Accounts Receivable Research Articles

A framework for modelling customer invoice payment predictions

By offering clients attractive credit terms on sales, a company may increase its turnover, but granting credit also incurs the cost of money tied up in accounts receivable (AR), increased administration and a heightened probability of incurring bad debt. The management of credit sales, although eminently important to any business, is often performed manually, which may be time-consuming, expensive and inaccurate. Such an administrative workload becomes increasingly cumbersome as the number of credit sales increases. As a result, a new approach towards proactively identifying invoices from AR accounts that are likely to be paid late, or not at all, has recently been proposed in the literature, with the aim of employing intervention strategies more effectively. Several computational techniques from the credit scoring literature and particularly techniques from the realms of survival analysis or machine learning have been embedded in the aforementioned approach. This body of work is, however, lacking due to the limited guidance provided during the data preparation phase of the model development process and because survival analytic and machine learning techniques have not yet been ensembled. In this paper, we propose a generic framework for modelling invoice payment predictions with the aim of facilitating the process of preparing transaction data for analysis, generating relevant features from past customer behaviours, and selecting and ensembling suitable models for predicting the time to payment associated with invoices. We also introduce a new sequential ensembling approach, called the Survival Boost algorithm. The rationale behind this method is that features generated by a survival analytic model can enhance the efficacy of a machine learning classification algorithm.

Invoice level forecasting with discrete survival methods for effective forecasting of account receivables in supply chain

The objective of this study is to obtain accurate and timely Account Receivables forecasts that can feed into the Cash Flow Forecasting (CFF) for a generic wholesales scenario in a supply chain company. The main components of the CFF are the Account Receivables (AR), Account Payables (AP) and Working Capital (WC). The focus of this work is on AR, in particular on how they contribute to the overall cash flow forecasting. The prediction of AR is based on the predicted payment date of each invoice, which can be obtained from the time to payment given the issue date. In this context, we propose the use of discrete survival methods for predicting the time to payment of issued invoices. To obtain more accurate predictions for time to payment, we fit a generalized linear mixed effects model with a time-varying baseline hazard function. This approach provides greater precision for subsequent account receivables predictions compared to the prediction based on simply the payment terms of each invoice, standard time series and classification models, and continuous-time survival methods. We demonstrate the approach on two financial data sets.

Unveiling the secrets: decoding the factors influencing MSMEs' accounting process and strategic debtors' management in Oman

PurposeThis study investigates determinant factors (influence of the third party (ITP), credit policy (CP) and follow-up process (FP)) of micro, small and medium enterprises' (MSMEs) accounting processes (APs) and strategic debtors' management.Design/methodology/approachThe study employed a sequential mixed-method approach, combining quantitative and qualitative methods for comprehensive data analysis. Phase I involved purposively selecting and interviewing 10 MSME owners or accountants to gain insights into debtors' management. In Phase II, a quantitative approach was used for collecting survey data from 72 MSME owners or accountants. Structural equation modeling-partial least squares (SEM-PLS) are the statistical tools that validated the study's proposed hypotheses.FindingsThe findings indicate that determinant factors (ITP, CP and FP) positively affect MSMEs' AP, significantly influencing strategic debtors' management. As a result, sole proprietors can use this study's findings to create value through systematic management of their debtors, guaranteeing sustainable firm growth and profitability.Practical implicationsThe sample has restricted to MSMEs in Oman, where the findings may not be generalized to other companies. Overall, the findings suggest that it requires considering the proposed determinant factor of MSMEs' AP to manage their debtors or accounts receivable (AR) to be more profitable.Originality/valueMSMEs play an essential role in the growth of any country's economy. However, the dearth of comprehensive research on influential factors of MSMEs' debtors’ management studies justifies the significance of the current study.

