In the U.S., the area of South Florida has long been ripe for financial crime. According to David Schwartz, president and CEO of the Florida International Bankers Association and QuantaVerse board member, South Florida is viewed as an attractive geography for criminals. “The things that make South Florida an attractive location for investors and people looking for a better place to live — our geography, weather, cultural diversity — attract good people and unfortunately, they also attract bad people,” he told American Banker in a recent interview.
Financial institutions in Miami are under extreme scrutiny to try to detect money laundering, human trafficking and other related global crimes. FinCEN has issued geographic targeting orders for specific zip codes (including Miami-Dade County, Los Angeles County, all boroughs of New York City, and more) which forces banks to scour databases to check if they serve any of the affected businesses in those areas.
Additionally, many South Florida banks often find themselves transacting with Latin American businesses and consequently encounter people with similar or complicated names. “You can get an incredible volume of false positives you have to analyze, so being able to put all that data into a machine and have that machine be able to compile it all, review it all, analyze it, pull it from various sources can definitely go a long way to enhancing banks’ programs,” Schwartz said in an interview with Latin Finance Magazine.
Advanced data analytics technologies such as artificial intelligence (AI) and machine learning hold the promise of helping financial institutions in South Florida and Latin America to effectively and efficiently identify financial crimes by quickly gathering data from various sources and analyzing the data much faster than a human compliance officer could.
Case in Point: U.S. financial institutions with exposure to Latin America
Based on a myriad of factors, the U.S. banks with Latin American exposure are evaluating solutions that could help augment and improve the efficiency of their rules-based transaction monitoring system (TMS), mitigate risk, automation investigations and meet anti-money laundering compliance requirements.
Banks have identified and shortlisted several providers that offered AI solutions for financial crime, and many have ultimately selected QuantaVerse because the company had proven solutions that were ready to deploy at scale in banks’ production environment.
QuantaVerse’s comprehensive Financial Crime Report provides institutions with a quality standard and consistency for SAR reporting obligations by streamlining the process for investigators. These tailored reports include risk scores and supporting evidence enabling investigators to more efficiently evaluate cases. AI-derived Financial Crime Reports ensure investigators apply their time to evaluating complex cases instead of digging for and amassing data. The information in the QuantaVerse Financial Crime Report is tailored to BSA requirements which also speeds SAR filing time.
With these banks’ deployment of QuantaVerse AI solutions, they now have an improved risk management strategy in place and are well-prepared to more effectively and efficiently use their resources to identify 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?