Artificial intelligence (AI) has the potential to transform the way we do business, making companies, as The Economist once put it, “swifter, cleverer, and leaner.”
Unfortunately, legitimate businesses aren’t the only ones with access to AI tools. As financial fraud investigators are increasingly finding, fraudsters are exploiting the capabilities of machine learning algorithms to impersonate legitimate users, manipulate data, and evade detection.
And the threat isn’t limited to outside attacks by black hat hackers and other cyber ne’er-do-wells. Unethical employees with access to AI tools are seeing opportunities to use their inside knowledge of businesses to exploit weaknesses and steal from their employers.
FINANCIAL FRAUD: IMPERSONATION, SPOOFING AND MANIPULATION
Financial fraud investigations have revealed that AI-powered fraud attacks often include some form of impersonation, spoofing or manipulation.
Impersonation involves using AI algorithms to imitate the behavior of legitimate users to bypass security measures and gain unauthorized access to systems and data. When spoofing, fraudsters deploy AI-powered chatbots to impersonate legitimate customers, employees, or automated systems to trick victims into revealing sensitive information or performing fraudulent transactions. A fraudster engaged in manipulation will use AI to re-engineer data and transactions to evade detection by fraud prevention systems.
Fraudsters are being aided by the fact that tools for accomplishing AI frauds are becoming more sophisticated, easier to use, and cheaper.
Take, for instance, voice-mimicking. As the Wall Street Journal recently reported, “powered by AI, a slew of cheap online tools can translate an audio file into a replica of a voice, allowing a swindler to make it “speak” whatever they type.” In a highly publicized 2019 incident, thieves using voice-mimicking software imitated the voice of a corporate executive and duped his subordinate into sending hundreds of thousands of corporate dollars into a secret account.
FIGHTING BACK, STEP ONE: FRAUD AWARENESS TRAINING
While the tools and technology may be new, the behavior of fraudsters involved in AI-related scams is hardly groundbreaking. Organizations can take proactive, common-sense steps to protect themselves and fight back.
The first step? Organizations should implement fraud awareness training for their executives and employees to help them understand the risks associated with AI-powered fraud and to identify and report suspicious activity. Employee fraud awareness training should be tailored to the specific needs of the organization and its employees. By providing comprehensive training, companies can help reduce the risk of fraud and protect their reputations, finances, and employees.
As part of fraud awareness training, organizations should:
1) Communicate the impact of fraud: Discuss how fraud can lead to legal repercussions, loss of reputation, and financial losses.
2) Develop a fraud prevention policy: A comprehensive policy will outline the roles and responsibilities of employees in preventing fraud. A policy should include procedures for reporting suspected fraud and the consequences of fraudulent activities.
3) Provide examples of fraudulent activities: Real-life examples of fraudulent activities can help employees understand how a fraud occurs and what they may do to prevent it.
4) Reinforce training through ongoing communication: Provide regular updates on the company’s fraud prevention policy, address new fraud risks, and encourage employees to report suspected fraud.
FIGHTING BACK, STEP TWO: CONTROLLING ACCESS
A robust set of access controls is essential to help an organization prevent and detect unauthorized access and data breaches. Consider implementing or strengthening:
1) User authentication and authorization: At a minimum, passwords and usernames should be required to access an organization’s technology systems. And organizations would be well-advised to implement multi-factor authentication (MFA), which requires that users provide additional authentication factors such as biometric information, security tokens, or one-time passcodes to gain access to systems. MFA measures can significantly reduce the risk of unauthorized access to systems and data. Additionally, access should be granted on a need-to-know basis, so users only have access to the data and functionality they require to perform their job duties.
2) Role-based access control: Implementing role-based access control, also known as RBAC, helps prevent unauthorized access to systems by defining user roles and assigning access permissions based upon those roles. This gives the organization greater control over who can access specific parts of the system and the actions they can perform.
3) Data encryption: Data encryption can prevent unauthorized access to sensitive data within systems. Encryption can be used to protect data both at rest and in transit, ensuring that only authorized users can access the material.
FIGHTING BACK, STEP THREE: CONTINUOUS MONITORING
Continuous, real-time monitoring and analysis of transactions can help organizations detect fraudulent behavior and respond quickly. In a fraud prevention effort or financial fraud investigation, spotting the fraud is all about identifying patterns and anomalies in the data. By using advanced analytics and machine learning algorithms to detect and prevent AI-powered fraud, organizations can reduce the risk of financial loss and reputational damage, and protect their valuable assets and resources. Consider:
1) Real-time detection of anomalies: Continuous monitoring of transactions enables real-time detection of anomalies in user behavior, such as repeated login attempts or unusual access patterns to sensitive data. Machine learning algorithms can analyze transaction data and identify patterns that are indicative of fraudulent activity.
2) Predictive analysis: Machine learning algorithms can identify potential fraudsters and prevent them from carrying out an attack. This involves analyzing transaction data and identifying patterns that are indicative of fraudulent activity, such as purchases outside of a user’s usual spending habits.
3) Early warning alerts: Early warning alerts allow security teams to respond quickly to potential attacks. For example, if a transaction is flagged as potentially fraudulent, an alert can be sent to the appropriate personnel to investigate the transaction.
4) Multi-channel monitoring: Continuous monitoring of transactions across multiple channels—such as online transactions, point-of-sale systems, and mobile devices—can help identify patterns of fraudulent activity that may be missed if monitoring is limited to a single channel.
5) Adaptive learning: Machine-learning algorithms can adapt and learn from new patterns of fraud, enabling them to detect and prevent new types of attacks. This involves analyzing new data and identifying patterns that are indicative of fraudulent activity, and using this information to adjust fraud detection algorithms accordingly.
FIGHTING BACK, STEP FOUR: MONITOR FOR VULNERABILITIES
Just as they would with transactions, organizations should continuously monitor their security protocols for vulnerabilities and ensure that systems are up to date and protected against evolving fraud threats.
Fraudsters don’t clock in and clock out on a regular schedule. They are just as likely to attack in the middle of the night as they are during the workday. It’s costly and inefficient, however, for most organizations to hire 24/7 employees to spot AI-driven cyberattacks.
Increasingly, the solution is to fight fire with fire. Artificial intelligence can be used to help provide continuous security monitoring and protection and to identify weaknesses in systems. Meanwhile, key employees are left to focus on the critical security issues that require human Intervention.
CONCLUSION: TAKE PROACTIVE STEPS
As artificial intelligence becomes more sophisticated, organizations must take proactive measures to protect themselves against AI-powered fraud attacks—including investing in advanced fraud prevention training, policies, and systems, conducting real-time monitoring and analysis, controlling access to their systems through tools like multi-factor authentication, and conducting regular updates to security protocols—including using new artificial intelligence tools to fight back.
By taking these measures, organizations can reduce their risk of falling victim to AI-powered fraud and maintain the trust of their customers and stakeholders. The team at Forensic Strategic Solutions has long used artificial intelligence to assist our clients in detecting and preventing fraud. To learn more about our financial fraud investigation work, contact us for a consultation or visit our Fraud Examinations practice page for more information.