When Cash Is King—An Accounts Receivables App Accommodating Different Use Situations

ABSTRACT Today’s economic environment has accelerated an already changing working context for accounting. Collecting accounts receivables (AR) stands out, as it enables companies to turn invoices into cash. The objective of this article is to elaborate on both the most important use situations for an AR collection management and a subsequent app design accommodating these use situations. We take a healthcare product provider as our reference company and come up with nine use situations prioritized by an analytic hierarchy process as follows: (1) drive operational governance, (2) switch to a more proactive collection management, (3) increase order-to-cash process effectiveness, (4) set targets and monitor them, (5) automate AR process actions by an agent, (6) predict process errors in advance, (7) exchange information across departments, (8) set up a predictive liquidity management, and (9) automate workload tracking of AR process activities.

THE NEXUS BETWEEN CASH CONVERSION CYCLE, WORKING CAPITAL FINANCE, AND FIRM PERFORMANCE: EVIDENCE FROM NOVEL MACHINE LEARNING APPROACHES

This study examines the moderating role of the cash conversion cycle (CCC) while investigating the effects of working capital finance (WCF) on firm performance. Using more than 18000 observations from Chinese manufacturing firms, we computed several proxies for each variable of the study and merged these proxies via Principal Component Analysis (PCA) to create one master proxy for each variable. These master proxies contain all the essential information of individual proxies. Hence, they are more useful in producing reliable results than individual proxies. We also compared the predicting power of 15 econometric and machine learning estimators to select the best estimator. Based on the highest [Formula: see text] value, we used two machine learning estimators, K-Nearest Neighbors (KNN), and Artificial Neural Networks (ANN) for subsequent analysis. To strengthen the empirical analysis, we employed another machine learning technique, i.e., the Bagging method, which is an ensembling technique that uses multiple estimators simultaneously to improve the accuracy and generalization of results. We used the Bagging method with 50[Formula: see text]KNN estimators. The findings unfold that the sensitivity level of firm performance to short-term debts shifts when the CCC period of firms fluctuates. More precisely, the WCF–performance relationship in firms with extended CCC is more sensitive compared with this relationship in the full sample. On segregating the three elements of CCC, we observe that the WCF–performance relationship in firms carrying extended account receivable (AR) days or extended Inventory days is more sensitive than the full sample. These findings are useful for firms’ management for revising the optimal level of short-term debts according to CCC fluctuation. Also, the lending agencies can use these results for the assessment of firms’ risk levels and adjustment of the interest rate.

Optimum operational schedule and accounts receivable financing in a production supply chain considering hierarchical industrial status and uncertain yield

Consider a production supply chain system, in which a small- and medium-sized enterprise (SME) supplies products to and has accounts receivable (AR) from a core enterprise (CE), and the SME collateralizes the AR to a commercial bank (CB) for a loan to make production. We address and model hierarchical industrial status of the SME, CE, and CB and the uncertainties in SME's production yield and market demand. The production decisions on the order quantity of the CE and the production quantity of the SME are determined through a Stackelberg game, and the AR loan contract that specifies the loan quota and interest rate is determined by the CB by means of optimizing its expected profit while satisfying the requirements from the SME. We derive the analytical optimal conditions for these decisions and demonstrate the effects of the market value of AR, upper bound of loan interest rate, production yield uncertainty, and market demand uncertainty on the operational decisions and the AR loan contract through sensitivity analysis. The analytical approach and results and numerical findings provide decision tools and advice for production schedules and loan application of the SME as well as suggestions for intervention of macro regulations.

The Impact of Working Capital Management on the performance of Listed Firms: Evidence of an Emerging Economy

The aim/purpose of this scientific inquiry is to empirically examine the impact of working capital management (WCM) [cash conversion cycle (CCC), number of days inventory (INV), number of days account receivable (AR), number of days account payable (AP)] and control variables [sales growth (GROW), size (SIZE), leverage (LEV), current ratio (CR) fixed financial assets to total assets (FFA)] on firm performance (FP) [ROA, Tobin’s Q (TQ)] in the context of an emerging economy, Ghana. The research used a dynamic panel System of Generalized Method of Moment (GMM) to test the hypotheses. Utilizing financial data extracted from final accounts of 36 listed companies, spanning 2010-2019, the study examined WCM-performance-nexuses by following the methodologies of researchers/scholars in extant literature. Findings/Results indicates that, whilst INV, AR, LEV demonstrated negative/inverse/indirect associations with FP; AP, GROW, SIZE, CR, FFA depicted positive/direct associations with FP. CCC however, exhibited a quadratic concave relationship with ROA.

