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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.

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.

In this research, the authors apply artificial neural networks (ANNs) to uncover non-linear relationships among factors that influence the productivity of ragpickers in the Indian context.

A broad long-term action research program provides a means to shape the research question and posit relevant factors, whereas ANNs capture the true underlying non-linear relationships. ANN models the relationships between four independent variables and three forms of waste value chains without assuming any distributional forms. The authors apply bootstrapping in conjunction with ANNs.

The authors identify four elements that influence ragpickers’ productivity: receptiveness to non-governmental organizations, literacy, the deployment of proper equipment/technology and group size.

This study provides a unique way to analyze bottom of the pyramid (BoP) operations via ANNs.

This study provides a road map to help ragpickers in India raise incomes while simultaneously improving recycling rates.

This research is grounded in the stakeholder resource-based view and the network–individual–resource model. It generalizes these theories to the informal waste value chain at BoP communities.

After completion of the case study, the students would be able to comprehend the crisis at hand for Milkbasket, why did it resist Reliance’s takeover in the first place, then to evaluate the pros-cons and future prospects for the organization post-acquisition; to evaluate from an ethical standpoint the process of mergers and acquisitions using ethical frameworks to understand how, when, to whom and through what processes do mergers and acquisitions qualify the test of being ethical; and to analyse different hostile takeovers, especially through tender offers, proxy contests and toehold bidding strategy in this case.

Founded in 2015, Milkbasket was a micro-delivery start-up based in Gurugram (near New Delhi), India. Milkbasket would let its subscribers order till midnight and deliver groceries, milk and other everyday essentials to its subscribers before 7 a.m. next day. It had burnt a lot of cash and was facing difficulty in getting investors; as such they were engaged in discussions with many companies. Two of them – Reliance Retail Venture Limited and BigBasket – were not accepting the proposed valuation, but Milkbasket got term sheets from other two companies.

This case study can be used for graduate courses on strategic management, business ethics and corporate governance. This case study can also be used in corporate finance course to highlight the importance of making ethical/responsible judgements to protect stakeholder interests.

Teaching notes are available for educators only.

CSS 3: Entrepreneurship.

The purpose of this study is to calculate the effect of different packet sizes 256, 512, 1,024 and 2,048 bytes on a large-scale hybrid network and analysis and identifies which routing protocol is best for application throughput, application delay and network link parameters for different packet sizes. As the routing protocol is used to select the optimal path to transfer data packets from source to destination. It is always important to consider the performance of the routing protocol before the final network configuration. From the literature, it has been observed that RIP (Routing Information Protocol) and OSPF (Open Shortest Path First) are the most popular routing protocols, and it has always been a challenge to select between these routing protocols, especially for hybrid networks. The efficiency of routing protocol mainly depends on resulting throughput and delay. Also, it has been observed that data packet size also plays an essential role in determining the efficiency of routing protocol.

To analyse the effect of different packet sizes using two routing protocols, routing information protocol (RIP) and open shortest path first (OSPF) on the hybrid network, require detailed planning. Designing the network for simulate and then finally analysing the results requires proper study. Each stage needs to be understood well for work accomplishment. Thus, the network’s simulation and evaluation require implementing the proposed work step by step, saving time and cost. Here, the proposed work methodology is defined in six steps or stages.

The simulation results show that both routing protocols – RIP and OSPF are equally good in terms of network throughput for all different packet sizes. However, OSPF performs better in terms of network delay than RIP routing protocol in different packet size scenarios.

In this paper, a fixed network of 125 objects and only RIP and OSPF routing protocol have been used for analysis. Therefore, in the future, a comparison of different network sizes can be considered by increasing or decreasing the number of objects in the proposed network. Furthermore, the other routing protocols can be used for performance evaluation on the same proposed network.

The analysis can be conducted by simulation of the network, enabling us to develop a network environment without restricting the selection of parameters as it minimizes cost, network deployment overhead, human resources, etc. The results are analysed, calculated and compared for each packet size on different routing protocol networks individually and the conclusion is made.

