with HSBC, an influential global bank, to launch an AI-powered solution for anti-money laundering. The solution, known as \u201cAnti-Money Laundering AI\u201d or \u201cAMLAI,\u201d is a cloud-based solution that uses artificial intelligence and machine learning technology to uncover suspicious transactions.\u00a0<\/span><\/p>\nThe partnership between a top tech player and a global financial institution effectively kicked off an arms race in the banking sector. Banks must innovate their AML programs, notably in transaction monitoring, to keep pace with the latest technological innovations. As banks look to implement real-time AML decisioning, they will need to prove the data they have provided is accurate, complete, and readily available.\u00a0<\/span><\/p>\n4 Anti-Money Laundering Systems Architecture Challenges<\/span><\/h3>\nBefore financial institutions (FIs) can rush to innovate, they must first accept the shortcomings of existing AML legacy systems.<\/span><\/p>\nLegacy AML solutions are disparate<\/span><\/h4>\nToo many AML systems are composed of multiple technologies at different maturity levels. The fact is they are disparate solutions of different technologies, vendors, and maturity, some in implementation, some much older, with a lot of IP and complexity written into them.\u00a0<\/span>[\/vc_column_text][vc_single_image media=”121255″ media_width_percent=”100″ uncode_shortcode_id=”135531″ media_link=”url:https%3A%2F%2Fhubs.la%2FQ02dk8fv0|target:_blank”][vc_column_text uncode_shortcode_id=”183014″]This arrangement means data can reside in multiple systems and is very difficult to support from the perspective of data analytics, data modeling, data availability, and data quality. Using multiple technologies also creates a complicated customer experience. End-users must log into several systems to obtain data. Since these systems don\u2019t communicate clearly with each other, end-users could wind up with conflicting information, producing many false positive alerts. If the most recent and relevant data is unavailable, the lag time can affect customer screening, customer KYC processes, and transaction monitoring, making suspicious activity reporting difficult. This is further compounded when data is needed for AI and machine learning processing.<\/span><\/p>\nMoreover, governance of different systems can stretch operating systems and drive up the bank\u2019s ownership cost. Overseeing and maintaining multiple systems requires bank staff to connect insights across various tools. Juggling several governance requirements results in operational and financial inefficiencies.\u00a0<\/span><\/p>\nThe end result is added strain on investigative staff. These professionals are expensive and passionate about AML. However, they can\u2019t focus on where existing or new risk typologies reside or evolve because of existing governance. This isn\u2019t good for the institution either because it increases the total cost of compliance.<\/span><\/p>\nChanging systems is an expensive undertaking<\/span><\/h4>\nBanks invest a lot of effort and time into building the rules, scenarios, testing, and governance behind their anti-money laundering systems architecture. As a result, the cost and risk of change are incredibly high.<\/span><\/p>\nFor example, if a bigger bank has invested $20 million in an AML system, the cost to change a solution may seem low. However, the cost to replicate the intellectual property and processes behind the system, and the update for approval and governance, can be greater than the original solution\u2019s cost, license price, or implementation. In other words, banks have already invested large sums in their existing infrastructure and are reluctant to spend more to change it.<\/span><\/p>\nChanging the status quo is challenging<\/span><\/h4>\nFinding a suitable replacement is not just challenging because of the technological requirements. There is also a tangible risk in replacing a legacy system that can affect multiple stakeholders.\u00a0<\/span><\/p>\nA head of compliance like a Bank Secrecy Act (BSA) officer or a money laundering reporting officer won\u2019t be easily swayed by a vendor claiming they can reduce operational costs by 20% by implementing a new solution. Instead, these professionals want proof that the new solution won\u2019t miss any risk coverage like potential money laundering transactions. If the new solution fails to do its job, these team members could cause a non-compliance problem or land in legal trouble.<\/span><\/p>\nUpdating or even adjusting an AML system can be costly. But winning the hearts and minds of different organizational stakeholders can be even more challenging \u2013 and expensive.