INTRODUCTION: UNDERSTANDING THE THREAT OF WHITE-COLLAR CRIME
Edwin Hardin Sutherland described white-collar crime as offences carried out by individuals of high social standing, notable reputation, and respectability within their professions. The rise in white-collar crimes has become a global concern in recent times. For instance, Hong Kong recorded a 45% surge in these offences in 2022 compared to the previous year. Similarly, India saw a distressing 80% increase in white-collar crimes in 2019.
Kroll’s 2023 Fraud and Financial Crime Report reveals an alarming trend: 68% of respondents anticipate a rise in financial crimes over the next year, with advancing technology identified as a pivotal challenge.
Corporations can be held liable if they fail to prevent economic crimes. Additionally, corporations can be charged criminally if they fail to establish a proper legal framework against financial crimes. Such a legal framework underscores the crucial role of AI in mitigating misconduct and shielding companies from liability risks.
To keep pace with the ever-evolving criminal tactics, we require advanced technology in the dynamic world of financial crime. Artificial Intelligence (AI) is an imperative tool for hindering them. This blog will further explore how AI is remoulding financial crime detection by highlighting its exceptional momentum, precision, and capacity to analyze massive datasets.
THE PROGRESSION OF AI IN DETECTING FRAUD
The journey of fraud detection has witnessed a passionate transformation over the years, adapting from labour-intensive manual methods to AI-powered systems. With financial crimes growing alarmingly, the demand for real-time innovations became cardinal.
- From Manual Efforts to Automated Precision
Fraud detection has always counted on the work-intensive manual labourers. Manual labour is undoubtedly leisurely, which is also susceptible to error. It’s hard for traditional methods to keep up with the rise in financial crimes. To mitigate the situation, AI-powered systems surged to keep track of a vast database with efficiency, pace and reliability for the detection procedures.
- The Power of AI in Fraud Detection
Artificial intelligence empowers systems to evaluate monumental databases, detect irregularities and locate fraudulent patterns. Two primary AI techniques are instrumental in this area:
- Supervised Learning: AI models are trained to set apart lawful transactions from defrauding ones. The model can speculate and locate fraudulent activities if properly trained.
- Unsupervised Learning: Dissimilar to supervised learning, unsupervised learning recognizes inconsistencies by inspecting similar sequences, thus helping to detect disguised threats.
EFFECTIVE WAYS AI IS COMBATING FINANCIAL CRIME
- Sophisticated Data Analysis and Anomaly Detection
Artificial intelligence processes vast datasets at an incredible pace, making it a vital weapon for financial institutions. Analyzing huge volumes of data, AI spots patterns and frameworks suggesting fraudulent schemes, thus allowing organizations to detect suspicious or illicit transactions that would be challenging to detect manually.
- Real-Time Threat Detection
AI-powered systems continuously monitor financial transactions, leading to immediate spotting of suspicious behaviour. When abrupt changes in transaction amounts or destinations appear, the system can spontaneously flag these for detection. This real-time response assists in flagging threats rapidly, mitigating risks before they surge.
- Behavioral Insights and Risk Profiling
AI fabricates encompassing profiles for customers based on their historical transaction data and behaviours so it can detect any distortions from normal patterns, such as unexpected high-value transactions or activity in high-risk locations. This proactive approach upgrades an institution’s strength to locate and prevent fraud, money laundering, and other economic crimes.
- Textual Data Analysis with NLP
Natural Language Processing (NLP) is reshaping ways by which institutions observe communication data. NLP algorithms can spot language structures linked to economic crime, by scanning emails, messages, and chat logs. Such inspections expose secret networks, forbidden activities, and unauthorized interactions, giving the surveyor necessary leads.
- Predictive Analytics Through Machine Learning
Machine learning algorithms discover past events of financial crime, leading them to predict impending threats. These models continuously adapt and recognize reshaped tactics used by criminals. This vigilance enhances financial institutions to foresee risks, implement precautionary measures, and reinforce their defences against impending threats.
- Enhanced Cybersecurity
AI-powered systems can locate and respond to cyber threats like phishing attacks, malware, and data breaches, safeguarding sensitive and private information.
ETHICAL CONSIDERATIONS AND LEGAL FRAMEWORK
The use of artificial intelligence raises various ethical concerns like bias, privacy, transparency, etc.
Bias in AI algorithms from unsteady training datasets leads to inaccuracies in risk evaluations or unjustly singles out individuals.
Under the OECD AI principles, those involved in unfolding AI systems are presumed to be devoted to transparency by sharing pertinent information about these systems.
Another concern is about data privacy, where AI systems may breach the privacy of individuals as the datasets are heavily encompassed by personal data. There’s also the concern of unsupervised use of personal data. So, it’s extremely important to safeguard the privacy of individuals through authorized data protection mechanisms.
India currently has no such committed legal framework for the use of AI in law enforcement, but data protection laws like the Information Technology Act of 2000 regulate digital crimes and data protection, which in turn looks into data privacy (right to privacy being a fundamental right) during fraud detection. Current legislations like data protection regulations (e.g., General Data Protection Regulation and the California Consumer Privacy Act) and cybersecurity frameworks (e.g., NIST Cybersecurity Framework) provide a foundation for algorithmic accountability, liability and cybersecurity to prevent prejudiced aftermath and harmful consequences.
CONCLUSION
To conclude, With the rapid advancement of technology, combating financial crime has entered a new era. Traditional methods of detecting and preventing fraud and illicit activities have proven incompetent against the growing sophistication of modern criminals. AI is a potential weapon in a cathartic journey to thwart white-collar crimes. Its capability to speed up convoluted procedures and identify potential risks lends an innovative approach to detecting economic fraud.
However, the use of AI comes with some ethical concerns regarding bias, data privacy and transparency in legal enforcement. AI systems should be committed to protecting the privacy of individuals and creating a balance between the correct implementation of AI and attenuating economic fraud. It is not a silver bullet but a significant factor to keep up with the high-tech fraudsters.
Author(s) Name: Meghnila Chakraborty (Heritage Law College, Calcutta University)
