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The accurate prediction of incoming cash flows enables more effective cash management and allows firms to shape firms' planning based on forward-looking information. Although most firms are aware of the benefits of these forecasts, many still have difficulties identifying and implementing an appropriate prediction model. With the rise of machine learning algorithms, numerous new forecasting techniques have emerged. These new forecasting techniques are theoretically applicable for predicting customer payment behavior but have not yet been adequately investigated. This study aims to close this research gap by examining which machine learning algorithm is the most appropriate for predicting customer payment dates.

By using various machine learning algorithms, the authors evaluate whether customer payment behavior patterns can be identified and predicted. The study is based on real-world transaction data from a DAX-40 firm with over 1,000,000 invoices in the dataset, with the data covering the period 2017–2019.

The authors' results show that neural networks in particular are suitable for predicting customers' payment dates. Furthermore, the authors demonstrate that contextual and logical prediction models can provide more accurate forecasts than conventional baseline models, such as linear and multivariate regression.

Future cash flow forecasting studies should incorporate naïve prediction models, as the authors demonstrate that these models can compete with conventional baseline models used in existing machine learning research. However, the authors expect that with more in-depth information about the customer (creditworthiness, accounting structure) the results can be even further improved.

The knowledge of customers' future payment dates enables firms to change their perspective and move from reactive to proactive cash management. This shift leads to a more targeted dunning process.

To the best of the authors' knowledge, no study has yet been conducted that interprets the prediction of incoming payments as a daily rolling forecast by comparing naïve forecasts with forecasts based on machine learning and deep learning models.

A common distraction to contractors is that of cash management, and particularly incoming payments. In the extreme, a failure to manage a project's cash flows may bring about business failure. A contractor's financial viability rests on how actual payments from an owner are received. The purpose of this paper is to present a method for contractors to evaluate the punctuality and fullness of owner payments based on historical behaviour.

Owners are classified according to their late and incomplete payment practices. The payment profile of an owner, in the form of aging payments received based on claims, is used as a basis for the method's development. Regression trees are constructed based on three predictor variables, namely, the average time to payment following a claim, the total amount ending up being paid within a certain period and the level of variability in claim response times.

The method will be of interest to contractors concerned with managing their cash positions, as well as those persons looking at contractor‐owner relationships.

The method is intended to be used internally within a contractor's organisation to assist in decision making. The method can also be used by subcontractors, suppliers, and consultants. Owners may use the method reflectively to improve their own practices, to save time and cost by reducing disputes, and to develop better owner‐contractor relationships.

This paper represents an original approach, and an original contribution to contractor pre‐tendering risk analysis practices, and an extension to contractor claim‐payment analysis.

The past decade, with its unprecedented surge in financial activity and the occurence of financial crises, has been one of increased awareness on the part of both regulatory authorities and market participants of payment system's potential for propagating and amplifying financial shocks, especially in a cross‐border context. This has led the European Commission to propose a Directive aiming at reducing systemic risk in payment systems. Systemic risk is the risk that the illiquidity or failure of one participant in a payment system, and its resulting inability to meet its obligations when due, will lead to the illiquidity or failure of other participants in that system, with, at worst, knock‐on effects in the financial markets at large. This paper draws the background and describes the contents of the Commission's proposal to reduce systemic risk.

Management Information Systems, Innovation Management, Strategic Management, Strategic Leadership, Organizational Development, Financial Management, Risk Management and Corporate Governance.

MBA.

Musoni Kenya is a Kenyan microfinance institution (MFI) whose idea was conceived in The Netherlands. The Musoni business model is ICT-enabled, 100 percent mobile based, virtually paperless, and runs on an ICT platform housed in Musoni BV in Amsterdam, The Netherlands. It is built on tested mobile technology that allows huge savings on transaction and operating costs. Using mobile payments, clients receive and perform bank operations anytime anywhere. This saves transport costs, transaction time and increases safety as no cash has to be carried around sometimes in dangerous areas. The mobile payments enable clients to make large improvements in loan officer efficiency and makes tracing payments seamless, saving on administration costs. The Musoni branches are also inexpensive as they are only used as the point of contact with customers hence reducing the cost of setting up operations even in remote areas. These efficiencies are passed on to clients in the form of lower interest rates and to stakeholders in the form of good returns on investments. The company aims to use this knowledge, experience and global ICT platform to expand to other countries with a suitable mobile payments environment.

