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The paper presents the pros and cons of crowdsourcing competitions and highlights the importance of strategy and collaborative efforts. The study identifies the key stakeholders of crowdsourcing and its critical elements (7Ps) that need to be mapped and managed efficiently for obtaining innovative solutions.

The paper draws its insights from the explorative research conducted over a two-year period (2016-18). Qualitative interviews held with competition organizers, participants and innovation intermediaries (Innocentive, Nine Sigma, Skild) provided the primary data. Secondary data came from literature survey and the study of archival documents and competition websites. The study was conducted as a part of doctoral research.

Crowdsourcing competitions can help organizations to discover innovative solutions by tapping the power of collective intelligence. However, they need to envision and execute these collaborative initiatives strategically and synergistically. Proper design, managerial buy-in and orchestrated efforts by the triumvirate ‘Seekers, solvers and supporters’ are critical to derive the desired outcomes.

These findings are the resultant outcomes of an exploratory research. Further investigation can help companies to identify the relative importance of the critical elements identified in the study. Future research on the best practices can amplify the prospects of finding innovative solutions through crowdsourcing competitions.

Crowdsourcing competitions cannot be used impulsively and indiscriminately. Managers have to carefully align the motive and incentive of different actors. Attention to design and the critical factors identified in the study can enhance the prospects of getting qualitative and innovative submissions from the crowd.

Crowdsourcing competitions have great potential to find innovative solutions for many stubborn global problems. When designed and driven rightly, it can expand the solution mix and accelerate the discovery process.

The paper highlights the need to converge the efforts for diverse stakeholder in crowdsourcing competitions. There are actionable insights for managers in form of 7 Ps- Purpose, Problem, Prize, Platform, Promotion and Partners. When aligned effectively, it can yield innovation dividends to all. By focusing on these vital factors, companies can fine-tune their crowdsourcing strategy and make the initiative more engaging and create value for all the actors.

Purpose: Worldwide economies have been shattered by the alarming increase in Non-Performing Assets (NPAs) in Banking Sector. In India, the rise in NPA levels gives a clear insight into the health of industry and state. This study aims to determine how NPAs in India impact the profitability of eight banks chosen from the public and private sectors; specifically: Punjab National Bank (PNB), Bank of India (BOI), UCO Bank, Punjab and Sind Bank (PSB), HDFC Bank, Axis Bank, ICICI Bank, and Yes Bank; during the period 2009/2010 to 2017/2018.

Design/methodology/approach: The study utilised IBM SPSS version 20 application to carry out our statistical analysis of measures of central location (mean and median), measures of dispersion (standard deviation), to carry out the Kolmogorov–Smirnov test to check the normality of data, the Mann–Whitney U test (for two groups) for median comparison between private and public sector banks and the Kruskal–Wallis test (for more than two groups) for median comparison for more than two banks. p ≤0.01 and p ≤0.05 were the two-tailed significance level used for determining the significance of all statistical tests.

Findings: Trend analysis and statistical tests show that the trend in public sector banks to have NPAs is higher compared to private sector banks, and losses arising from NPA impact the banks’ profitability.

Practical implications: It is apparent that NPAs are a large threat to banks in India as it reflects the state of the Indian economy. The growth of the economic cycle is predominantly dependent on the smooth and profitable functioning of private and public sector banks. This current study focusses on and compares the impact of NPAs on the profitability of public and private sector banks. NPAs have grown exponentially more in the case of public sector banks than private sector banks, which has affected the former banks’ financial health and performance. Increases in the level of NPAs adversely affect the working style and long-term stability of public and private sector banks in the economy.

Social Implications: NPAs have a negative influence on the profitability of the banks as well as on the economic growth of the country too. However, it is recommended that management in the banking sector, particularly the public banks, should use various preventive and recovery strategies to reduce the risk of failure and to keep track of NPAs to stay safe.

Originality/value: This study aims to determine how NPAs in India impact the profitability of eight banks chosen from the public and private sectors; specifically: PNB, BOI, UCO Bank, PSB, HDFC Bank, Axis Bank, ICICI Bank, and Yes Bank; during the period 2009/2010 to 2017/2018.

Purpose: The purpose of the study is to investigate how blockchain, a new emergent technology, has spread throughout the insurance industry by evaluating incidents reported in the literature, either academic or trade publications. This study specifically aims to develop a comprehensive understanding of the main effects of blockchain on the insurance sector’s core operations.

Design/Methodology/Approach: Review based approach has been utilised by evaluating incidents reported in the literature, either academic or trade publications.

Findings: Blockchain is a technology attracting more and more interest from academics and business professionals. It can be seen that the technological advancements and benefits of blockchain in the insurance industry can help it expand manifold. The wise use of technology can only provide the same. If applied without thought, it can be disastrous too. With the future integration of AI and other machine learning technologies, the authors can see that clerical work in the industry can completely shift to computers and humans can use more of their time in more developmental activities and results.

Practical Implications: The sector is moving in the direction of digital transformation employing emerging technologies and is ready for disruptions and change shortly regarding processes, industry structure, rivalry, and organisational structure.

