Money laundering is a multi-billion-dollar issue. Detection of laundering is very difficult. Banks and regulatory authorities struggle to identify these illegal transactions. Not only does it cost them billions in unpaid taxes, but it also promotes crime at the cost of socioeconomic health of a country.

There exist many automated algorithms which aim to detect illegal transactions but most of them have a high false positive rate: legitimate transactions are incorrectly flagged as laundering. The converse is also a major problem --false negatives, i.e. undetected laundering transactions. Naturally, criminals work hard to cover their tracks.

The aim of this module is to utilise IBM’s synthetic dataset of over 10 lakh financial transactions to create a deep learning model for fraud detection. The dataset, which is available on the Kaggle repository, is based on a virtual world inhabited by individuals, companies, and banks. Individuals interact with other individuals and companies. Likewise, companies interact with other companies and with individuals. These interactions can take many forms, e.g. purchase of consumer goods and services, purchase orders for industrial supplies, payment of salaries, repayment of loans, and more. These financial transactions are generally conducted via banks. Using a combination of supervised learning, deep learning, and GridSearchCV assisted models, this module will aim to achieve at least 90% accuracy in identifying illegal transactions.

Technological advancements, diffusion of technology among the population, competitive pressures, and evolving consumer behaviours and attitudes have disrupted the food retail business.  There has been a shift from the conventional business module that impacts all stakeholders in the industry. Some of the ill impacts of virtualization have been identified to comprise businesses facing losses leading to economic imbalances and job cuts. As Dubai is also going through a rapid transformation based on the adoption of technologies amidst the pandemic of COVID19, this study attempts to investigate food retailers ‘condition during and post the pandemic. It also aims to understand the extent of digital disruption in the retail industry and the problem with stakeholders in the transition to virtualization and to sustain the market.

The current study is significant in understanding the overall impact of digital transformation and organizations' perspectives on dealing with it. We will survey customers and supervisors of food retail stores across Dubai. We intend to find out the extent of digitalisation adopted by these stores and the impact it has had on customers' shopping experience. Based on the results, recommendations will be made to allow firms to navigate through digital disruption successfully and attain business success.

Contemporary businesses are evolving at a pace that has never been witnessed before, and one of the most critical changes taking place is diversity in the workplace. Organizational HR policies promoting inclusivity and diversity aim to engage a diverse workforce, opitimise their productivity and maximise their commitment towards the company. However,  organisations encounter several challenges in recruiting, retaining, and managing a diverse workforce. It thus becomes pertinent to identify strategies that have been implemented around the world in recent years to this effect.

Some countries like India have also introduced government policies to promote workplace diversity. In India several workplace laws protecting the rights of minority sections like women, LGBTQ+, persons with disabilities, and persons belonging to protected categories have been introduced in recent years. However, the problem is that medium and small-scale companies are unacquainted with such laws, while large-scale companies have reported difficulties in meeting the goals. This study aims to understand the recent HR policies introduced by India’s large and medium scale companies to adhere to the recent diversity laws and the challenges faced in this regard.

India has made commendable strides in enhancing its electrical distribution systems by reducing AT&C (Aggregate Technical and commercial) losses from 23.72% in 2016 to 17% in 2021. However, it is much higher than in countries like the USA, Japan, and South Korea. Technical factors that lead to AT&C losses include aging infrastructure, overloading of transformers and lines, and voltage fluctuations. On the other hand, non-technical factors include theft, inaccurate metering and billing, and lack of billing infrastructure. Its operational factors include inefficient collection processes, inadequate load management, unmetered and unbilled connections,  and voltage instabilities.

The primary objectives of this research are twofold: firstly, to comprehensively review existing methods for monitoring AT&C losses in distribution systems in India, and secondly, to implement a predictive model aimed at mitigating AT&C losses. This model aims to reduce AT&C losses due to theft and pilferage, inaccurate billing, operational inefficiencies, and inefficient collection processes. This model attempts to improve the ‘smart grid infrastructure’ which India has been actively working on for many years. Finally, this study proposes a method to integrate the predictive model into components of the power distribution system in India.