The purpose of this paper is to understand the dimensions of the e-fulfillment process and its influence on customers in pure e-tailing; to classify the pertinent literature that has evolved over time addressing relevant managerial issues; and to identify the gaps between the practices prevalent in the e-fulfillment and those suggested by academicians to develop insights for future research.

A critical systemic literature review approach was used for the study with quantitative and qualitative analysis.

The authors identified seven dimensions of e-fulfillment in the literature on pure e-tailing: e-business quality, product quality, pricing, availability, timeliness, condition and ease of return and explored its linkages with shopping satisfaction and repurchase intention of customers in e-tailing.

The study was skewed toward an empirical approach. The study does not include many of the analytical models in this space.

This study helps e-tailers, academicians and practitioners understand critical dimensions of e-fulfillment and its influence on customers in the pure e-tailing setting in order to design customer-centric e-fulfillment architecture.

The study identified seven dimensions of e-fulfillment in the literature and explored its influence on shopping satisfaction and repurchase intention of customers in pure e-tailing. This is the first compilation of standalone/isolated studies available in the literature to provide e-tailers and academicians meaningful insights into e-fulfillment in the pure e-tailing setting.

This paper aims to examine and compare extant literature on the application of multi-criteria decision-making (MCDM) techniques in humanitarian operations (HOs) and humanitarian supply chains (HSCs). It identifies the status of existing research in the field and suggests a roadmap for academicians to undertake further research in HOs and HSCs using MCDM techniques.

The paper systematically reviews the research on MCDM applications in HO and HSC domains from 2011 to 2022, as the field gained traction post-2004 Indian Ocean Tsunami phenomena. In the first step, an exhaustive search for journal articles is conducted using 48 keyword searches. To ensure quality, only those articles published in journals featuring in the first quartile of the Scimago Journal Ranking were selected. A total of 103 peer-reviewed articles were selected for the review and then segregated into different categories for analysis.

The paper highlights insufficient high-quality research in HOs that utilizes MCDM methods. It proposes a roadmap for scholars to enhance the research outcomes by advocating adopting mixed methods. The analysis of various studies revealed a notable absence of contextual reference. A contextual mind map specific to HOs has been developed to assist future research endeavors. This resource can guide researchers in determining the appropriate contextual framework for their studies.

This paper will help practitioners understand the research carried out in the field. The aspiring researchers will identify the gap in the extant research and work on future research directions.

To the best of the authors’ knowledge, this is the first literature review on applying MCDM in HOs and HSCs. It summarises the current status and proposes future research directions.

Quantitative objective studies on the problem of celebrity selection are lacking. Furthermore, existing research does not recognize the group decision-making nature and the possibility of customer involvement in celebrity or influencer selection for social media marketing. This study conceptualizes celebrity selection as a multi-attribute group decision-making problem while deriving the final ranking of celebrities/influencers using interactive and flexible criteria based on the value tradeoff approach. The article thus proposes and demonstrates a quantitative objective method of celebrity selection for a brand or campaign in an interactive manner incorporating customer's preferences as well.

Each decision-maker's preferences for celebrity selection criteria are objectively captured and converted into an overall group preference using a modified generalized fuzzy evaluation method (MGFEM). The final ranking of celebrities is then derived from an interactive and criteria-based value tradeoff approach using the flexible and interactive tradeoff method.

The approach gives a different ranking of celebrities for two campaigns based on group members' perceived importance of the selection criteria in different scenarios. This group includes decision-makers (DMs) from the brand, marketing communication agency and brand's customers. Further, each group member has an almost equal say in the decision-making based on fuzzy evaluation and an interactive and flexible value tradeoff approach to celebrity selection for receiving a rank order.

The approach uses secondary data on celebrities and hypothetical scenarios. Comparison with other methods is difficult, as no other study proposes a multi-criteria group decision-making approach to celebrity selection especially in a social media context.