Value Relevance of Accounts Receivable Factoring and Its Impact on Financing Strategy under the K-IFRS after COVID-19 from the Perspective of Accounting Big Data

This study investigates whether recognized accounts receivable (AR) factoring is more value relevant than disclosed AR factoring. After the adoption of the Korean International Financial Reporting Standards (K-IFRS), AR factoring is recognized as short-term debt, thus increasing firms’ leverage ratio. Using cross-sectional equity valuation regressions, we find that recognized AR factoring is value relevant, unlike disclosed AR factoring. Moreover, the market value of equity and AR factoring are more significantly correlated in highly leveraged firms than in less-leveraged ones. Accounting data are important from the perspective of big data. In the accounting industry as well, professionals started realizing the implications of big data. The COVID-19 pandemic has created a health crisis and wreaked havoc in an already-fragile global economy. Although there is no way to predict exactly what the economic damage from the COVID-19 pandemic will be, there must be widespread agreement that it will have severe financial impact on every company. Global financial markets have suffered dramatic falls due to the pandemic, and highly leveraged companies are in serious need of financing. While diving deeper, sound debt management and debt transparency are critical to ensure debt sustainability. Thus, companies would be willing to use AR factoring in order to overcome this financial status. This study also shows that highly leveraged firms decrease AR factoring after K-IFRS adoption.

Financing a capital-constrained supply chain: factoring accounts receivable vs a BCT-SCF receivable chain

PurposeBlockchain-driven supply chain finance (BCT-SCF) has recently been receiving increased global attention. A number of business programmes have been carried out using this approach, but existing research has rarely focussed on this novel SCF model. This paper aims to fill this gap by proposing a mathematical model to analyse the value of BCT-SCF.Design/methodology/approachFirst, this paper considers a multi-period two-echelon supply chain consisting of a capital-constrained supplier and a newsvendor-like retailer. Then, two financing channels are proposed. The supply chain actors can either factor accounts receivable (AR) from a bank or obtain financing through a BCT-SCF platform by which AR can be converted into a bill receivable and used to make payment. Further, to investigate the preferences of all actors between the two financing channels, this paper compares the two channels and examines how the degree of financial constraints and the cost of implementing the BCT-SCF model impact the financing preferences of all actors.FindingsBCT-SCF model can help a supply chain realise its optimisation both in production and financing efficiency, the preference for the BCT-SCF model increases as the initial capital of supplier and the BCT-SCF platform usage fee rate decrease.Practical implicationsThis research bridges the gap between theoretical analysis of BCT-SCF and its realistic application. The results demonstrate that with the BCT-SCF model, a win-win situation among supply chain actors is possible, which is helpful for the supply chain to choose a more efficient financing channel.Originality/valueThis research introduces a mathematical model based on the “receivable chain” of CZBank and the model is set in a multi-period supply chain, which is the first time BCT-SCF has been considered as part of a more complex but realistic background setting.

Effects of cost-benefit analysis under back propagation neural network on financial benefit evaluation of investment projects.

To determine the influence of the weight of the economic effectiveness evaluation criteria of the major investments of listed enterprises, and provide new management ideas for the development of the follow-up enterprises, firstly, the financial benefit evaluation system of investment projects is analyzed and constructed, and the specific evaluation process is analyzed. Then, on this basis, the evaluation index is refined; the basic structure of BP neural network (BPNN) is introduced, and genetic algorithm is used to improve BP neural network. The cost-benefit analysis model is constructed based on the improved BPNN. The listed company A is taken as an example to analyze its development data in recent years, and then the data of 10 listed companies are taken as the research object. Matlab simulation software is used to train and verify the improved BPNN model, analyze and predict the weight value of the financial benefit index of the investment projects of these 10 companies, and then determine the index to improve the financial benefit of the investment projects. Under the analysis of the development data of listed company A in the past 10 years, it is found that the indicators of the listed company's profitability per share, debt risk operation ability, development, and growth ability in the past 10 years are in relatively stable state. The principal component analysis of its 20 secondary sub-indexes is conducted based on the four primary indicators: profitability, debt risk, operational capacity, and development and growth. A total of eight principal components including return on equity (ROE), return on assets (ROA), (total asset turnover) TATO, turnover of account receivable (AR), asset-liability ratio, interest protection multiple, income growth rate, and year-on-year rate of increase for complete assets are extracted. The average error between the final output value, the actual value, and the expected value is 0.0304 and 0.0169, respectively. The weight coefficient of the monetary benefits evaluation indicator of investment items is calculated, and the computed results show that year-on-year rate of increase for complete assets, TOTA, ROA, turnover of total capital, and ROE are important indexes in the financial benefit evaluation of investment projects. It indicates that to improve the financial benefit of investment projects of listed enterprises, it is necessary to enhance the year-over-year growth degree of total properties and ROA.

