THE AML CHALLENGE CONTINUES UNABATED…
As this blog has repeatedly catalogued over the last year here[1], here[2] and here[3], Money Laundering is a massive global headache and one of the biggest crimes against humanity. Not a month goes by when we do not hear of billions of dollars in ill gotten funds being stolen from developing economies via corruption as well as from proceeds of nefarious whether it is the Panama papers or banks unwittingly helping drug cartels launder money.
I have seen annual estimates of global money laundering flows ranging anywhere from $ 1 trillion to 2 trillion – almost 5% of global GDP. Almost all of this is laundered via Retail & Merchant Banks, Payment Networks, Securities & Futures firms, Casino Services & Clubs etc – which explains why annual AML related fines on Banking organizations run into the billions and are increasing every year. However, the number of SARs (Suspicious Activity Reports) filed by banking institutions are much higher as a category as compared to the numbers filed by these other businesses.
The definition of Financial Crimes is fairly broad & encompasses a large area of definition – the traditional money laundering activity, financial fraud like identity theft/check fraud/wire fraud, terrorist activity, tax evasion, securities market manipulation, insider trading and other kinds of securities fraud. Financial institutions across the spectrum of the market now need to comply with the regulatory mandate at both the global as well as the local market level.
What makes AML such a hard subject for Global Banks which should be innovating quite easily?
The issues which bedevil smooth AML programs include –
- the complex nature of banking across retail, commercial, wealth management & capital markets; global banks now derive around 40% of revenue from non traditional markets (North America & Western Europe)
- the scale of customer activity ranging from 5 to 50 million at the large global banks
- patchwork of local regulations, risk and compliance reporting requirements. E.g. Stringent compliance requirements in the US & UK but softer requirements elsewhere
- tens of distribution channels
- growing volumes of transactions causing requirements for complex analytics
- the need to constantly integrate 3rd party information of lists of politically exposed persons of interest (PEPs) using manual means
- technology while ensuring the availability of banking services to millions of underserved populations – also makes it easy for the launderers to conduct & mask their activities
The challenges are hard but the costs of non-compliance are severe. Banks have been fined billions of dollars, compliance officers face potential liability & institutional reputation takes a massive hit. Supra national authorities like the United Nations (UN) and the European Union (EU) can also impose sanctions when they perceive that AML violations threaten human rights & the rule of law.
TECHNOLOGY IS THE ANSWER…
Many Banks have already put in rules, policies & procedures to detect AML violations and have also invested in substantial teams staffed by money laundering risk officers (MLRO) & headed by compliance officers. These rules to detect money laundering work based on thresholds and patterns that breached such criteria. The issue with this is that the money launderers themselves are in the class of statisticians and they constantly devise new rules to hide their tracks.
The various elements that make up the risk to banks and financial institutions and the technology they use to detect these can be broken down into five main areas & work streams as shown below.
Illustration: The Five Workstreams of AML programs
- Customer Due Diligence – this involves gathering information from the client as well as on-boarding data from external sources to verify these details and to establish a proper KYC (Know Your Customer) program.
- Entity Analysis – identifying relationships between institutional clients as well as retail clients to understand the true social graph. Bank compliance officers now have gone beyond KYC (Know Your Customer) to know their customer’s customer, or KYCC.[4]
- Downstream Analytics – detecting advanced patterns of behavior among clients & the inter-web of transactions with a view to detecting hidden patterns of money laundering. This also involves assessing client risk during specific points in the banking lifecycle, such as account opening, transactions above a certain monetary value. These data points could signal potentially illegitimate activity based on any number of features associated with such transactions. Any transaction could also lead to the filing of a suspicious activity report (SAR)
- Ongoing Monitoring – Help aggregate such customer transactions across multiple geographies for pattern detection and reporting purposes. This involves creating a corporate taxonomy of rules that capture a natural language description of the conditions, patterns denoting various types of financial crimes – terrorist financing, mafia laundering, drug trafficking, identity theft etc.
- SAR Investigation Lifecycle – These rules trigger downstream workflows to allow human investigation on such transactions
QUANTIFIABLE BENEFITS FROM DOING IT WELL…
Financial institutions that leverage new Age technology (Big Data, Predictive Analytics, Workflow) in these five areas will be able to effectively analyze financial data and deter potential money launderers before they are able to proceed, providing the institution with protection in the form of full compliance with the regulations.
The business benefits include –
- Detect AML violations on a proactive basis thus reducing the probability of massive fines
- Save on staffing expenses for Customer Due Diligence (CDD)
- Increase accurate production of suspicious activity reports (SAR)
- Decrease the percent of corporate customers with AML-related account closures in the past year by customer risk level and reason – thus reducing loss of revenue
- Decrease the overall KYC profile backlog across geographies
- Help create Customer 360 views that can help accelerate CLV (Customer Lifetime Value) as well as Customer Segmentation from a cross-sell/up-sell perspective
CONCLUSION…
Virtually every leading banking institution, securities firm, payment provider understands that they need to enhance their AML capabilities by a few notches and also need to constantly evolve them as fraud itself morphs.
The question is can they form a true picture of their clients (both retail and institutional) on a real time basis, monitor every banking interaction while understanding it’s true context when merged with historical data, detect unusual behavior. Further creating systems that learn from these patterns truly helps minimize money laundering.
The next and final post in this two part series will examine how Big Data & Analytics help with each of the work streams discussed above.
REFERENCES…
[1] Building AML Regulatory Platforms for the Big Data Era – http://www.vamsitalkstech.com/?p=5
[2]Big Data – Banking’s New Weapon Against Financial Crime – http://www.vamsitalkstech.com/?p=806
[3] Reference Architecture for AML
– http://www.vamsitalkstech.com/?p=833
[4] WSJ – Know Your Customer’s Customer is the New Norm – http://blogs.wsj.com/riskandcompliance/2014/10/02/the-morning-risk-report-know-your-customers-customer-is-new-norm/
2 comments
Thanks Vamsi. it is very useful to understand the Key AML problems and solutions approaches.
Thanks for the informative post. I am hoping i can come back to this again before my interview next week.