The objective of this case is to illustrate general innovation concepts in a leading microfinance company in Kenya. The case documents the innovation dilemma facing the management of the fledgling microfinance company in determining the pace of innovation and the feasibility of launching of a similar service in Uganda following the successful establishment and growth of the company in Kenya.

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Razorpay is a four-year-old Indian B2B fintech startup in digital payments which is venturing into digital lending. It aims to simplify digital payment flows involved in acceptance, processing, and disbursement of payments through superior technology and automation. This case details how Razorpay creates value for businesses by offering service convenience in B2B space. Razorpay started as a payment solutions provider, primarily known for their payment gateway. Over time the market for digital payment in India has matured, with multiple providers offering similar products making it difficult for Razorpay to sustain its growth by using technological leadership and service differentiation. To maintain its growth trajectory, Razorpay has launched multiple new products in the digital payment space as well as announced a foray into creating a marketplace for digital lending through launch of Razorpay Capital. The case provides details of the growth of Razorpay and its move from its core strength of payment gateway

The paper aims to explore reasons for choosing different payment instruments and pain points from using them in a Russian context. It proposes that given the expansion of the range of personal payment instruments, the choice for payment is now influenced by many factors, including the type of financial provider and potential benefits for consumers.

This paper is an exploratory study that uses data from the qualitative research conducted in three Russian cities (Moscow, Yekaterinburg and Saratov) based on 50 online payment diaries and 12 group discussions. It was complemented by the analysis of consumers’ posts on six relevant media platforms.

The results show that a bank card is a new must and people choose it for convenience, safety and access to online purchases inside and outside Russia. Cash is used out of habit or wherever cashless payments are either not free or unavailable. Reasons for smartphone pass-through wallet usage include speed, attribute of style and higher cashbacks.

The limitations of the study are similar to any qualitative research and include, in particular, lack of generalization. Proposed hypotheses might be further tested quantitatively on a representative sample.

The results might help providers of financial services in creating better quality products that address consumer pain points and in developing strategies that allow for the changing preferences of consumers.

To the authors’ knowledge, this is the first such study to consider reasons for choosing and pain points from using certain payment instruments in the emerging markets, in particular, Russia.

Fiserv, a pioneer in electronic payments, would like to increase the number of consumers who receive bills electronically. Currently, adoption is relatively low. To help guide their efforts, Fiserv managers have done extensive customer research and have segmented the market based on customer perceptions of e-billing. Students must recommend which segments to target and why. To support their recommendations, students must calculate the likely financial costs and benefits of adoption, estimate the likely returns for targeting different segments, and make targeting and positioning recommendations based on these calculations. Because Fiserv's direct customers are billers (such as utilities and credit card companies) and its end users are individual consumers, the case allows a focus on both B2B and B2C issues.

This case gives students the opportunity to estimate the relative profitability of different segments and to make targeting and positioning recommendations based on these calculations. It highlights the importance of assessing segments based on both quantitative and qualitative considerations. It also emphasizes the potential difficulties associated with targeting multiple segments at once.

Details an empirical study of the measurement of stockout costs in a distribution system and examines the incidence of stockouts in the supply of spare parts to the motor trade. Comprehensive studies were conducted in three major car workshops in Sweden and cost data collected from a variety of sources, including structured observation, quasi‐experiments, interviews and secondary data from internal information systems. On the basis of this research, a typology of stockout cost situations has been constructed and central concepts and measurement models developed. Results reveal that stockout costs are measurable in the system under review; information about stockout costs can have a significant impact on managerial decision making.