Search results

The London Interbank Offered Rate (LIBOR) is considered to be the most important interest rate in finance upon which trillions in financial contracts are decided. In 2008, it was revealed that the LIBOR traders were rigging the interest rates. Yet, there is an unresolved question that regulators and banking officials did not address in their quest to seek answers to the fraud: Were the banks under financial strain when they underreported their LIBOR rates? To answer this question, the article posits that the pressure to meet market expectations led the banks to experience financial strain. Data were gathered from 2004 to 2008 on the banks that were involved in the fraud (fraud banks) and matched with a control group of non-fraud banks. The results from a logistic regression model found sufficient statistical evidence to support the claim that fraud will be greater in banks characterized by a higher level of organizational complexity. Variables such as percent of outside directors, board members on the audit committee, and number of employees were all found to be statistically significant. These variables may offer key insights into detecting and preventing frauds in banks.

Over the last decade, customer experience management has gradually emerged as the most important activity for organisations. Organisations have turned towards leveraging the ubiquitous and easy-to-use technology in enhancing and enabling experience for the time-crunched customers of today who are looking for greater convenience and choices. It is therefore not surprising that disruptive technologies such as smartphones, virtual and augmented reality, cloud computing, big data analytics, Internet of things, artificial intelligence and robotics have also found their way into the design of customer experience. This chapter aims to present an overview of the technologies that have transformed the customer experience landscape. This chapter contributes by showcasing two illustrative cases from very diverse domains, a private sector bank and a public sector transportation organisation, to elucidate how India, a rapidly developing economy, is embracing technology to enhance the customer experience.

Financial crimes involve several offenses without violence with some people obtaining financial benefit and causing financial loss to some others. The globalization of financial systems, the growing volume of trading transactions, and the acceleration of information technologies have brought many conveniences to the financial world; but unfortunately, financial crime has spread and diversified. Therefore, the fight against financial crimes, which are often complex and organized in a way which is nonviolent but causes significant financial damage to people and organizations, is gaining importance. In this sense, the struggle against this type of crime, which has become a serious threat, must be resolved by applying a comprehensive policy that should include all segments of the society.

In this chapter, we aim to give a general framework of financial crimes and carry out a literature review on the subject. Moreover, we outline the different types of financial crime (such as money laundering, insider dealing, fraud, market abuse, bribery, corruption, terrorist financing, white collar crimes, tax evasion, embezzlement, forgery, counterfeiting, identity theft, etc.) and their impact. As a result, this study has the purpose of providing awareness by drawing attention to the concept of financial crime, which is an important threat nowadays that an ordinary person may suffer at any time in daily life.

Purpose: To analyse the insurance market breakthroughs through ‘Big Data’ and the possibility of new techniques of services provided, creating access for information gathering and fraud detection. This can contribute to improved risk management processes and mitigation strategies referred to as ‘InsurTech’.

Methodology: We catalogue the technique which is especially useful and being evaluated as having the ability to bring innovations to the insurance business. In doing this, we reveal which marketplaces actively participate in start-ups and how insurers engage in them and present them, highlighting the impact of blockchain technology, ride services, robo-advice, and data analysis on the insurance industry.

Findings: Findings show that because emerging economies have fewer organisation needs to ensure the distribution model, technology and research may significantly influence such areas. Nonetheless, whether industrialised or emergent, relevant legislative inspections should be carried out to protect subscribers’ welfare.

Practical implication: Since ‘Big Data’ impacts insurers’ constant monitoring of business risks and corporate governance, an overview of how information is harnessed should be carefully studied. Moreover, it is essential to study the handling of algorithms to guarantee that the expectations are reasonable and that unforeseen effects are avoided to the greatest extent feasible, and regulators have a mechanism for engaging in this review.

Purpose — The purpose of this chapter is to study the pattern of rogue trading, paying special attention to the aspects of the dishonest behavior of perpetrators.Design/methodology/approach — The chapter discusses selected cases of rogue trading that received the largest coverage by the mass media.Findings — No unique pattern of rogue trading schemes can be identified; however, certain similarities can be brought up based on the discussed cases. There are many aspects of dishonesty involved in fraudulent trading besides illicitness of unauthorized trading as such.Research limitations/implications — The chapter is based largely on a literature review and available data on the instances of rogue trading; probably, there is a vast amount of rogue trading cases undisclosed in order to draw a bigger picture.Originality/value — We apply the framework of white-collar crime process by McKay, Stevens, and Fratzl (2010) in order to clarify whether rogue trading schemes match the development of a typical white-collar crime. Conclusions are built on the analysis of several cases.