Editor's Note: AI-powered banking data analytics is revolutionizing the way financial services firms manage operational risks. It's crucial for firms to tread carefully and evaluate the risks before implementing AI solutions. In this blog, we will explore the current state of AI and five key considerations that firms should keep in mind when incorporating AI into their operational risk management strategies.
Global Artificial Intelligence (AI) in banking had a market size of USD 8.30 billion in 2019 and is expected to reach USD 130 Billion by 2037 and register a CAGR of 42.9% during the forecast period.
A breakthrough technology, accelerating data availability, and the latest business models and value chains are revolutionizing the ways banks serve customers, operate internally, and interact with third parties. Operational risk must keep up with this spirited environment, involving the ongoing risk landscape.
Legacy controls and processes must be updated, but banks can also look on the imperative to transform as an improvement opportunity. The adoption of latest technologies and the use of new data can improve operational risk management itself. The focus is on risk management, undertaken with greater efficiency and integrated with business decision-making.
Deploying advanced technologies is very significant in the present scenario for financial-services firms. And efforts to address the new complexities are bringing measurable bottom-line impact. For instance, one global bank tackled unacceptable false-positive rates in anti–money laundering (AML) detection—as high as 96%. Utilizing machine learning to identify critical data flaws, the bank made necessary data-quality enhancements and thereby quickly eliminated an estimated 35,000 investigative hours.
Furthermore, a North American bank assessed conduct-risk exposures in its retail sales force. Using banking analytics models to monitor behavioral patterns among 20,000 workers, the bank identified unwanted anomalies before they became tedious problems. The cases for change are diverse and compelling, but transformations can present formidable complexities for functions and their institutions.
The current state of AI
Artificial intelligence (AI) is disrupting diverse industries, but banking is projected to benefit most from incorporating AI systems in the next couple of years. Analysts estimate that AI will save the banking industry more than $1 trillion by 2030.
The interest in adopting AI for risk management efforts is fueled by increasing data regulations and traditional methods of data oversight becoming unreliable, given the large volumes of data that organizations are handling. Amid these regulations and large volumes of data, organizations are desperate for resources to analyze, assess and monitor risk while staying current with compliance pressures.
Operational risks in banks
Operational risks are one of the most significant types of risks faced by banks. These risks arise from the failure of people, processes, or systems in an organization, which can lead to financial loss, reputational damage, and regulatory sanctions. Operational risks can also arise from external events such as cyberattacks, natural disasters, or political instability.
In this blog, we will explore the key operational risks faced by banks and how they can be managed.
1. Cybersecurity is one of the most significant operational risks faced by banks. With the increasing use of technology and the internet, cyber threats have become more sophisticated and frequent. A cyberattack can result in significant financial loss, reputational damage, and loss of customer confidence. To manage cybersecurity risks, banks need to implement robust security measures, such as firewalls, encryption, intrusion detection, and response systems. Banks also need to regularly update their security systems and conduct regular security audits to ensure their effectiveness.
2. Fraud Risks Fraud is another significant operational risk faced by banks. Fraud can be perpetrated by employees, customers, or external parties. Banks need to implement strong internal controls, such as segregation of duties, transaction monitoring, and fraud detection systems, to prevent fraud. Banks also need to conduct regular fraud risk assessments and provide training to employees on how to detect and prevent fraud.
3. Business continuity risks arise from events that disrupt a bank's operations, such as natural disasters, power outages, or cyberattacks. To manage business continuity risks, banks need to have a comprehensive business continuity plan in place that includes backup systems, data recovery procedures, and alternative work arrangements. Banks also need to regularly test their business continuity plans to ensure their effectiveness.
4. Regulatory and compliance risks arise from the failure to comply with applicable laws, regulations, and industry standards. These risks can result in significant fines, penalties, and reputational damage. Banks need to have a robust compliance program in place that includes regular compliance training, internal controls, and monitoring systems. Banks also need to stay abreast of changes in laws and regulations and update their compliance programs accordingly.
5. Reputation risks arise from negative publicity, customer complaints, or other events that damage a bank's reputation. Reputation risks can result in a loss of customer confidence, which can lead to a significant financial loss. To manage reputation risks, banks need to have a robust public relations and crisis management plan in place. Banks also need to monitor social media and other channels to identify potential reputations.
Therefore, AI has the potential to significantly reduce operational risks in the finance sector by automating processes, detecting anomalies, and identifying potential risks before they materialize. By leveraging the power of AI in banking, financial institutions can improve their cybersecurity, compliance, customer service, and risk management capabilities, and ultimately reduce the likelihood and impact of operational risks.
Sr. Vice President and Industry Head – BFSI at Polestar Solutions. He is a management graduate from IIM Lucknow with a Post Graduate Diploma in AI/Machine Learning from IIIT Bangalore. He has played leadership roles earlier in various firms including Deloitte. He is an avid speaker at different industry forums and a regular guide to several MSMEs through CII and NASSCOM.