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Nonperforming assets in any banking system have stressed the economic health of nations. Resultantly, literature has given considerable impetus to predict failures and bankruptcy. Past studies have focused on the outcome of failures, while, there is a dearth of studies focusing on ongoing firms in bad shape. We plug this gap and attempt to identify underlying communication patterns for firms witnessing prolonged underperformance. Using text mining, we extract and analyze semantic, linguistic, emotional, and sentiment-based features in non-numeric communication channels of these poor-performing firms and their peers. These uncovered patterns highlight the use of vocabulary and tone of communication, in correspondence to their financial well-being. Furthermore, using such patterns, we deploy various Machine Learning algorithms to identify loser firm(s) way ahead in time. We observe promising accuracy over a time window of five years. Such early warning signals can be of critical importance to various stakeholders of a firm. Exploration of writing style-related features for any firm would help its investors, lending agencies to assess the likelihood of future underperformance. Firm management can use them to take suitable precautionary measures and preempt the future possibility of distress. While investors and lenders can be benefitted from this incremental information to identify the likelihood of future failures.

This paper empirically examines the impact of corporate governance reforms on the financial performance of Indian state-owned enterprises (SOEs) for the period 2003–2011.

The findings indicate that the various corporate governance reforms collectively exhibited a statistically significant positive impact on performance when a difference in difference estimation process is used. However, the performance of SOEs is less than that of publicly listed companies, which is consistent with prior research. When the SOEs are compared with a matched pairing of publicly listed companies of similar size and same industry, their performance was comparable and in many instances superior. This is indicative of the regulatory constraints on competitors and preferential access to resources and markets given to the SOEs. As SOEs move towards a more mixed ownership model with more of them listed on the stock exchange and greater public ownership of shares the corporate governance issues will increase in importance.

The controlled sell down of shares in SOEs presents a need for continuing governance reforms and ongoing research to track progress.

The most striking observation from the study is that changes that were introduced as a corporate governance reform, such greater professionalism in boards, did not gain traction and enhance performance, rather the process of director selection and the concentrated bureaucratic and political interference stymied what was asserted to be conceptually sound reforms.

Purpose: This chapter aims to evaluate the impact of money laundering and terrorism financing on the Indian economy and to study the effectiveness of prevention of money laundering acts and terrorist financing as per the guidance of the financial protection task force.

Need for the study: Developing countries like India have been more vulnerable to terrorism and financial scams over the last four decades. Despite the establishment of regulating bodies and anti-money laundering acts, this problem continued to be a national threat. Therefore, examining the impact of money laundering and terrorism finance on the Indian economy is necessary.

Methodology: This study is based on secondary data gathered from the web portals of government agencies and international organisations dealing with money laundering and terror funding. Newspapers, journals, and annual reports are reviewed to identify the modus operandi of money laundering operators and their impact on the economy.

Findings: Money laundering and terrorism financing significantly threaten the Indian economy and national security. Despite different anti-money laundering laws and multiple regulating authorities, the system has pitfalls that allow economic fraud and money transactions for terrorist activities. There is a need for cyber security, and integrated enforcement agencies to combat money laundering at national and international levels.

Practical implications: This study would be helpful for academicians and policymakers to understand the nexus of money laundering and terrorism financing and its impacts on the Indian economy.

Introduction: China released an action plan in 2015 to foster the growth of big data, demonstrating that big data development has become an essential and inescapable choice for stabilising growth, advancing reform, adapting structures, helping people’s livelihoods, and supporting government modernisation. The rudimentary ecology of the big data sector, combined with a favourable policy climate, creates ideal conditions for developing big data in China. The use of big data, on the other hand, has steadily shifted from a theory to reality, thanks to the explosive increase of data resources and the rise of specialised big data firms. Globally, the insurance industry is undergoing a technological revolution. The internet, mobile networks, social networks, cloud computing, and big data are all examples of digital technologies that progressively influence daily business operations and will usher in a golden era for the insurance industry.

Purpose: This chapter aims to understand the use of big data in the insurance industry innovation and the challenges insurance companies face. This chapter will also offer insight into the big data strategies of insurance companies.

Methodology: This chapter attempts to study literature reviews related to big data and examines the use of big data in the insurance industry. Also, different techniques associated with collecting big data and an assortment of big data sources are analysed in the context of the developing insurance industry.

Findings: This chapter helps us understand how big data innovations are useful for the insurance industry. The present chapter helps us in understanding the challenges faced by insurance companies. This chapter will also offer insight into the big data strategies of insurance companies.

Background: Human resource management (HRM) is the tactical method for a business enterprise’s optimistic and systemic administration. This study aims to identify the common and major triggering attributes and the knowledge gap between HRM and an organisation’s employee attrition rate.

Method: The employee Attrition Case Study Dataset used is an anecdotal data set that tries to figure out relevant variables that determine employee behavioural aspects towards attrition. This study investigates why attrition occurs, the major triggering attributes for employee turnover, and how it might be anticipated to employ artificial intelligence (AI) to avert corporate losses.

Results: Employees’ monthly income, age, average monthly hours, distance from home, total working years, years at the company, per cent of salary hike, number of companies worked, stock options level, job role and other factors are taken into consideration. A feature importance extraction framework was devised to investigate the various dormant factors. The findings also show feasible hypotheses that help enhance employee engagement, reinvent the worker dynamic, and higher levels of risk decrease attrition rate.

Implications: Employees’ monthly income, age, average monthly hours, distance from home, etc., are all major variables in employee attrition in the Indian IT business. This research adds to the theory development of behavioural elements in people analytics based on AI.

Purpose: Can we predict employee attrition through employee behavioural patterns advancement using AI tools.