Money Laundering: The More We Neglect the More We Regret

The repercussions of money laundering have manifested themselves in outcomes much worse than one could anticipate. Yet the ever increasing threat to socio-economic development as a result of resources dwindling into illicit channels seems like a mere tip of the iceberg.

As the world continues to bounce back from garbs of ill-funded tragedies like Paris or Mumbai or Nigeria, it is worth a thought to sit back and ponder as to what can be expected if we could cut off the supply line for activities which come around to have such catastrophic impacts.

It is typically in a vicious circle that the laundered money operates in. Money gets stashed in banks which have limited capabilities and mechanisms for accountability and is dispensed to illicit channels through fraudulent transactions which the institutions fail to pick up. This money percolates right down and serves as the much needed resource for notorious activities to be planned & executed. The spoils generated from foul activities are again fed back into banks deficient in anti-money laundering (AML) technologies.

Yet another caveat in the anti-money laundering technologies is the time lag between a fraudulent or a suspicious event and the time when it gets reported & detected if at all. Effectively enabling ever increasing regulations and while protecting their customer data which is collated from diverse and disparate sources along with proactive fraud vigilance presents a problem which manual effort alone seems impossible to surmount. Technology is playing its part in this piece but, is there a scope for further intervention from technology? Most certainly, yes.

Industry needs an automated way of searching and sifting through the growing volumes of both structured and unstructured data. Additional layer of analytics that understand and unravel monetary trails using sophisticated models can effectively uncover relationships and predict patterns of suspicious activities.

Anti-Money laundering has to be institutionalized as a ‘must have’ on-going process as opposed to a knee-jerk ‘can have’ exercise for most contemporary organizations. Fraud mechanisms continuously evolve and at a rate faster than technologies to prevent them. In this regard, Cognitive Process Automation (CPA) and Artificial Intelligence (AI) engines that “learn” to develop new rules and associations become an integral part of effective anti-money laundering techniques.

The good news however, is that around the world crevices both in existing financial policies and the enabling technologies are being identified on a priority basis. The even better news is that the technology which can put a dead-stop check on money laundering is also here. The need of the hour remains a change in approach and emphasis with which financial institutions deal with the menace and the ever pervading question of how willing are we?

About the Author

Sriram Kannan is a senior fin-tech and analytics consulting leader with almost 17 years of extensive and progressive experience in a suite of areas - BI, Analytics, EPM/BPM, Management controllership, Finance-technology, Risk & Compliance - with proven ability to manage large-scale, multi-discipline programs effectively and exceed expectations in delivery of results.

At Wipro, he seamlessly collaborates with sales-force and GTM teams in defining and crystallizing strategy, building target operating models, ROI driven road maps and business cases and contributes to crafting and shaping opportunities through "CxO level" engagements. Additionally, his role entails leading Finance, Risk transformation and Performance improvement engagements globally while encouraging the adoption of BI/Analytics solutions for clients - enabling customers gain sustainable competitive advantage through increasing speed and accuracy in reporting, analytics and decision making.

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