The financial industry is at a turning point where AI has become a double-edged sword. While it’s driving innovation, it’s also introducing new risks to contend with.
Over the last few years, as the industry evolves to combat fraud with AI, criminals not encumbered with strict regulation and able to take advantage of large banks’ slow processes have become more sophisticated in leveraging these very solutions to orchestrate cyberattacks. In fact, almost half of fraud attempts are fueled by AI.
This is a problem that financial institutions can’t afford to ignore. Shockingly, global losses are forecast to hit $400 billion over the next decade.
From synthetic identities and adversarial machine learning to blockchain vulnerabilities and decentralized finance (DeFi) exploits, the threat landscape is evolving rapidly. Financial institutions must not only address these challenges but also leverage the same AI-driven tools to stay ahead.
So, how exactly do they do that?
The answer lies in pursuing proactive security strategies coupled with greater collaboration across the industry. Let’s dive into how financial institutions can put this plan into action.
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Be aware of the most threatening risks
With the advent of DeFi, flash loan attacks have been on the rise. That’s because fraudsters are taking advantage of the very aspects that have made DeFi so attractive: decentralization, anonymity, and a lack of centralized oversight.
One of the most shocking examples is from 2022, when an attacker used a flash loan to gain voting power and alter governance rules, causing The Beanstalk protocol to lose $182 million. This is a harrowing reality check of how attackers are abusing DeFi protocol vulnerabilities to execute these loans, manipulating prices or draining liquidity pools.
Decentralization seems to open up a wide range of opportunities for fraudsters. In fact, the decentralized nature of blockchain causes poorly written contracts because of a limited focus on software code quality in smart contracts. This opens up loopholes for attackers to take advantage of. In what was dubbed ‘the largest crypto heist in history,’ an anonymous hacker stole $600 million from The Poly Network. How? A vulnerability in its smart contract logic.
Unfortunately, that’s not where the vulnerabilities end for finance companies. There are plenty of avenues of attack that criminals are pursuing, and it’s vital that company leaders ensure they’re keeping aware of these.
Use data and AI to be proactive, not reactive
As cyberattacks continue to advance technologically, financial organizations literally cannot afford to lag behind with an archaic approach to security.
Beyond just reacting to threats, predictive AI solutions empower financial organizations to take proactive measures. These solutions are developed to anticipate cybercriminal tactics and strengthen defenses before an attack occurs by several mechanisms, including natural language processing (NLP) to scan content in customer outreach, real-time monitoring to generate alerts of suspicious activity, and training ML models with historical datasets to pinpoint trends and anomalies accordingly.
To build out a proactive security strategy, there are a number of recommended steps to take. First, assess existing frameworks by identifying current weaknesses and gaps in security defenses—these are the most likely targets criminals will initially try to exploit. One way to gauge the current state of security is through AI-powered simulations of potential attacks.
Next, use historical and current data with the help of NLP and generative AI (gen AI) to analyze the content of emails and customer communications. This helps detect phishing attempts or social engineering attacks while simultaneously training NLP and AI models to detect future threats as they’re exposed to this information.
Finally, real-time anomaly detection within financial ecosystems is a technology that has significant potential to drive cost savings and prevent fraud. With billions of transactions occurring globally, AI has empowered major payment networks like Visa and Mastercard to process and analyze vast volumes of transactional data (including behavioral biometrics) by leveraging real-time insights to identify and mitigate fraudulent activity instantly.
Nurture training and a collaborative approach
Technological innovation can only go so far, especially if an organization relies solely on its own knowledge resources. Equipping personnel in proactive security management must be prioritized.
For example, identifying a customer call as potentially fraudulent is only one part of adequate security management. Teams need to understand how they can act on these alerts so there’s alignment between the AI-driven predictions and ensuing human response to intervene—otherwise, much of AI’s potential is wasted.
Moreover, attempting to independently address the epidemic of cyberattacks will quickly become a Sisyphean struggle. A shared burden is halved, particularly when tackling challenges like operationalizing AI tools to stay ahead of tech-savvy fraudsters. Not only are social integration and AI democratization needed within each organization—they’re also critical across the industry.
To illustrate, high-quality, balanced data is essential to limit bias in AI models and ensure the accuracy of analyses. Unbalanced datasets jeopardize accuracy because they can cause too many false positives or limit tools’ ability to detect more local nuances in fraudulent behaviors. Collaboration between entities to share anonymized data on fraud helps them build a more holistic and balanced dataset. This reinforces learning abilities through a feedback loop to ensure continuous improvement. Besides this, it empowers organizations to generate synthetic fraud data based on known schemes so models are more adept at picking up on anomalies.
There is a knock-on effect here. The more reliable data available, the better—especially when deploying pre-trained models built on generalized fraud patterns to navigate lengthy training and deployment processes. Financial entities can then fine-tune them with institution-specific data to further reduce training time.
Industry-wide collaboration also bolsters a culture of transparency, which is a hugely critical factor to prioritize in security strategies. AI systems, especially those built on deep learning networks, operate as black boxes. This makes it difficult for organizations to explain and justify their decisions to regulators (who are trying to make sure there is no bias).
Alongside this, accountability is another crucial consideration. Banking institutions must comply with several global regulations (such as the GDPR, CCPA, PCI, and DSS) that emphasize accountability and transparency. One of the key solutions for banks is to maintain auditable pipelines that include detailed logs of all data used for training to ensure transparency. While audits don’t have to be shared between institutions, raised standards around transparency and stronger communication can strengthen financial organizations’ reporting capabilities.
Data and knowledge are undeniably powerful in combating the rising onslaught of financial cyberattacks. Without them, financial organizations will continue to lag behind fraudsters who are ultimately turning their own technology against them. Greater collaboration and knowledge sharing are pivotal to fortifying long-term security measures that are proactive and infallible, not only boosting industry awareness on these issues but empowering institutions to reinforce their AI tools’ capabilities.
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