PENGGUNAAN PROFILE, STATISTIC, DAN STRATIFY D ALAM MENGAUDIT ACCOUNT RECEIVABLE (STUDI KASUS)

Computer Assisted Audit Techniques (CAATs) refers to the software ACL (Audit Command Language Version 9), is useful to calculate profile, statistics, and stratibi automatically. It uses to know the whether the occurrence of anomalies in an Account Receivable (AR) balances information. The field amount were used where the final balances ofAR are stored. This research has discussed the 161 records from 772 records have negative balances or 27.72% of total AR Balances. The research has examined the company policy against negative balances. Does a negative balance can be converted into goods or collateral accounts. If a positive balance equal to a negative balance, the AR balance will be zero. This leads to miss interpretation of the AR and affect the credibility of sales information, which the Information is as no sale occurred when credit sales did occurred.

Why do firms invest in accounts receivable? An empirical investigation of the Malaysian manufacturing sector

PurposeThe purpose of this paper is to investigate the factors that influence Malaysian manufacturing sector investment in accounts receivable (AR), an asset seen by many as one of the riskiest in any company’s balance sheet.Design/methodology/approachThe authors test several theories, related to AR, using a cross-section of 262 listed manufacturing firms over a period of five years (2007-2011). Both fixed and random effect approaches are adopted to deal with potential heterogeneity across firms.FindingsThe results show that investment in AR in Malaysia are influenced by firm size, short-term finance, sales growth and collateral. Profit, liquidity and gross margins have no role in affecting the decision of trade credit granting to customers. The results are inconsistent with previous studies. Size and short-term finance have a negative, rather than positive, impact. Liquidity and gross margins have no, rather than positive, effect. While profit and sales growth are predicted to feature a U-shaped relationship with investment in AR, the former is insignificant while the latter is strictly increasing. The only factor found to be consistent with prior studies is collateral.Research limitations/implicationsThe results have two principal implications. First, policy makers should not take a holistic view of the trade credit market. Given that policy makers aim to improve liquidity and trade, they should design policies that are not only country specific but also sector specific. As is clear from our results, what holds for other countries or sectors may not necessarily be true for the Malaysian manufacturing sector. This has important implications for policy makers in emerging economies.Practical implicationsInvestment in AR, in the Malaysian manufacturing sector, is impacted by many of the factors implied by either theory or empirical evidence. However, the main finding in this paper is that the Malaysian manufacturing sector is rather different. First, while liquidity and gross margin have been found to have a positive and significant effect on AR helping hand theory in prior studies, the results show that these two factors play no role in influencing the level of AR in the Malaysian manufacturing sector.Social implicationsUnlike the experience in developed economies, firms in our sample that have access to short-term finance are less likely to grant trade credit. This suggests that the helping hand theory does not hold as far as the Malaysian manufacturing firms are concerned: firm that have better access to short-term finance in Malaysia do not use trade credit to pass on the benefit to their customers by granting them trade credit.Originality/valueIt is unclear why firms invest in AR given the high risks of uncollectability as well as tedious, time-consuming and costly legal process for debt recovery compared to firms from more developed economies. This paper examines the reasons business-to-business lending, through AR, is widespread in Malaysia and investigates the factors that affect this decision despite the risk involved. To our knowledge, this is the first study to date that looks at the factors that influence AR level in the Malaysian manufacturing sector.