Research Limitations: Future researchers could investigate the existing technique to examine the dissemination of upcoming technologies, such as cognitive computing, blockchain, and artificial intelligence, in certain industries or organisational functions.

Purpose: The goal of this study is to delve into the causes behind the Fintech sector’s rise in various areas and its prospects. Fintech is rapidly expanding because of government legislation, multiple schemes, consumer expectations, a cashless economy, digitisation, globalisation, innovation, and other drivers.

Need for the Study: Fintech firms are forming alliances with traditional financial organisations to stay afloat and compete. India is becoming a superpower regarding e-startups, especially unicorns. Many startups are undergoing initial public offerings (IPOs). Fintech is an emerging space in India, spreading its wings rapidly in every sector.

Methodology: This work is based on a literature review. It utilises secondary data from numerous research publications, magazines, newspapers, published reports, relevant websites, Forbes magazine articles, stories from The Economic Times, the RBI Portal, and information from StartupIndia, Assocham, and Pwc, among others, to develop a conceptual framework showing the growth drivers of Fintech.

Findings: The whole world has been affected severely due to COVID-19. Crisis always comes with some opportunity, and it is up to us how to turn the calamities into opportunities that further turn into innovation that has the power to lead the world. Fintech is that fruit that had been born normally but grew abnormally (tremendous growth) during the pandemic. Also, the roots are so deeper that they will flourish more and more. It has been found that the emergence of a cashless economy, ease of internet connectivity, etc., are the major factors that paved the way for growth for Fintech in India.

Practical Implications: This study contains the conceptual framework which can guide the stakeholders, policymakers, management teams, field experts, etc., in knowing about their area expertise and looking for improvement, if any.

Originality: There are many papers on the relationship between Fintech and financial inclusion, but this is the first study that builds the conceptual framework for the growth drivers of Fintech.

Purpose: FinTech is exploding all over the world. FinTech companies play a critical role in growing the banking industry. This chapter reviews existing literature on FinTech in banking, particularly its publication trend, journal productivity and impact, affiliated organisations and related themes.

Need for the Study: FinTech is reshaping the banking sector as banks move towards digitisation. FinTech has eliminated the need for paper, reduced the requirement for physical presence, and destroyed the necessity for cash. Several researchers have studied the features and benefits of FinTech technologies in the banking field. So, there is a need to analyse the available literature to identify the scope of further research in this field.

Methodology: For a comprehensive review, Bibliometric and content analysis of 77 open access green articles collected through the structured database of ‘Dimensions’ is done. These articles are published in the UGC Journal List Group II.

Findings: It is revealed that the execution of FinTech is continuously increasing in the banking sector, which has resulted in automation in various banking activities. The study revolves around technology and Banking, Financial Inclusion and Growth, and the Impact of the Financial Crisis on Banking and Peer-to-Peer (P2P) lending.

Practical Implications: The conclusions of this study can help academia and industry improve their understanding of FinTech in Banking, specifically its publication trend, geographical distribution, and creation of coherent themes. Careful analysis of collected articles will help to explore the scope of further research.

Introduction: New ideas and concepts of big data have emerged in recent years in response to the astounding growth of data in many industries. Furthermore, the phenomenal increase in the use of the internet and social media has added enormous amounts of data to conventional data processing systems. Still, it has also created challenges for traditional data processing.

Purpose: A significant characteristic of the insurance sector is critically dependent on information. This sector generates a great deal of structured and unstructured data, which traditional data processing techniques cannot handle. As compared to conventional insurance data processing and decision-making requirements, this lesson shows an analysis of data technology’s value additions.

Research methodology: The author assesses the primary use of cases for data in the insurance industry via a case study analysis. From the perspective of the insurance sector, this chapter examines the concepts, technologies, and tools of big data. A few analytical reviews by the insurance company are also provided, which justified several gains gained either through inefficient processing of massive, diverse data sets or by supporting better decisions.

Findings: This chapter demonstrates the importance of adopting new business models that allow insurers to move beyond understand and protect and become more predictive and preventative by using the tools and technologies of big data technology.

Introduction: Foreign direct investment (FDI) is a deciding factor in the insurance industry’s growth in any nation. Besides, similar socioeconomic conditions, some countries tend to attract more FDI inflows. This chapter focuses on exploring the FDI in the insurance industry in Brazil, Russia, India, China, and South Africa (BRICS).

Purpose: The chapter aims to explore the current situation of FDI in the insurance industry in BRICS member nations and uncover the factors that have led to higher foreign investments in some countries.

Methodology: Using descriptive and comparative approaches, this chapter explains the FDI scenario in the insurance sector of BRICS nations.

Findings: Based on a comparative analysis, the authors observed that deregulation, increased foreign engagement, and adoption of innovative technology and distribution methods are some avenues that could be worked upon to improve FDIs in the Indian insurance sector.

Purpose: Tax fraudulence is not an aberration, but it is a deliberate attempt to get rid of the tax burden. The government took enormous measures to curtail tax fraud, which seems dispensable. Taxation is the largest area that needs to be explored as individuals make discrete stings while filing taxes. Since compliance-focussed systems for tax collection are missing, this department is facing difficulties in providing quality data. This chapter will cover blockchain operations in the tax system to streamline the entire process and highlight the challenges.

