Advanced technology is taking charge in fighting financial crimes
Developments in technology and economic globalization make sending money anywhere in the world faster and easier today than ever before. However, with these developments comes an increasing challenge to identify and prevent financial crimes such as money laundering and other illicit economic activity. The United Nations Office on Drugs and Crime (UNODC) reports money laundering and related financial crimes make up an estimated 2 -5% of global GDP, or between $800 billion and $2 trillion annually. This is a staggering statistic seeing that these illegal funds can be used to carry out drug and terrorist activities which impact countries all over the world.
Fighting the Shadow Economy
Shadow economies exist in every country and can be simply defined as financial transactions that occur “off the books,” activities done for cash which avoid normal tax and business principles. These activities include paying undeclared workers, money laundering, counterfeiting, and illicit trade. The World Economic Forum (WEF) reported between 2012 – 2014 the estimated global shadow economy to be worth around $650 billion. Greece is a country with one of the largest reported shadow economies, making up as much as 21.5% of the country’s GDP. This has impacted the country to a large degree and is one of the main reasons the country has been involved in a debt crisis for close to a decade. I wrote an article focusing on Greece’s shadow economy, please see the link to view the story here. Financial crimes, and more specifically money laundering erode tax revenues which can be used by governments to improve the lives of its people. Moving forward technology will play a major role in curbing illicit economic activities which fund violence, drug trade, and terrorist activity.
Notable cases of global money laundering activities
Money laundering activity is prevalent in almost every country around the world, however a few cases stand out among the rest.
Europe’s largest bank was handed a $1.9 billion fine by the US government in 2012 after being accused of laundering money for Mexican drug cartels and nations funding terrorist activities (e.g. Iran, Saudi Arabia). Criminal charges were not brought forth by US prosecutors for fear the consequences could severely disturb the global financial market. A scandal of immense debate as the company was pretty much let off with a slap on the wrist even though their illegal activities directly resulted in deaths of civilians in violent countries, mainly Mexico. The fine equated to about five weeks of HSBC’s annual profit, hilariously sad given the damage caused to society.
A Costa Rica-based digital currency company which was specifically designed by founder Arthur Budovsky to help users conduct anonymous and untraceable illegal transactions and launder the proceeds of their crimes. The money laundering service was shut down by the United States government in 2013. The US government stated the site had an estimated one million users worldwide, processed 55 million virtual transactions, totaling $6 billion in criminal proceeds. Budovsky plead guilty to charges of running a money laundering operation and was sentenced to 20 years in federal prison.
The former leader of Nigeria, establishing himself after successfully carrying out a coup in 1993 is known as one of Africa’s greatest kleptocrats, a ruler who uses political power to steal its country’s resources for personal gain. During his five-year reign (1993 – 1998), it is estimated he stole $4.4 billion using European banks to house a majority of the funds. After his death, the Nigerian leaders worked with Swiss authorities to recoup funds from the Abacha family, totaling $360 million. An even more alarming figure is between 1970 – 2008, Nigeria illegally transferred $217.7 billion out of the country.
Known from the popular Netflix series, Narcos, Joe Franklin – Jurado Rodriguez, is widely known as one of the top money launderers of all time. A Harvard educated economist, Franklin worked directly for Cali cartel drug lord Jose Santacruz-Londono. Franklin utilized deposits in 135 accounts in 68 European banks to launder a total of $36 million for the Cali cartel. In 1992, Franklin was convicted in Luxembourg for his financial crimes, ordered to pay a fine of $330,000 and serve five years in prison. He was later extradited to the United States for his crimes and handed down an additional seven and a half years for his financial role in the Cali cartel. His whereabouts today are unknown.
Technology to combat illicit economic activities
Living in an innovative age where technology is developing at a rapid pace, financial institutions are finding new ways to leverage technology to combat illicit financial activity. Technologies like blockchain, biometrics, and machine learning will help bring in a new era of anti-money laundering oversight, hopefully curbing financial crimes that benefit no one but criminals themselves.
Blockchain
Blockchain is a cryptographic ledger comprising of a digital log of transactions which can be shared across a public or private network. Centralized and transparent financial transactions allowing for improved monitoring of any transaction made on the ledger. Financial institutions can develop and implement ‘smart contracts’ with built-in algorithms to run AML functions alerting staff of any suspicious activities. Furthermore, as financial transactions are added to the blockchain they are visible by all attached financial institutions, taking transparency to a different level. This makes laundering and hiding cash much harder for criminals, if not almost impossible.
As Floyd DCosta, co-founder of Blockchain Worx, said in an article, “blockchain-based AML platform makes it possible for regulatory authorities, risk officers, auditors and other relevant stakeholders to monitor complex transactions in an automated and effective manner, as well as immutably record audit trails of suspicious transactions across the system.” This statement alone shows the crime-fighting abilities of blockchain, shedding immense light into the shadow economies around the world. Adoption of blockchain will take time but the technology is already present that can be applied to AML compliance functions and have an immediate impact in deterring financial crimes.
Biometrics
Biometrics is the measurement and statistical analysis of people’s unique physical and behavioral characteristics. These characteristics include DNA, fingerprints, and even iris recognition. I used an iris recognition software to check into my flight at Love Field in Dallas a few weeks ago, interesting to say the least. A major perk of using biometrics is it can be a major deterrent of fraud and illicit financial activity. Financial institutions will be instantly able to improve their Know Your Customer (KYC) compliance through the use of biologically-linked identification processes.
The University of Oxford conducted a study where users believe that biometric authentication is more secure (83%) and more convenient (92%) than passwords. User identity technology is starting to revolutionize personal and financial security, many banks already allow access to their apps via facial or fingerprint identification on smart phones. When financial institutions can more easily and accurately identify who they are doing business with, financial criminals are more reluctant to commit illegal acts such as money laundering. It will be very interesting to watch how biometrics develops into a crime fighting machine moving forward.
Machine Learning
Machine Learning is the science of getting computers to learn and act like humans do. Anti-money laundering is a complex space, a compliance function with heavy regulatory oversight. Although machine learning is slowly being used to support AML functions, regulators have been slow to adapt this technology from a regulatory standpoint. This is mainly due to creating transparency on how machine learning is processing inputs and outputs, the “why” behind the machine learning’s methodologies.
Regarding machine learning applied to AML, a Forbes article stated, “Machine learning has been shown to be particularly useful in conducting suspicious activity monitoring and transaction monitoring, two key AML activities.” Machine learning will allow employees to focus more on complex tasks than manually reviewing every alert from suspicious transactions. Employees can set rules and let machine learning categorize suspicious transactions by set levels, such as high, medium, and low. This will allow employees to better utilize their time to focus on AML activities which need more human touch, leveraging technology to complete more tedious and mundane AML tasks.
Wrapping Up
