Owing to rapid technological development, financial crime risks have forced reporting entities, including financial institutions, and virtual asset service providers (VASPs) to amend their counter-fraud, anti-money laundering, and counter-terrorism financing (AML/CTF) programs through the implementation of several alternative processes including detection, scoring, alerting, workflow processing, and suspicious activity reporting. Reporting entities which have traditionally relied on programmed automated monitoring systems now use AML automation which combines AI, machine learning, and robotic process automation (RPA). This approach includes out-of-the-box rules-based models exhibiting intelligent segmentation and automated simulation, alongside profiling analytics to identify known risk scenarios. Coupling these technologies with novel predictive and anomalous analytics has facilitated the discovery of previously unknown money laundering and other financial crime risks.

In order for AI to be effective, it must be trained with sufficiently extensive, reliable, and accurate data. However, institutions can encounter scenarios where the data is insufficient, unreliable, and/or inaccurate. Several other challenges may develop based on a company's internal control, geographical location or customer base which may be further complicated by data protection laws and privacy laws.

This Research Topic will identify the challenges to data mining, data protection, data security and data privacy, legal and ethical aspects of robotic technologies and Artificial Intelligence which are unique to advanced economies and emerging markets, and proffer solutions and recommendations which can effectively address these challenges. Research themes include:

- Artificial Intelligence and data privacy: challenges in finding the right balance
- AI and data mining problems
- Difficulty in quantifying and mitigating privacy risks of AI with personal data
- Difficulty in measuring and managing security risks of AI with personal data
- Corruption and its impact on the effectiveness of AI-enabled systems
- Challenges with accountability and trust in data privacy management of AI data
- Challenges in implementing anti-money laundering policies, procedures, and processes that are
adequate enough to support AI
- Insider fraud and its impact on the effectiveness of AI-enabled systems
- Culture of anti-money laundering compliance and its impact on the effectiveness of AI-enabled
systems
- The challenges in mapping out legal requirements to AI/IT controls
- The challenges in complying with data protection laws for AI
- The challenges of reforming intellectual property protection for AI-enabled software
- The challenges of reforming data protection for AI-enabled software
- The limitations of legal institutions for addressing cybercrime risks
- Economic implications of AI
- The challenges in tailoring legal protection for AI-enabled software
- Access barriers to big data
- Difficulty in designing a framework for robotics and AI regulation
- Legal, ethical, and societal implications of robotics and AI research and applications
- Barriers to conducting crime and criminal investigation in the clouds using AI
- Challenges in regulating robot and AI behavior by design
- Challenges in regulating new technologies powered by AI in times of change
- Constitutional rights and new technologies powered by AI
- Barriers to technology and AI regulations
- Insider attacks in cloud computing
- Difficulty in framing adequate guidelines for addressing legal and ethical issues of robotics and AI
- Difficulty in designing and implementing privacy regulations in the metaverse for data used to train
the AI Algorithm
- Ethical assessments of emerging technologies and AI
- Barriers to implementing guidelines on regulating robotics and AI
- Ethical, legal, and social issues of robotic technologies and AI

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Frontiers in the Internet of Things

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