Need of the Study: As the growth of technology is in the boom stage, research should be carried out to create more awareness regarding its usage and its possible threats. This study will spark more light on taxation by taking blockchain as the torch to improvise the vision.

Design/methodology/approach: The researcher did a comprehensive analysis of the Indian Tax Department and determined whether simplifying taxation could be possible through blockchain. The study gathered concrete facts about the various challenges faced while implementing this system.

Finding: Blockchain technology is rapidly making a roadmap in the taxation system, and there is tremendous potential to create a future vision with blockchain.

Practical Implications: This study will accentuate the function of blockchain technology that is becoming more prominent in the global technology sphere. This study will aid scholars in elaborating on the role of blockchain technology.

Purpose: This chapter aims to examine machine learning (ML) models for predicting credit card fraud (CCF).

Need for the study: With the advance of technology, the world is increasingly relying on credit cards rather than cash in daily life. This creates a slew of new opportunities for fraudulent individuals to abuse these cards. As of December 2020, global card losses reached $28.65billion, up 2.9% from $27.85 billion in 2018, according to the Nilson 2019 research. To safeguard the safety of credit card users, the credit card issuer should include a service that protects customers from potential risks. CCF has become a severe threat as internet buying has grown. To this goal, various studies in the field of automatic and real-time fraud detection are required. Due to their advantageous properties, the most recent ones employ a variety of ML algorithms and techniques to construct a well-fitting model to detect fraudulent transactions. When it comes to recognising credit card risk is huge and high-dimensional data, feature selection (FS) is critical for improving classification accuracy and fraud detection.

Methodology/design/approach: The objectives of this chapter are to construct a new model for credit card fraud detection (CCFD) based on principal component analysis (PCA) for FS and using supervised ML techniques such as K-nearest neighbour (KNN), ridge classifier, gradient boosting, quadratic discriminant analysis, AdaBoost, and random forest for classification of fraudulent and legitimate transactions. When compared to earlier experiments, the suggested approach demonstrates a high capacity for detecting fraudulent transactions. To be more precise, our model’s resilience is constructed by integrating the power of PCA for determining the most useful predictive features. The experimental analysis was performed on German credit card and Taiwan credit card data sets.

Findings: The experimental findings revealed that the KNN achieved an accuracy of 96.29%, recall of 100%, and precision of 96.29%, which is the best performing model on the German data set. While the ridge classifier was the best performing model on Taiwan Credit data with an accuracy of 81.75%, recall of 34.89, and precision of 66.61%.

Practical implications: The poor performance of the models on the Taiwan data revealed that it is an imbalanced credit card data set. The comparison of our proposed models with state-of-the-art credit card ML models showed that our results were competitive.

Introduction: The frequency and complexity of cyber assaults have grown in recent years. Consequently, organisations have increased their expenditures in more robust infrastructure to protect themselves from these cyber assaults. These organisations’ assets, data, and reputations are at risk due to rapidly increasing cybercrimes. However, complete protection from these many and ever-changing threats is very challenging as a result. To deal with them, companies are taking steps to reduce risks and limit company losses in their occurrence.

Purpose: Progressively, the insurance sector organisations are including digital protection as a component of the board’s general danger technique. Protection enterprises, then again, depend on accurately expecting risks, while a significant number of them depend on normalised approaches. Because of the exceptional attributes of the digital assaults, transporters now and again depend on subjective strategies dependent on master decisions. There is an unmistakeable absence of observational information on digital protection, specifically subjective examinations planning to comprehend and depict necessities, impediments, and cycles applicable for digital protection.

Methodology: There are various unanswered inquiries and worries about the oversight and legitimate and administrative assessment of network safety weaknesses in the protection business. In the wake-up of looking over all these worries and issues, steps to alleviate them are laid out after an extensive literature survey and secondary data sources. In this study, the authors have principally viewed the executive parts of the associations as the danger. While considering network protection, their insight of needs was taken as one among a few dangerous treatment systems, just as the necessities of the organisations’ protection in assessing the danger level of likely customers.

Findings: This section analyses past research in network safety and information security in the protection market. The danger of the executives’ strategies, the numerical models, and the forecasts of digital occassions are illustrated in this section. Lastly, the future headings are likewise expressed momentarily.

Practical implications: This review might be valuable for additional examination and logical discussion, yet additionally for down-to-earth applications. Moreover, it could be gainful to organisations as a supportive instrument for better agreement on what digital protection is and how to get ready to take on network safety and information security procedures in the association.

Significance: These associations’ resources, information, and notoriety are in danger because of quickly expanding cybercrimes. Cybercriminals are utilising more refined approaches to start digital assaults. Digital protection was anticipated to affect security conduct before any proof was gathered. Progressively, organisations are including digital protection as a feature of their general danger to the executive system. Because of the exceptional attributes of the digital assaults, transporters as often as possible depend on subjective methods dependent on master decisions. Thus, this space of network safety and information security is vital uniquely in the protection market.