In a 2018 global study on occupational fraud and abuse published by the ACFE, which analyzed 2,690 cases of occupational fraud, found that the total loss to organizations caused by fraud cases exceeded USD 7.1 billion. Probably no other corporate function is challenged more than internal audit departments. One of the chief responsibilities of audit teams is assessing programs and systems for fraud-related red flags. Internal audit teams are pressured to find hidden risk by their board, regulatory agencies, and the audit committee while at the same time often receiving a cold welcome from the functions they are auditing.
Internal audit teams are tasked with reviewing internal controls, improper payments, cybersecurity, compliance, and a myriad of other topics. The one commonality in internal audit reviews is that they all require some type of data to be reviewed. When internal audit teams create annual audit plans, they routinely plan to take representative samples of data and then conduct a manual analysis to search for abnormalities or anomalous activity. Representative samples require the development of a time-consuming sampling methodology, which coupled with smaller data sets, can lead to potential risk gaps.
According to the ACFE, internal control weaknesses were responsible for nearly half of all fraud incidents. However, there are new technologies and innovations (such as artificial intelligence (AI) and machine learning) on the market that can help mitigate fraud.
Advanced data analytics technology, including AI and machine learning, can assist organizations in processing account openings, loan applications, insurance applications and claims, such as unemployment claims, and other access point-related documents. Emerging data analytics techniques are also vastly improving internal audit’s capabilities to analyze millions of data points instead of relying on the traditional, manual representative sampling methodology.
These new technology solutions can analyze huge amounts of business data to identify hidden commonalities and linkages such as addresses, e-mails, telephone numbers, relatives, and other personally identifiable information. Advanced techniques such as Natural Language Processing (NLP), graph search algorithms and predictive analytics can enhance the effectiveness and efficiency of fraud detection and prevention programs.
New data analytics and AI solutions can help internal audit teams perform more efficient and effective audits of all programs and are also uniquely situated to assist in identifying risks related to improper payments, FCPA, Anti-Money Laundering (AML), fraud, and insider threat matters. Utilizing these new technologies and other advanced techniques can markedly improve internal audit’s ability to investigate data sources for evidence of financial crimes.
Report: How Financial Firms Are Using Artificial Intelligence and Machine Learning to Meet AML Demands of Today and Tomorrow
It’s a well-known fact that the global pandemic caused a radical shift in consumer banking and payments behavior. What isn’t as obvious is how financial institutions responded behind the scenes. Fortunately, a new study helps shed light on the pandemic’s impact on the adoption of new technologies for anti-money laundering (AML) efforts.
Regulators and those handling compliance at covered institutions have long accepted the pitiful state of AML program efficacy, including: An estimated $2 trillion laundered through the global banking system annually 90+% of false positives coming from transaction...
While not mandating that firms invest in technology to automate financial crime investigations, regulators are certainly encouraging it. They are noticing that advanced BSA/AML teams are using robotic process automation (RPA) bots to gather data for investigations. They are aware that those same firms are using machine learning to analyze huge data sets, identify patterns, and pinpoint where exceptions or anomalies exist.