Finance and Accounting
AI in financial compliance: Navigating regulatory challenges
Financial regulatory compliance is essential for maintaining the integrity and transparency of financial institutions. With AI increasingly integrating into financial services, understanding its role in regulatory adherence is more critical than ever.
While AI offers greater efficiency, it also presents challenges in keeping up with evolving regulations. Despite the industry’s concerns, nearly 70% of companies believe AI will significantly impact risk and compliance, transforming areas like anti-money laundering (AML), fraud detection, and data privacy management.
At Infosys BPM, we help financial institutions navigate complex regulatory environments. Our expertise in digital transformation, combined with AI-driven finance solutions, empowers finance leaders to optimise processes, mitigate risks, and drive business value.
This article delves into the transformative potential of ALM, fraud detection, and broader finance applications while also addressing the unique regulatory challenges that accompany its implementation.
Key regulatory bodies and frameworks impacting AI in finance
Several regulatory bodies globally oversee financial institutions, ensuring they meet compliance standards while incorporating emerging technologies like AI.
• Securities and Exchange Commission (SEC): Regulates U.S. securities markets, focusing on protecting investors through disclosure requirements and market integrity.
• Financial Industry Regulatory Authority (FINRA): Oversees brokerage firms and ensures fairness in trading practices, including AI-driven platforms.
• Basel Committee on Banking Supervision (BCBS): Establishes international banking regulations, such as Basel III, focusing on capital adequacy and risk management.
Alongside these bodies, other frameworks, including the Dodd-Frank Act and GDPR, influence how AI can be applied in financial services.
- Intelligent data handling: The ability to ingest and process all types of data classes and categories — structured/unstructured, upstream/downstream, internal/external, historical/inflight data.
- Contextualised insights: The ability to mimic human decision-making and actions by leveraging LLMs, Machine Learning, and Neural Networks.
Navigating the regulatory maze with AI
AI is emerging as a powerful tool to help organisations navigate the regulatory maze. The balance between innovation and oversight remains crucial in this dynamic environment.
AI in anti-money laundering: Transforming compliance
Money laundering remains a significant threat, with regulators increasing scrutiny to prevent illicit activities. AI is transforming AML by leveraging machine learning (ML) and Natural Language Processing (NLP) to detect suspicious transactions and patterns in real time. AI-powered systems can spot anomalies like unusual transaction volumes or suspicious transfers, which traditional systems may miss. By continuously learning new tactics, AI improves detection, offering proactive alerts to minimise compliance breaches and financial penalties, ensuring financial integrity.
The role of AI in fraud detection: Proactive protection
Fraud detection is critical in the digital era as financial institutions face complex fraud schemes. AI models, trained on historical data, detect unusual behaviour across transactions, like rapid changes in frequency or unauthorised access. Machine learning identifies fraud patterns, while other AI models process vast data to reduce false positives, improving efficiency. Continuously updated, these systems adapt to evolving fraud tactics, helping financial institutions stay ahead of criminals and effectively manage high-risk cases.
AI in finance: Streamlining regulatory compliance
The global financial regulatory landscape is complex, with bodies like the Financial Action Task Force (FATF) and Markets in Financial Instruments Directive 2014 (MiFID II) enforcing strict requirements. As institutions adopt new technologies, AI streamlines compliance by ensuring adherence to local and global regulations.
AI tools analyse vast datasets, flagging discrepancies and non-compliance in real time, reducing the risk of fines. They also enhance transparency in financial operations, building trust with regulators and customers through timely, accurate reporting.
Balancing innovation and oversight
As AI transforms financial compliance, balancing innovation with accountability is crucial. AI should augment, not replace, human judgment, with compliance professionals interpreting insights and making informed decisions. To manage risk while fostering innovation, regulators are creating regulatory sandboxes. These controlled environments allow financial institutions to test new AI solutions without facing immediate penalties for non-compliance, enabling experimentation within a safe, supervised space.
The challenge of bias and fairness in AI
While AI boosts efficiency, fairness is vital, especially in credit scoring and loan approvals. AI models may unintentionally perpetuate biases from historical data, leading to discriminatory outcomes. To address this, AI systems must be designed to eliminate bias and comply with regulations like the Equal Credit Opportunity Act (ECOA). Financial institutions must audit AI models regularly to ensure fairness, protect against legal liabilities and ensure equitable outcomes for all customers.
Transparency and explainability: A key regulatory requirement
A key challenge in AI for financial compliance is ensuring transparency and explainability. Regulatory bodies require institutions to clarify how AI models make decisions, especially in areas like credit scoring or fraud detection. This is difficult with complex models like deep learning, which are often “black boxes.” Financial institutions must balance using sophisticated AI systems with meeting regulatory demands for clear, understandable decision-making, ensuring compliance and maintaining trust with customers.
AI agents: A rapidly evolving innovation
AI agents are revolutionising financial compliance by automating complex processes such as AML monitoring, fraud detection, and regulatory reporting. These autonomous systems analyse vast datasets in real time. They can identify risks and ensure adherence to evolving regulations. By reducing manual intervention, AI agents enhance efficiency, minimise compliance risks, and allow financial professionals to focus on high-value tasks, ensuring agility in an increasingly complex regulatory environment.
