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Transaction monitoring system set up by financial institutions is one of the most used ways to track money laundering and terrorist financing activities. While being effective to a large extent, the system generates very high false positives. With evolving patterns of financial transactions, it also needs effective mechanism for scenario fine-tuning. The purpose of this paper is to highlight quantitative method for optimizing scenarios in money laundering context. While anomaly detection and unsupervised learning can identify huge patterns of false negatives, that can reveal new patterns, for existing scenarios, business generally rely on judgment/data analysis-based threshold finetuning of existing scenario. The objective of such exercises is productivity rate enhancement.

In this paper, the authors propose an approach called linear/non-linear optimization on threshold finetuning. This traditional operations research technique has been often used for many optimization problems. Current problem of threshold finetuning for scenario has two key features that warrant linear optimization. First, scenario-based suspicious transaction reporting (STR) cases and overall customer level catch rate has a very high overlap, i.e. more than one scenario captures same customer with different degree of abnormal behavior. This implies that scenarios can be better coordinated to catch more non-overlapping customers. Second, different customer segments have differing degree of transaction behavior; hence, segmenting and then reducing slack (redundant catch of suspect) can result in better productivity rate (defined as productive alerts divided by total alerts) in a money laundering context.

Theresults show that by implementing the optimization technique, the productivity rate can be improved. This is done through two drivers. First, the team gets to know the best possible combination of threshold across scenarios for maximizing the STR observations better coverage of STR – fine-tuned thresholds are able to better cover the suspected transactions as compared to traditional approaches. Second, there is reduction of redundancy/slack margins on thresholds, thereby improving the overall productivity rate. The experiments focused on six scenario combinations, resulted in reduction of 5.4% of alerts and 1.6% of unique customers for same number of STR capture.

The authors propose an approach called linear/non-linear optimization on threshold finetuning, as very little work is done on optimizing scenarios itself, which is the most widely used practice to monitor enterprise-wide anti-money laundering solutions. This proves that by adding a layer of mathematical optimization, financial institutions can additionally save few million dollars, without compromising on their STR capture capability. This hopefully will go a long way in leveraging artificial intelligence for further making financial institutions more efficient in controlling financial crimes and save some hard-earned dollars.

This paper aims to introduce two draft concepts, spiritual self-managed teams and holacracy, as solutions for reducing the friction within neo-enterprises and the issues of hierarchical leadership dynamics and mindset present within orthodox organizations’ structures and communications and they help businesses to grow further, achieve their goals, and become self-sustainable.

To counter the popular maxim, “management and leadership are what cause many problems for organizations and its people,” the authors argue for six novel propositions constructed around the two draft concepts following a critical review and meta-analysis of notable business/leadership cases, presented in a narrative-based descriptive style.

This article presents a list of novel propositions for entrepreneurs, managers and researchers who may investigate further and possibly test it in organizations. The findings merit opening new frontiers for perceiving leadership, group dynamics and decision-making in organizations using spiritual ideas.

Adopting the paper’s content can benefit organizations’ management, efficiency and sustainability. Implementation of the two novel concepts – spiritual self-managed teams and holacracy – and their combination can significantly reduce friction within organizations’ structures and communications.

Good quality input data is critical to developing a robust machine learning model for identifying possible money laundering transactions. McKinsey, during one of the conferences of ACAMS, attributed data quality as one of the reasons for struggling artificial intelligence use cases in compliance to data. There were often use concerns raised on data quality of predictors such as wrong transaction codes, industry classification, etc. However, there has not been much discussion on the most critical variable of machine learning, the definition of an event, i.e. the date on which the suspicious activity reports (SAR) is filed.

The team analyzed the transaction behavior of four major banks spread across Asia and Europe. Based on the findings, the team created a synthetic database comprising 2,000 SAR customers mimicking the time of investigation and case closure. In this paper, the authors focused on one very specific area of data quality, the definition of an event, i.e. the SAR/suspicious transaction report.

The analysis of few of the banks in Asia and Europe suggests that this itself can improve the effectiveness of model and reduce the prediction span, i.e. the time lag between money laundering transaction done and prediction of money laundering as an alert for investigation

The analysis was done with existing experience of all situations where the time duration between alert and case closure is high (anywhere between 15 days till 10 months). Team could not quantify the impact of this finding due to lack of such actual case observed so far.

