Internal auditing in banks: Balancing code and compliance

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  Quantumrun Foresight
• December 20, 2024

Insight summary

Internal bank auditing is changing rapidly as financial institutions face new technologies, stricter regulations, and evolving risks. With the rise of artificial intelligence (AI) and real-time monitoring, banks can better detect fraud and improve data governance, but this shift may also lead to reduced job opportunities in traditional audit roles. Governments and companies would need to balance the benefits of automation with concerns about data privacy and cybersecurity.

Internal auditing in banks context

Internal bank auditing has undergone significant transformations in recent years, as banks face increasingly complex regulations and increased scrutiny. Internal auditing, traditionally aimed at assessing a bank's risk management, compliance, and governance structures, now plays a broader role in navigating technological advancements, economic pressures, and evolving customer demands. For example, new credit risk concerns, fueled by higher borrowing costs and economic slowdowns since 2023, have prompted auditors to revisit credit risk fundamentals to better safeguard financial stability. In addition, the rising importance of data integrity and operational efficiency has placed a premium on audits that can identify potential weaknesses before they escalate into larger issues.

In practice, internal auditing functions as the third line of defense in banking, providing independent evaluations of internal processes and controls to ensure that all activities comply with regulations and industry standards. Internal auditors work closely with senior management to identify potential risks, evaluate the effectiveness of current systems, and recommend necessary changes. A key area of focus has been fraud detection and prevention, especially as financial crimes like money laundering and embezzlement become more sophisticated. For example, HSBC’s internal audit team identified money-laundering networks in South Africa involving USD $4.2 billion between 2014 and 2017, demonstrating the critical role audits play in mitigating financial crimes.

Internal bank auditing continues to evolve with the integration of advanced technologies and regulatory frameworks. For example, AI and algorithm-driven oversight systems are changing how audits are conducted, improving accuracy and enabling real-time monitoring. In addition, regulators such as the European Central Bank have emphasized the need for stronger data governance and internal controls, pushing banks to adopt more comprehensive audit cycles that account for emerging risks like cybersecurity threats and environmental concerns.

Disruptive impact

As banks adopt more advanced auditing technologies, individuals will experience more reliable protection against fraud and financial crimes. For example, AI-powered audits can flag suspicious transactions in real time, which could prevent identity theft or unauthorized withdrawals. However, increased automation may lead to job shifts, as fewer staff may be required to handle manual audit tasks, which could result in fewer opportunities in traditional banking roles. In addition, individuals may need to navigate more complex digital banking systems, which could present learning curves for less tech-savvy users.

Companies may need to allocate more resources to compliance and data governance to keep pace with evolving regulatory demands. For example, banks using algorithm-driven audits could find it easier to comply with international data protection laws, but the initial investment in such technology could be costly. Additionally, businesses that fail to adapt may face higher penalties for non-compliance, which could erode trust with clients and lead to lost revenue. On the positive side, companies that integrate these advanced systems effectively may reduce operational costs by streamlining compliance processes and improving overall efficiency.

Meanwhile, governments may be able to implement stricter regulatory frameworks with greater ease, as technology allows for more accurate and transparent auditing practices. This development could improve efforts to combat money laundering and financial fraud across borders. However, increased reliance on automation in auditing could raise concerns about data privacy and the potential for technology misuse, which could prompt governments to draft new legislation on ethical AI use and cybersecurity. Furthermore, governments may need to invest in cross-border partnerships to address global financial crime, especially as audits become more digitally interconnected.

Implications of internal auditing in banks

Wider implications of internal auditing in banks may include: 

• Companies incorporating AI-driven audits to reduce human error and lower the risk of regulatory fines.
• Banks investing in cybersecurity infrastructure to protect customer data, leading to higher operational costs but increased consumer confidence.
• The growing need for auditors with specialized technology skills, shifting labor markets and requiring new education and training programs.
• Automation in audits potentially reducing entry-level banking jobs, creating pressure on younger workers to pursue more technical careers.
• Increased transparency in financial reporting fostering greater public trust in financial institutions, positively affecting consumer behavior.
• Banks adopting more efficient audit processes to meet environmental goals, as digital audits reduce the need for paper-based documentation.
• The rise of AI audits creating new ethical concerns for policymakers, resulting in public debates over data security and accountability.
• The reliance on complex algorithms prompting businesses to reassess their contingency plans in the event of system failures, ensuring continuity.

Questions to consider

• How could greater transparency in banking operations increase your trust and interactions with financial institutions?
• How might improved fraud detection through AI affect the security of your personal financial data?