 respondents were considered out of the entire population.
 Findings: Findings indicate that credit policies, debt recovery technique mechanisms are significant and positively related to the portfolio quality of MFIs in Cameroon. More specifically, good lending policies such as proper study of the loan documents, group liability, monitoring repayment frequency would increase financial performance. In addition, recovery techniques such as constant customer reminder, prolonging recovery period, penalties etc, enabled MFIs to perform financially well.
 Originality value: This study explores and shed light on an important factor responsible in majority for the unpaid installment debts in the Cameroon context; the MFI itself through the personal and friendly relationships with clients. Thus we provide practical strategies for the recovery of these debts till the last franc. In addition, we show that these strategies increase MFI’s sustainability across their profitability.

Общественная опасность криминальных посягательств в сфере деятельности по возврату просроченной задолженности

The thesis about the increased public danger of acts related to the illegal return of overdue debt obligations is substantiated and proved in the work. The author identifies qualitative and quantitative criteria, which include the circumstances on which the assessment of the degree of public danger of criminal attacks in the area of economic activity under study depends. The results of the study are based on our own study of empirical materials obtained during the study of judicial practice (103 published sentences in criminal cases for crimes involving illegal activities for the recovery of overdue debts).

The obligations «not to alienate the thing» and «not to assign» as separate types of negative obligations

The relevance of the stated topic is determined by the lack of scientific developments in the national law regarding the obligations "not to alienate the thing" and "not to assign". These obligations are types of negative obligations that consist in refraining from certain actions. The subject of the obligations "not to alienate the thing" and "not to transfer the right" is to refrain from the act of transferring a certain thing or right to a third party by the debtor. The author has set a goal to investigate the specifics of obligations regarding non-alienation of a thing (pactum de non alienando) and non-assignment of rights (pactum de non cedendo). In the course of the study, the author offered answers to the questions regarding the features of the obligations "not to alienate" and "not to assign " compared to other negative obligations, regarding the validity of the acts committed in violation of these obligations and the legal consequences for the debtor who, contrary to the prohibition alienated the thing or assigned the right. The following types of obligations were analyzed: "not to alienate the subject of the pledge without the consent of the creditor", "not to sell the goods, in respect of which there is a clause on the retention of ownership by the seller until full payment", "not to assign the right of claim to a third party". All these obligations are accessorymas they are established only if the main obligation exists. The article clarifies that the consequences of the specified obligations differ depending on the powers of the obligated person. The pledgor, in whose possession the object of pledge remains, is the owner of the thing, and therefore cannot lose the authority to dispose of it. The author emphasizes that the absence of the pledgee's consent to its alienation should not affect the validity of the deed itself regarding the alienation of the subject of the pledge. Alienation by the pledgor of the pledged thing does not terminate the pledge, which is the right to someone else's property, and therefore the creditor under the pledge does not lose the opportunity to collect in case of non-fulfillment of the main obligation by the debtor. However, in the judicial practice of Ukraine, such deeds regarding alienation of the subject of pledge without the consent of the creditor are recognized as invalid. Regarding the transaction by the buyer of the goods, the ownership of which is retained by the seller until payment (on the basis of the norm of Article 697 of the Civil Code of Ukraine), a different solution is proposed: since the buyer has not acquired the right of ownership, he cannot transfer it to another person, and therefore the transaction regarding alienation of this product will not create legal consequences for the acquirer. Acts committed in violation of the obligation "not to assign" should be considered valid, since the contractual prohibition in itself is not a reason for declaring the acts invalid. The lack of consent of the debtor for the main obligation does not affect the validity of the assignment agreement. This conclusion is not supported by judicial practice, in which debt recovery is denied to new creditors who have acquired the right of claim, which the original creditor withdrew without the debtor's consent, contrary to the terms of the contract. In case of violation of the obligations "not to alienate the thing" and "not to assign", general legal consequences should be аpplied, in particular, the collection of the contractual penalty, termination of the main contract, collection of damages.

Statute - Barred Collection: A Machine Learning Approach for Enhanced Debt Recovery Compliance

Statute - Barred Collection: A Machine Learning Approach for Enhanced Debt Recovery Compliance

An empirical snapshot of English corporate insolvencies

AbstractThe article presents an empirical study of English corporate insolvencies initiated between December 2016 and December 2018. The research focuses on creditors' voluntary liquidations (‘CVLs’), the most frequently occurring insolvency procedure. It also looks at a few administrations and compares findings with CVLs to analyse which procedure may lead to better returns to creditors. The article highlights key statistics such as the average costs of procedures and the impact of the prescribed part fund on distribution. Further, the study assesses HMRC's potential debt recovery following its return as a preferential creditor and discusses whether Crown preference is justified. Given that data analysis on the practicalities of distribution during insolvency is lacking, the empirical study arguably helps fill a gap in the literature. Essentially, the article provides quantitative data on the practicalities of distribution and assesses the impact of the order of priority on repayment to creditors.

Effects of Debtors’ Management on Financial Performance of Savings and Credit Cooperative Organizations in Nandi County, Kenya

Savings and Credit Cooperative Organizations (SACCOs) are key components of the economy and social development. However, it’s experiencing both internal and external issues that should be handled. The study attempted to identify the effects of business financial parameters on the performance of SACCOS in Nandi County. Specifically, the study investigated the effects of the management of loan debtors on the financial performance of SACCOs in Nandi County. The study was guided by liquidity preference theory, human capital theory, efficient structure theory, and Pecking order theory. The study used a descriptive research design to examine all the influences on financial performance. The target population was therefore all the ten SACCOS in Nandi County registered and licensed as of January 2023, where eighty-seven employees were randomly sampled to participate. The primary sources of data collection used in data collection were structured questionnaires as the main tool. Content validity was used to determine the validity, while Cronchbach’s alpha coefficient was used to determine the reliability of the research instrument. The data was analyzed using both descriptive and inferential statistics. For descriptive statistics, tables and narratives were used, and for inferential statistics, correlations and regression analyses were used. The Statistical Package for Social Sciences software (SPSS) helped with data analysis. The study findings indicated that there was a positive and significant effect of loan debt management on the performance of Saccos in Nandi County (t = 9.833, P < 0.05). The study recommended that Sacco management should maintain a credit policy manual to help in debt recovery and even avail the required resources needed for effective management of loan debtors.