The key finding from paper suggests that the money launderers typically either increase their level of activity or reduce their activity in the recent quarter. This is not true in terms of real behavior. They typically show a spike in activity through various means during money laundering. This in turn impacts the quality of insights that the model should be trained on. The authors believe that once the financial institutions start speeding up investigations on high risk cases, the scatter plot of SAR behavior will change significantly and will lead to better capture of money laundering behavior and a faster and more precise “catch” rate.

Inter‐organizational innovation networks provide opportunities to exploit complementary resources that reside beyond the boundary of the firm. The shifting locus of innovation and value creation away from the “sole firm as innovator” poses important questions about the nature of these resources and the capabilities needed to leverage them for competitive advantage. The purpose of this paper is to describe research into producing design‐oriented knowledge, for configuring inter‐organizational networks as a means of accessing such resources for innovation.

This exploratory investigation conflates emerging constructs and themes analytically induced from a systematic survey of 142 scholarly and practitioner articles and 45 expert interviews with senior professionals operating in the biopharmaceuticals industry.

The findings identify seven theoretically and empirically grounded technological rules associated with effective inter‐organizational networking for innovation. They embody evidence ex post of networking theory and practice. Based on van Aken's seminal work, they comprise design‐oriented knowledge to provide a solution architecture of viable action options for managers, a priori, to purposefully design innovation networks. Collectively these rules represent a tentative taxonomy, a means of classifying design principles, to assist managers in navigating their decision‐making processes.

This study demonstrates the need for explicit design‐oriented knowledge for configuring inter‐organizational networks. Finally, the implications of the findings for strategic management theory are discussed from a dynamic capabilities view. The significance of a dynamic capability which addresses the renewal of network‐specific resources is highlighted.

The case describes the situation of Vasudha Kumar, Manager – Background Vetting at Praxum Services Limited. She is surprised by the unexpected resignation of her team member a few days before she has to leave for her maternity break. In a small team, how does she deal with this unanticipated resignation? She wonders if this is ethical. Kumar, the protagonist of the case had to handle multiple crisis situations along her journey. How will she overcome challenges of managing the new gen workforce?

The purpose of the study is to understand what impact the first COVID-19 lockdown in 2020 had on the income and food security of rural households in India and whether it differed across socioeconomic factors. Moreover, the study assesses the needs of rural households and determines whether they had received adequate support services during the lockdown.

The study adopted a repeated-measures quantitative survey design with 1,319 rural women from 16 states of India and conducted a telephonic survey.

The lockdown had directly impacted rural households' employment status and income but the impact differed depending on the type of employment. For example, working in a salaried job or on one's farm led to a lower likelihood of a complete decline in income in states other than Kerala as the lockdown continued. The study also revealed a change in the pattern of food consumption, with higher consumption of subsidized staple foods. It also became evident that the aid announced by the government reached the rural population with some delay.

A limitation of the study was that many respondents refused to participate in phase 2, which reduced the sample size when comparing the two phases because the women did not own mobile phones. Instead, they were using their husbands' phones.

The study's findings can help better understand the needs of rural populations during crises, such as the COVID-19 pandemic. This can help to plan better and build preventive actions for such populations once their needs are understood. In addition, this can aid disadvantaged people for a minimal level of preparedness and security during such a crisis in the future.

The recent coronavirus disease 2019 (COVID-19) pandemic poses numerous challenges to supply chains. This pandemic is quite unique when compared to previous epidemic disruptions and has had a severe impact on supply chains. As a result, the operational challenges (OCs) caused by COVID-19 are still unknown among practitioners and academics. It is critical to comprehensively document current OCs so that firms can plan and implement strategies to overcome them. Consequently, this study systematically identifies and ranks COVID-19-related OCs.

This study uses an integrated methodology combining expert interviews and the best-worst method (BWM) to analyze the results. The data have been collected from the electronics industry of Bangladesh, an emerging economy. This study also conducts a sensitivity analysis to check the robustness of the results.

The results reveal 23 COVID-19-related OCs under five categories: sourcing, production and inventory management, demand management and distribution, return management and after-sales service, and supply chain-wide challenges. The quantitative investigation reveals that overstock in finished goods inventory, low end-customer demands, order cancellations from dealers and retailers, high inventory holding costs and lack of transportation are the top five OCs.

