Mohammed Alobaid is a seasoned professional with extensive expertise in cybersecurity and counter fraud management. With a career spanning over a decade, he has made significant contributions to leading organizations across the Middle East. Currently serving as the CEO at Barq Digital for Cybersecurity Services and also the Head of the Counter Fraud Department at Gulf International Bank in Khobar, KSA, Mohammed has spearheaded initiatives that have notably enhanced the bank’s fraud protection effectiveness by over 500%. His leadership in implementing the Counter Fraud Framework (CFF) and creating critical security procedures underscores his proficiency in advancing organizational security.
Prior to his current role, Mohammed was the Chief Information Security Officer at the Saudi Broadcasting Authority, where he developed and executed a comprehensive cybersecurity strategy and risk management program. He also served as the AVP Information Security at First Abu Dhabi Bank, overseeing information security governance for the KSA branch and managing compliance with data privacy regulations.
Mohammed ’s career began with roles at Saudi Aramco and Advanced Petrochemical Company, where he excelled in risk management and cybersecurity strategy. His work has been instrumental in establishing robust security policies and improving operational efficiency across various sectors. He holds a Bachelor of Science in Network Administration & Security from Utah Valley University and has earned several certifications, including Certified Fraud Examiner (CFE), Certified Ethical Hacker (CEH), and Cyber Security Analyst+ (CySA+). His diverse experience and advanced technical skills make him a leading authority in cybersecurity
Introduction
- Expert Bio Introduction:
- Could you please introduce yourself and provide an overview of your experience in cybersecurity, particularly your work with AI and machine learning in fraud detection?
I am a cybersecurity professional that worked in multiple dimensions of technology, but more so in the field of technology defence in general. Working as a CISO
in multiple sectors really opened my eyes and mind to the different angles adversaries might exploit in their relentless effort to disrupt an organization for whatever reason
they’re after. With the emergence of AI and ML, adversaries were of the first to jump on such technology in order to capitalize on its massive power of data retrieval, task-
automation, data manipulation … etc. Therefore, in the pursuit of staying ahead of adversaries, employing AI and ML in cybersecurity and Fraud Combatting has become
a necessity. In a nutshell, utilizing AI and ML in cybersecurity and Fraud Combatting is proven to be effective in slowing adversaries to a manageable degree.
Understanding AI and Machine Learning
- Definition and Basics:
- How would you define artificial intelligence (AI) and machine learning (ML) in the context of cybersecurity and fraud detection?
Let’s start by stating the fact that almost every aspect of our world has a pattern, whether a living organism or a man-made machine, they all have patterns. Because every party is adamant on
surpassing the other (adversaries vs defendants), the utilization of those technologies is only going to increase rapidly and widely, similarly to any other mew technology. The need to developing
talent around AI and ML is ever so daunting in order to stay ahead of adversaries.
- Historical Perspective:
- Can you provide a brief history of AI and ML in cybersecurity? How has their role in fraud detection evolved over the years?
The use of AI and ML in cybersecurity goes back to the 1980’s where rule based anomaly detection started, followed by the emergence of Big Data in the early 2000’s.
those factors drove the evolvement of AI and ML. The same wave has been followed with fraud combatting, where rule-based, after the fact, static systems were in use.
However, because of the technology advancement over the years, the need of AI and ML increased dramatically to protect personal and public assets.
The Role of AI and ML in Fraud Detection
- Mechanisms of Detection:
- How do AI and ML technologies work to detect fraud in cybersecurity? What are the fundamental principles behind these mechanisms?
Simply, and as I mentioned earlier, since everything has a pattern, the ability to detect those patterns quite early is a game changer. This earlier to detect the pattern, the better the decision
is made to whether or not it is an adversary act. Therefore, quicker detection and less damage from fraudsters.
- Types of Fraud Detected:
- What types of fraud are most effectively detected using AI and ML technologies? Can you give some specific examples?
Fraud nowadays is commonly built around social engineering. Those methodologies where a fraudster takes over an account and performs transaction on behalf of a person are not as effective due to the AI/ML
detection and prevention advanced capabilities. Therefore, the current focus of social engineering is to convince the person to perform the transactions that the fraudster wants without their interference
in the process. However, this process depends on the awareness, or lack, of the individual, and their ability to detect a scam call, or chat via texting applications before the incident occurs. None the less, AI and ML
are currently used to detect and prevent even such incidents, by detecting any anomalies in behavioural patterns over a period of time.
- Advantages Over Traditional Methods:
- What are the main advantages of using AI and ML for fraud detection compared to traditional methods?
AI and ML helped due to the speed of detecting and figuring out anomalies (in real-time for most of the cases), as opposed to the traditional methods where data is fed into a system that analyses actions based on a pre-set, static rules. The former lead to an earlier detection of fraud schemes, that in return gets added to the “Learning of the Machine” in order to be prevented the next time it happens.
Implementation and Integration
- Implementation Process:
- What are the key steps involved in implementing AI and ML technologies for fraud detection within an organization?
All organizational systems should feed into the AI/ML engines in order to learn internal and external transactional behaviours. The more accurate and comprehensive data is fed into the engines, the better decisions are taken in return, thus less false positives. Of course, the AI and ML engines require a “learning period” in order to graph patterns for each customer or entity and use it as a baseline
- Integration with Existing Systems:
- How can organizations integrate AI and ML solutions with their existing cybersecurity infrastructure?
