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.
Packaged financial crime solutions are built from the ground up by proven AI experts working alongside experienced AML professionals. Unlike other AI for AML approaches, packaged AI solutions are quick to implement and are continuously updated by the solution provider based on input from multiple customers and regulators.
Serving almost 20 million customers, the bank was concerned about the risks associated with false negatives that its current AML compliance technology was missing. Intent on driving financial crime out of its operation, the bank began searching for a solution that could enhance its existing rules-based transaction monitoring system (TMS) and minimize the risk related to undiscovered financial crime.
While all agree the promise of AI for AML is great, taking the steps to select and implement AI within an institution is relatively new. So, what is the best approach for implementing AI and machine learning into your efforts to combat financial crime?