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How to Navigate Tighter AML Budgets with Innovation
Banks need more efficient, cost-effective ways to detect financial crime. Learn how decision intelligence brings precision and speed to investigations.
ACAMS, one of the leading organizations dedicated to fighting financial crime, recently published an in-depth report focused on global anti-financial crime (AFC) threats. The report highlights several threats, which are all important to know and understand.
Notably, the report calls out two internal pressures for banks – reductions in head count and dwindling budgets – as the biggest challenges in the AFC space. This coincides with the belief shared by many respondents that an economic downturn poses a moderate to very high risk to their financial crime functions over the next two years.
If we look at the headlines from the last couple of months, it’s understandable why anti-financial crime professionals are so concerned about these challenges. It seems most banks aren’t immune to the restructuring and required operational improvements that many other industries also face.
Banks globally are reducing staff. And while it’s disheartening to hear about layoffs, it is especially tough news for those in AFC roles focused on safeguarding the bank. These are our friends, neighbors, and colleagues who are deeply committed to helping keep criminals from penetrating and exploiting the financial ecosystem.
These layoffs come at a particularly frustrating time in the fight against financial crime. The BASEL Committee, the international bank supervisory committee established to provide guidance on regulation, supervision, governance, and risk management for the banking sector, releases an AML Index annually to gauge the pulse and trends in the anti-financial crime market. In the most recent summary, the committee concluded that global risks for financial institutions have only increased with new requirements being enacted, while the quality of anti-money laundering (AML) and counter-terrorist financing (CTF) frameworks are getting worse.
Meanwhile, transparency, legal, and political risks are becoming more complicated, while the regulation of new products. such as virtual assets, including crypto, is lagging. In summary, with the current state of anti-money laundering (AML) programs, the industry is falling further behind in the fight against financial crime. The time has come to take a fresh approach.
Effectiveness is key
We’re all aware of the abysmal stats for many of the legacy monitoring and detection systems in place at financial institutions today:
90%(ish) false-positive rates
Roughly half of money laundering activity being undetected
An estimated $2 trillion in criminal proceeds being laundered annually
Can more be done? No one would argue against doing more. But what can be done? Or, maybe more importantly, how?
The challenges mentioned above with staffing restraints and aging monitoring systems underscore the urgency of implementing enhanced AML measures to address evolving operational environments to combat criminal behaviors. Banks must make a shift in their monitoring and detection program approach from merely hoping to identify suspicious transactions that exceed a threshold and trying to understand what they might mean to more confidently and accurately identifying activity related to specific predicate criminal offenses.
This shift includes extending the scope of review to ensure a holistic understanding of their customers and the external parties their customers interact with. In short, banks must understand the risk of their own customers’ behavior and their exposure to external risk. And they need to do so in a more streamlined and automated way that is effective and efficient.
The Quantexa Decision Intelligence Platform powers AML software specifically in the retail banking space with comprehensive single views of entity profiles, their relationships to other parties, and the patterns of activity between those parties. By taking a transformative approach that involves enriching our understanding of often complex activities around subjects of interest and the movement of money and uncovering relationships and activities that are otherwise not obvious, we create the context for typologies tied to a known predicate criminal offense.
These typologies are defined as specific methods or schemes used for laundering money and other financial crimes – trafficking of humans or drugs, mule activity, hawalas, and many more. Each typology has different red flags and indicators based on regulatory guidance and historical outcomes that generate precise alerts linked to those specific crimes. Automating data collection and generating insights with context creates timesaving for investigators who can then make more informed, intelligent decisions.
A byproduct of this more comprehensive approach to monitoring is that different data attributes can be used to distinguish a victim from a perpetrator within transactions. That can help the banks better assist those in need while allowing them to close the accounts of bad actors much faster.
Intelligence-led Investigations
If not now, when?
There is an old adage about people working smarter, not harder. Never has this been truer than in the current AFC environment. Technology exists today that gives analysts and investigators the tools to spend more time on productive work instead of fighting deadlines with bloated queues. By providing automation and intelligence, financial institutions can enable their people to have a greater quality output rather than being overburdened with noise.
Banks need to be honest about what is happening in the anti-financial crime space. Criminals are not laying anyone off; their numbers appear to be growing as the economy becomes more competitive.
Banks also need to find the most cost-effective and forward-looking ways to solve their problems. By harnessing technology to look at monitoring and detection from a new point of view – through predicate typologies – humans can do their jobs more effectively and efficiently.
This technological and human partnership provides value by pinpointing specific risks and uncovering the unknowns through automation. That, in turn, allows the humans to focus more time on complex investigations tied to criminal behavior – the work they were hired to do in the first place.
