Dr. Sonal Sagar Boda

Quantitative Analysis of U.S. Cybercrime Financial Losses (2020–2024): AI-Enabled Predictive Modeling and Priority Areas for Monitoring

Abstract:

This study quantified United States cyber-enabled financial losses from 2020 to 2024 using official series from the Federal Trade Commission Consumer Sentinel Network Data Book 2024 and the Federal Bureau of Investigation Internet Crime Complaint Center 2024 report. The central question examined how reported losses changed each year during this period. Mixed-format tables were collected, definitions and category labels were harmonized, and each source was analyzed independently to preserve scope and comparability. The literature review synthesized recent advances in artificial intelligence and machine learning for security across threat detection, fraud and anomaly detection, incident triage, and threat-intelligence fusion, and situated these advances within established models, frameworks, and standards, including NIST Cybersecurity Framework 2.0, MITRE ATT&CK, ISO/IEC 27001, and the NIST Artificial Intelligence Risk Management Framework, alongside current global policy developments. Using a reproducible KNIME workflow, five-year trends in reports and losses were compared, crime types and report categories were mapped, losses were profiled by age group, state-level complaint patterns including cryptocurrency were examined, and international complaints were summarized where available. Key results showed that IC3 recorded 859,532 complaints and 16.6 billion dollars in reported losses in 2024, a 33 percent increase over 2023, with 256,256 complaints reporting an actual loss. The Consumer Sentinel Network aggregated 6.47 million reports in 2024, led by Credit Bureaus and Information Furnishers, Identity Theft, and Imposter Scams, with more than 12 billion dollars reported lost and high aggregate losses via bank transfers and cryptocurrency. Short-horizon predictive models implemented in KNIME produced forecasts for 2026 and 2027 for overall totals and high-loss categories. All results were interpreted as reported incidents and losses and therefore as lower-bound estimates that retain source definitions and caveats to support policy-relevant monitoring and prioritization.

Profile: 

Dr. Sonal Sagar Boda is a Research fellow at the University of the Cumberlands. He is a Technical Program Manager and leads enterprise-scale CRM transformation initiatives utilizing Salesforce and Mulesoft programs. With deep expertise in cloud computing, agile delivery, and data-driven strategy, he is known for driving impactful digital transformation across complex organizations.

Dr. Boda is a recognized contributor to the global tech and academic communities, having served as a keynote speaker, session chair, and peer reviewer for IEEE and Springer conferences. He holds editorial board positions with Eternal Scientific Publications and the International Journal of Innovative Research in Engineering & Multidisciplinary Physical Sciences. He is also a member of MLE Harvard Square and contributes to the IEEE Artificial Intelligence Policy Committee (AIPC), supporting responsible AI policy governance. Additionally, he has contributed as a judge for global hackathons, helping to evaluate and elevate innovative solutions from emerging tech talent. He is passionate about advancing innovation at the intersection of technology, research, and community impact.