The findings will help practitioners to understand the OCs and allow them to prepare for future major disruptions and formulate long-term strategies for operations during and after the COVID-19 pandemic.

This study contributes to the literature on supply chain complexity and challenges by considering a major pandemic outbreak. Moreover, the study also contributes to the knowledge on emerging economies, which have been largely neglected in the current literature.

Small businesses and start-ups have started to reopen post coronavirus disease 2019 (COVID-19) lockdowns but are facing numerous challenges mainly due to changed customer preferences and the need to fine-tune the business models. This research aims to identify the important aspects that start-ups need to focus on, as they weather the COVID-19 pandemic storm.

Research uses constructivist grounded theory methodology to analyse data collected through in-depth semi-structured interviews with entrepreneurs and senior employees at start-ups. A conceptual model based on nine categories impacting a start-up’s performance is investigated. Interview memos are thematically analysed to identify repeated ideas, concepts or elements that become apparent.

Study reveals that employees’ and customers’ safety, prudent cost management and online presence/doorstep services are key for start-ups to succeed today's changed business landscape due to COVID-19.

Findings act as a practical guide for start-ups in setting mechanisms, optimizing operations and fine-tuning strategy to address COVID-19 challenges. Start-ups are advised to evaluate the implications of the three findings on their respective businesses to successfully tackle the challenges posed by COVID-19.

This research, being cognizant of a start-up’s unique characteristics and nuances, takes a fresh approach to identify key aspects that start-ups need to focus on and fine-tune in the wake of COVID-19. The paper enriches scientific research of understanding impact of COVID-19 on organizations by specifically surfacing how start-ups can learn and adapt by knowing how other start-ups are surviving today.

This case studies the growth of OYO Hotels (OYO) to illustrate the operational processes necessary to succeed in the service sector. The case allows for a discussion of employee- and customer-management systems, tech-driven solutions, and profit drivers. The material unfolds OYO's growth and its solution for making economy hotels discoverable and bookable online.

The case raises a series of questions around OYO's business model, its ability to translate across global markets, and growth potential. It has been successfully taught in a second-year MBA class on the management of service operations.

There are two theoretical concepts that can be taught in this case.The new approach to teaching entrepreneurship is termed “lean start-up” and “hypothesis-driven entrepreneurship.” The business model canvas is a core tool of this approach. This framework defines nine key components of a successful business strategy. These components include defining value propositions; identifying customer segments; identifying channels; maintaining customer relationships; defining key activities, key resources and key partners; understanding the revenue model of the business; and the organization’s cost structure. This is considered to be a rigorous approach to learning about and developing a new venture.The other theoretical approach that can be discussed through this case is the link between uncertainty and entrepreneurial growth. These theories associate the willingness of entrepreneurs to bear the perceived uncertainty associated with entrepreneurial acts as representative of the belief-desire model. There is a need for entrepreneurs to experiment and search for alternative paths forward in order to counter this uncertainty. Systematic search processes to discover relevant information will strengthen this process.

This case is based on primary data collected through interviews with company personnel. The company consented freely to the use of their data in the case. The authors have no connection with the company. The four student coauthors had previously pursued an internship with the company and had worked on the machine learning analysis part.The two faculty coauthors in the case contacted the company after the internship and discussed the opportunity to write the case on the company. One of the faculty then interviewed key personnel in the company, including one of the co-founders.

Xoxoday is a technology company that provides employee rewards and corporate gifting to its customers. The company was started by Sumit Khandelwal, Manoj Agarwal, Abhishek Kumar and Kushal Agarwal. In 2018, the company reinvented itself as an experiential gifting company.The company faced some challenges during the lockdowns imposed due to COVID-19. Khandelwal knew that they had to try something new to achieve higher growth in the future. He wondered if higher usage of technology was the solution. It was necessary for them to carve a new path in these times.

This case study can be used at the undergraduate level in courses relating to entrepreneurship strategy and business models for entrepreneurs.The case can be used to highlight the dilemmas faced by entrepreneurs due to unforeseen crises. This case is relevant for classes that will discuss growth crises and out-of-the-box solutions for unprecedented crisis situations.