Factoring policy with constant demand and limited capital

AbstractThe convention of selling on credit (to customers) results in mass accumulation of accounts receivable (AR) on the balance sheet of firms. However, capital‐constrained firms do not have enough capital to invest in AR and cover the production cost incurred during the credit period. To finance future business, a capital‐constrained firm can employ factoring—a financing scheme wherein firms sell AR to a financial institution (called a factor) at a discount—to advance cash from AR. By formulating a time‐continuous model with constant demand over an infinite horizon, we study the factoring policy of firms in two practice‐based discounting schemes: automatic discounting and manual discounting. In the automatic discounting scheme, AR should be discounted at the same age, whereas this requirement is relaxed in the manual discounting scheme and how long in advance to discount the AR is contingent over time. In both discounting schemes, the firm needs to choose the timing of discounting in order to reach a capital‐unconstrained state as soon as possible. In the automatic discounting scheme, we approximate the firm's objective function with a quasi‐convex function whose error is demonstrated to be small. Based on this approximation, the firm's optimal decision and the factor's profit can be calculated more easily. When manual discounting is adopted, the firm should meet all the demand by exploiting factoring if the profit margin under immediate discounting is nonnegative. Given the same factoring discount rate, manual discounting is always more attractive to the firm than automatic discounting. However, the preference of the factor over the two discounting schemes depends on the factoring discount rate.

Tác động của chính sách vốn lưu động đến khả năng sinh lợi trên tổng tài sản của các doanh nghiệp ngành bất động sản Việt Nam

The article is aimed to examine the effect of working capital management on return on assets (ROA) of Vietnam real estate enterprises. The panel data used were of 35 real estate companies listed on Vietnam's stock market during 2010-2014. The results revealed that the components of enterprise’s working capital including the time (days) of accounts receivables (AR), the interval (days) of inventories (INV) and cash conversion cycle (CCC) are negatively correlated with ROA. In addition, ROA was affected by the firm size (SIZE), firm leverage (LEV) and the economic growth (GDP).

SMB AUTOMATION OF ACCOUNTS PAYABLE AND ACCOUNTS RECEIVABLE

ABSTRACT While many large organizations have streamlined their business process end-to-end with entity resource planning (ERP) systems, many small and medium size businesses (SMBs) have yet to even consider (much less implement) ERP systems due to the enormous cost and complexity. The monumental challenge that SMBs often face is that financial processes revolving around cash management, relating specifically to accounts payable (AP) and accounts receivables (AR), are too long and resource intensive. SMBs also tend to stretch other resources thin when forced to focus on the completion of financial tasks rather than focusing on their core competencies. Nevertheless, cash management strategies related to AP/AR are very important processes that span other important organizational units, and have a tremendous impact on treasury and solvency. Hence, in competitive environment it is the task of a SMB to gain efficiencies in order to remain profitable and continue as a going business concern. One such efficiency can be found in the automation of AP/AR processes. As such, this paper provides an overview of cash management strategies, common pitfalls and problems with manual processes and AP/AR systems, and the benefits to cash management through automation of the AP/AR processes. The paper further provides guidance on the desired attributes of an automated solution, systems analysis requirements, and steps for implementing an automation solution. Keywords ERP, SAP, Automation, Accounts Payable Automation, Accounts Receivable Automation

Comparing The Performance And Importance Of Trade And Inventory Policies Of Large Cap And Mid Cap Retailers

A companys trade and inventory policies determine the holding periods incurred to collect the accounts receivable, to convert inventories into sales, and to pay off the accounts payable. In this study, holding days for accounts receivable (AR), inventories (IV), and accounts payable (AP) of large cap and mid cap retailers for the years 2008 to 2012 were analyzed, compared, and regressed on return on assets (ROA). Results indicate that these holding days remain constant over time despite the late 2007 to mid-2009 U.S. recession. Comparative results show large cap retailers have shorter AR, same IV, and longer AP than mid cap retailers. Regression results conclude that for both large cap and mid cap retailers, IV is not correlated while shorter AR (collect sooner) is correlated with higher ROA. For large cap retailers, longer AP (pay later) is correlated with higher ROA, but for mid cap retailers, shorter AP (pay sooner) is correlated with higher ROA. Difference in bargaining power is suggested as the explanation for this discrepancy.