<\/span><\/p>\nToo much vendor noise<\/span><\/h4>\nEven if stakeholders agree that their AML legacy system requires an update, the question remains: which vendor to choose?\u00a0<\/span><\/p>\nIf you\u2019re like me, your inbox is probably overflowing with vendor outreach messages promising to fix some of your most pressing issues. Bringing new technologies into a bank\u2019s infrastructure involves a rigorous procurement process. Even after completion, a bank\u2019s IT or engineering team may return to the vendor for specific questions.\u00a0<\/span><\/p>\nIn the worst-case scenario, imagine the vendor shutters or goes out of business. The bank will have invested in a costly solution and cannot connect with anyone who understands how the solution works.<\/span><\/p>\nHow Banks Can Improve AML System Architecture\u00a0<\/span><\/h3>\nAs banks seek to implement real-time transaction monitoring, they must address some of the maintenance challenges of their existing anti-money laundering systems architecture. Here\u2019s how banks can embrace solutions that enable smarter AML maintenance.<\/span><\/p>\nPick scalable solutions\u00a0<\/span><\/h4>\nAny AML solutions a bank implements should not be static. Instead, it should be flexible enough to evolve with changing needs and circumstances. On-prem solutions are ill-suited for this reality. It\u2019s smarter to rely on cloud-based solutions to connect to your bank\u2019s network to install patches and run updates. This arrangement also offsets the need to procure new physical servers and hardware.<\/span><\/p>\nFocus on cost reduction<\/span><\/h4>\nScalability should be sufficiently elastic, so you\u2019re operating at peak capacity. If you have a lot of headroom you\u2019re not using, there\u2019s a cost associated with that. A cloud-based infrastructure can scale up and down and cost exact demand at a precise time rather than carrying an overhead of capacity. The elasticity of demand requirements of the capacity provision. Look for solutions that enable banks to pay for capacity as needed, resulting in smarter AML infrastructure spending. They should also allow greater flexibility for testing new highly data-intensive typologies with large historical data sets.<\/span><\/p>\nFocus on compliance-oriented requirements<\/span><\/h4>\nData moves quickly in the digital age. Any solution a bank implements should be managed as if in your own environment. Banks relying on a 3rd party vendor to handle data must ensure the vendor complies with the jurisdictions where the bank operates.\u00a0\u00a0<\/span><\/p>\nWhether they like it or not, the banking industry is essentially in an arms race regarding innovation. New innovations are being released on a regular basis. Banks that fail to adapt quickly or that adapt to today\u2019s groundbreaking innovation in five years will likely be 10 years behind their competitors.\u00a0<\/span><\/p>\nLegacy anti-money laundering systems architecture has numerous maintenance challenges and a high bank ownership cost. While many stakeholders are likely reluctant to embrace updates and changes to their AML systems architecture, waiting to innovate is not an option. Recent high-profile partnerships between banking and technology organizations demonstrate that real-time AML transaction monitoring is on the horizon.<\/span><\/p>\nTo prepare for this new reality, banks must enhance their anti-money laundering systems architecture with the latest solutions. Otherwise, both customers and regulators alike will be able to detect the pig beneath the lipstick.<\/span>[\/vc_column_text][\/vc_column_inner][vc_column_inner column_width_percent=”100″ gutter_size=”3″ overlay_alpha=”50″ shift_x=”0″ shift_y=”0″ shift_y_down=”0″ z_index=”0″ medium_width=”0″ mobile_visibility=”yes” mobile_width=”0″ width=”2\/12″][\/vc_column_inner][\/vc_row_inner][\/vc_column][\/vc_row]<\/p>\n<\/div>","protected":false},"excerpt":{"rendered":"A speaker at a financial crime conference I recently attended summed up the problem with legacy anti-money laundering (AML) systems quite eloquently. Too often, we\u2019re simply putting digital lipstick on an analog pig. In other words, too many banks underestimate the complexity of their anti-money laundering systems architecture as they seek to deliver real-time AML decisions. <\/p>\n","protected":false},"author":97,"featured_media":129137,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"inline_featured_image":false,"footnotes":""},"categories":[75],"tags":[501,68,77],"acf":[],"yoast_head":"\n
Enhancing Anti-money Laundering Systems Architecture | Feedzai<\/title>\n \n \n \n \n \n \n \n \n \n \n \n \n\t \n\t \n\t \n \n \n \n \n \n\t \n\t \n\t \n