As financial services organizations venture into the new AI frontier, they need to consider how they can harness AI effectively to detect and mitigate risks in real time. But as organizations explore the benefits of AI, they also need to understand the risks and challenges.
AI could profoundly transform banking because it can analyze vast amounts of data, identify patterns, and make predictions. According to a 2020 survey from The Economist Intelligence Unit, 77% of bankers who responded believe that unlocking value from AI will be the differentiator between winning and losing banks. One of the potential sources of value for banks is AI's capacity to enhance risk management and cybersecurity measures.
AI's prowess in risk detection and mitigation in particular offers huge promise for financial services organizations. A 2022 study by Juniper Research found that AI-driven fraud detection systems could save banks approximately $10 billion annually.
Machine learning (ML) algorithms can analyze transactional data in real time, identifying suspicious activities that deviate from established patterns. This proactive approach enables banks to detect potential threats before they materialize, which can significantly reduce the risk of financial loss.
For instance, AI can monitor transactions and flag any that appear unusual based on a customer's typical behavior. Examples include large withdrawals, frequent transfers to a new account, or transactions made in a location far from the customer's usual area of activity. By identifying these anomalies, AI can alert the bank to potential fraud and prompt immediate action to protect the customer's funds.
In the realm of cybersecurity, AI tools can identify and neutralize cyberthreats, protecting sensitive data from breaches. AI-powered systems can learn from each attack, continually enhancing their defense mechanisms. According to a 2019 survey by Capgemini, 69% of surveyed organizations believe they will not be able to respond to critical threats without AI.
AI can also help cybersecurity teams stay ahead of potential threats in a timely, accurate manner. Artificial intelligence and machine learning tools can:
This real-time threat analysis and response can significantly enhance an organization's cybersecurity posture.
Regardless of the benefits of AI in cybersecurity, financial services organizations have another critical incentive to apply AI for cybersecurity and risk management: They can’t afford not to. AI is becoming more sophisticated and more widely available, which means criminals are using it to mount increasingly complex and dangerous cyberthreats.
Cybercriminal gangs and advanced persistent threat groups are recruiting AI and ML specialists to design malware that can evade current-generation threat detection systems. Meanwhile, bad actors are using AI and ML to design malicious payloads that defy detection and to write customized phishing emails. These trends highlight the need for financial services organizations to stay ahead of the curve by integrating AI into their cybersecurity strategies.
While promising, implementing AI for cybersecurity and risk management in banking is not without challenges. Some of the most common challenges include:
As financial services organizations navigate the new frontier of AI in banking and cybersecurity, it's clear that this technology offers a powerful tool for risk detection and mitigation – and potentially much more. By harnessing AI in cybersecurity, organizations can enhance their security measures, protect sensitive data, and make their operations more resilient and prepared for inevitable threats.