This approach can help DMs make more informed, objective and effective decisions on celebrity selection for their brands or campaigns. It recognizes that there are multiple stakeholders, including the end customers, each of whose views is objectively considered in the aspects of group decision-making through a fuzzy evaluation method. Further, this study provides a selection mechanism for a given context of endorsement by objectively and interactively encapsulating stakeholder preferences.

This robust and holistic approach to celebrity selection can help DMs objectively make consensual decisions with partial or complete information. This quantitative approach contributes to the literature on selection mechanisms of influencers, celebrities, social media opinion leaders etc. by providing a methodological aid that encompasses aspects of interactive group decision-making for a given context. Moreover, this method is useful to DMs and stakeholders in understanding and incorporating the effect of nature or context of the brand and the campaign type in the selection of a celebrity or an influencer.

The paper focuses on bridging the existing literature gap on the role of leadership in influencing employee engagement considering the advancement in technologies. With this, the author explores how the three critical elements of service-based companies' business environment-artificial intelligence (AI) success, employee engagement, and leadership are interlinked and are valuable for raising the engagement level of employees.

A purposive sampling strategy was used to select the employees working in the respective companies. The survey was distributed to 150 senior management employees but responses were received from only 56 employees making the response rate 37.33%. Consequently, an empirical examination of these 56 senior management employees belonging to service-based companies based in Delhi NCR using a survey questionnaire was conducted.

The PLS-SEM (partial least squares structured equation modelling) revealed that AI has a positive role in affecting employee engagement levels and confirmed the mediation of leadership. The magnitude of the indirect effect was negative leading to a reduction in total effect magnitude; however, as the indirect effect model has a higher R square value, the inclusion of a mediating variable made the model more effective.

This study contributes to extending the existing knowledge of the academicians about the relationship theory of leadership, AI implementation in organizations, AI association with leadership and AI impact on employee engagement. The author extends the theoretical understanding by showing that more integration of AI-supported leadership could enable organizations to enhance employee experience and motivate them to be engaged. Despite its relevance, due to the limited sample size, focus on a specific geographic area (Delhi NCR) and the constraint of only using quantitative analysis, the findings open the scope for future research in the form of qualitative and longitudinal studies to identify AI-supported leadership roles.

The study findings are beneficial majorly for organizations to provide them with more in-depth information about the role of AI and leadership style in influencing employee engagement. The identified linkage enables the managers of the company to design more employee-tailored strategies for targeting their engagement level and enhancing the level of productivity of employees. Moreover, AI-supported leadership helps raise the productivity of employees by amplifying their intelligence without making technology a replacement for human resources and also reducing the turnover rate of employees due to the derivation of more satisfaction from existing jobs. Thus, given the economic benefit and societal benefits, the study is relevant.

The existing studies focused on the direct linkage between AI and employee engagement or including artificial intelligence as a mediating variable. The role of leadership is not evaluated. The leadership enables supporting the easy integration of AI in the organization; therefore, it has an important role in driving employee engagement. This study identifies the contribution of leadership in organizations by providing the means of enhancing employee satisfaction without hampering the social identity of the company due to the integration of AI.

The objective of this paper is to examine whether there are significant variations in the profile of debt‐financing as a result of differences in industry, size and age group of the sample corporate enterprises in India. It also ascertains the trend in debt financing practices among the various groups during the period 1992‐2003 and finds out the impact of the liberalized environment, in terms of the significant changes, if any, in phase‐2 (year 1998‐2003) of the liberalized business scenario vis‐à‐vis phase‐1 (year 1992‐1997) in the debt financing decisions of different group of the sample firms. The study covers a stratified representative sample of 601 corporate enterprises, consisting of fourteen industry groups, and is segregated into four size classes and two age categories. The study brings to fore that while industry and size have been observed to be a significant factor influencing composition and maturity structure of debt financing, the empirical evidence does not support the hypotheses that the debt financing decisions vary significantly across age categories.