It is not hard to integrate almost any system with AI and ML, it is the actual configuration of the engines that is a bit challenging. However, once those engines are properly configured, it is just a matter of preparing what data is sent to those engines for thorough analysis and monitoring
- Data Requirements:
- What types of data are required to train AI and ML models for effective fraud detection?
In my experience, configuring what the AI/ML should read, and react to requires a special type of knowledge in every field related to the purpose. For example, in Fraud, the knowledge required consists of Fraud knowledge, Some cybersecurity knowledge, technological knowledge, and finally great analytical knowledge. The talent pool is limited when it comes to a person with this specific variety of experience. That is why the person responsible of the configuration
is essential for the quality and quantity of data due to the fact that they are the ones deciding what gets fed into the engines and how much of data is required
Challenges and Limitations
- Data Quality and Quantity:
- What challenges do organizations face regarding the quality and quantity of data needed for AI and ML in fraud detection?
In my experience, configuring what the AI/ML should read, and react to requires a special type of knowledge in every field related to the purpose. For example, in Fraud, the knowledge required consists of Fraud knowledge, Some cybersecurity knowledge, technological knowledge, and finally great analytical knowledge. The talent pool is limited when it comes to a person with this specific variety of experience. That is why the person responsible of the configuration
is essential for the quality and quantity of data due to the fact that they are the ones deciding what gets fed into the engines and how much of data is required (Same as the previous answer)
- Model Accuracy and Reliability:
- How do you ensure the accuracy and reliability of AI and ML models in detecting fraud?
Giving the engines the time required to study the environment. In addition to closely monitoring the output and keep tuning to get rid of most of the false positives. Again,
it relates back to whomever is maintaining the AI/ML engines and their experience in the field of cybersecurity and fraud.
- False Positives and Negatives:
- How do AI and ML systems handle false positives and false negatives in fraud detection?
Giving the engines the time required to study the environment. In addition to closely monitoring the output and keep tuning to get rid of most of the false positives. Again,
it relates back to whomever is maintaining the AI/ML engines and their experience in the field of cybersecurity and fraud. (Same as the previous answer)
Ethical and Privacy Considerations
- Data Privacy:
- What are the privacy concerns associated with using AI and ML for fraud detection? How can organizations address these concerns?
Those are usually handled in collaboration within the different departments in an organization. However, there are multiple Data Protection regulations in every country that the organization
must adhere to. This concern is mainly pertinent to the configuration phase due to the fact that the exposure to confidential data is at its highest, but it gets a lot less during normal operations.
Therefore, confidential data masking, in addition to maintaining the access levels of personnel handling the system alerts should manage the risk to a great degree.
- Ethical Use:
- What ethical considerations should be taken into account when deploying AI and ML in fraud detection?
Case Studies and Real-World Examples
- Successful Implementations:
- Can you share any case studies or real-world examples of successful implementations of AI and ML in fraud detection?
- Lessons Learned:
- What lessons have been learned from these implementations, and how can other organizations benefit from them?
Initial configuration can make or break AI/ML utilization. I might sound repetitive, but a collective effort and different backgrounds of personnel put together at initial configuration will result in a
better operation and less issues in the future.
Future Trends
- Evolving Technologies:
- How do you see AI and ML technologies evolving in the field of fraud detection over the next few years?
AI/ML are so efficient and beneficial to the fraud detection, that it is only logical to invest more into expanding their scope and improving them. Therefore, AI/ML will attract more fields to be implemented
into their operations due to the details it can highlight that might have been absent previously, the level of automation it brings and the detailed analysis that can help improve the overall performance of an
organization. Moreover, it can drastically reduce the need for human intervention in day-to-day operations, if not increase the its efficiency.
- Impact of Emerging Technologies:
- What impact do you think emerging technologies, such as deep learning and quantum computing, will have on AI and ML in fraud detection?
Expert Advice and Insights
- Advice for Organizations:
- What advice would you give to organizations looking to implement AI and ML for fraud detection?
I would suggest that organizations do their due diligence prior to introducing AI/ML into their environments, whether it is the service provider, or the person configuring the engines, or ongoing maintaining and fine tuning the engine, those are all factors that need to be though of before implementing the technology. Once those pre-requisites are met, then I would highly encourage utilizing it.
- Advice for Aspiring Professionals:
- What advice would you give to young professionals aspiring to become experts in AI and ML for fraud detection?
I would say Go for it! The field is extremely promising and the demand is way higher than the offer. The field enormously needs AI/ML experts, and it is moving towards depending more on those technologies.
- Skills and Knowledge:
- What specific skills and knowledge should aspiring cybersecurity professionals focus on to excel in the field of AI and ML?
I would say the major skill would be the “Analytical Skill”. Working with AI/ML requires a great deal of data analytics to set the baselines and initial setup in the engine. Also, problem
solving skills are of a great benefit because that is where AI/ML come into play. Figuring out the problem is one part, but utilising the power of AI/ML for solving those problems will make someone excel and shine in the field.
Conclusion
- Final Thoughts:
AI/ML are enabling our field to make big, forward strides in combatting cyber criminals and fraudsters. However, it is only a tool that should not be thought of as to solely and magically resolve all our concerns. It is the skills, abilities of the minds behind the screens that drive the progress of the technology into something widely successful. Honing on the essential skills like analytics, problem solving, statistics, technology.. etc., in other words, the human ability will
be the main factor of the success of AI/ML afterall.
Conclusion:
Thank you for taking the time to share your expertise with our readers. Your insights will greatly contribute to the understanding and advancement of AI and machine learning in fraud detection.