The relative importance of working capital management and its components to SMEs' profitability

PurposeThis paper aims to report the results of an investigation of the relative importance of working capital management, measured by the cash conversion cycle (CCC), and its components (inventory, accounts receivable and accounts payable) to the profitability of SMEs.Design/methodology/approachThe paper employs panel data regression analysis and a questionnaire survey on a sample of 133 Alternative Investment Market (AIM) listed SMEs. The panel data analysis utilises financial data for the period 2005 to 2009. The questionnaire survey results are based on 19 SMEs that responded.FindingsPanel data analysis results show that the management of accounts payable (AP) and accounts receivable (AR) is important for SMEs profitability. However, AP management is relatively more important than AR management. Inventory (INV) and CCC management is not important for SMEs profitability. Questionnaire results suggest that management of CCC and all its components is perceived as important for SMEs profitability. In terms of relative importance, AR management is most important, followed by AP, INV and CCC respectively.Research limitations/implicationsThe sample is limited to AIM listed SMEs, and therefore the findings cannot be generalised to all companies.Practical implicationsOverall the results imply that the SMEs need to concentrate their limited resources on managing AR and AP in order to be more profitable.Originality/valueThe study is the first to investigate the relative importance of WCM and its components to SMEs profitability and use both regression analysis and questionnaire survey.

Insurer Payment Lags to Physician Practices: An Opportunity to Finance Electronic Medical Record Adoption

Objectives: Physician payments from public and private payers are still largely paper-based, so significant payment lags are prevalent across all reimbursement systems. Electronic Medical Record use has the potential to reduce payment lag and improve health system performance. Methods: We use a claims data set of 100,000 covered lives from a national employer to examine mean accounts receivable (AR) payment times by provider type, physician specialty, and state. Eleven physician specialty disciplines are included in the analysis of mean AR and days of payment lag. We also include a coefficient of variation (CV) of physician claims processed by place of delivery and correlation (R2: r-squared) values between mean days of AR and mean CV. Results: We find significant variation in mean AR days by provider type and physician specialty. There is also a great deal of variation in payment processing lags by state. We find a range of correlations between mean AR days and mean CV by provider type of service (R2=0.6288), physician specialties (R2=0.662), and the state of service (R2=0.1247). Conclusions: Low EMR adoption rates impact how all payer types pay physicians. The elimination of paper-based claims (and their associated lag times) could be achieved through the adoption of basic practice management systems bundled with EMRs. There is sufficient health savings from shortening the AR lag time period for insurer payment to providers to finance EMR adoption even with without federal HITECH incentives to practices.

Impact of working capital on firms’ profitability

The correlation between working capital and profitability of firms is analyzed for the management of cash cycle management. Working capital is made by the three important factors, debtor, creditor and stock. When we include cash conversion cycle (CCC) to working capital then it becomes working capital management (WCM). Two sectors are selected as a sample size: automobile and oil and gas sector. The time period is from 2004 - 2009. Different variables affecting the profitability of firms are selected. In this study, networking capital, inventory turnover in days, average account receivable and financial asset to total assets (FATA) are taken as independent variables. The result shows positive movement of working capital (WC) on firm’s profitability. R shows the fitness of the model which is 49.95 %. The independent variables explain 49.95% of the model. Key words: Working capital management (WCM), cash conversion cycle (CCC), account receivable (AR).

On delineating supply chain cash flow under collectionrisk

The credit terms for accounts receivable (AR) offered by sellers to buyers not only create a time lag between supply chain physical flow and cash flow, but also increase the collection risk contributed by late collection and default. Previous studies describing the relationship between the two major supply chain flows did not consider the collection risk, which poses a serious challenge to companies with limited cash resources when seeking growth opportunities in sales. This study first delineates the relationship between the two flows during a growth period without imposing any constraints. A stochastic optimization model is then developed to observe the managerial implications of cash flow risk under tight cash constraints.