# The problem with current Anti-Money Laundering processes: A lack of context
Author:  Pal Sinha, Barnali 
Author URL: https://financedigest.com/author/pal-sinha-barnali
Published: 2021-10-04
Category: FINANCE
Category URL: https://financedigest.com/category/finance
Meta Title: The Future of Anti-Money Laundering: Solving Critical Issues
Meta Description: Discover how to enhance AML efforts by merging KYC and Transaction Monitoring processes to uncover hidden connections and prevent financial crime.
URL: https://financedigest.com/the-problem-with-current-anti-money-laundering-processes-a-lack-of-contexthtml

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**_By Alexon Bell_**

[Anti-money-laundering](https://www.quantexa.com/solutions/anti-money-laundering/markets/) efforts are prone to some wins and some failures, as it has been since the dawn of the anti-money laundering industry. Governments, law enforcement and citizen protection organisations often struggle with pinning down bad actors. This is because bad actors are highly skilled at hiding their tracks.

Anti-money laundering initiatives should be modelled around strong Know Your Customer ( [KYC](https://www.quantexa.com/solutions/kyc/)) principles as the risk is always with the customer.  To better understand your customer, KYC and Transaction Monitoring processes need to merge (or highly overlap) and the age-old system used to dismantle criminal [organisations – by sorting identities and drawing out the network](https://www.financedigest.com/6-steps-to-organising-an-introvert-friendly-networking-event.html "6 Steps to organising an introvert-friendly networking event ") formed by the existing account, the individuals they represent, and the companies these are connected to – must come together to provide context.

Essentially, the new approach is about validating the KYC your customer presents by understanding their activity in context of their connections, and who they are doing business with.

Here are some ways the model can help [solve anti-money laundering’s most critical issues](https://www.financedigest.com/how-the-financial-services-industry-can-solve-the-issue-of-vulnerable-code.html "How the financial services industry can solve the issue of vulnerable code").

- **Traditional risk detection methods aren’t up to scratch**

Most traditional [approaches to anti-money laundering](https://www.financedigest.com/anti-money-laundering-time-to-take-a-new-approach.html "ANTI-MONEY LAUNDERING: TIME TO TAKE A NEW APPROACH?") (AML)detection have not kept up with the complex, sophisticated networks that criminals use to obscure their activities.

[Legacy AML tools rely on a rules-based system](https://www.financedigest.com/retailers-weighed-down-by-their-own-legacy-systems-survey-reveals.html "Retailers Weighed Down by their own Legacy Systems, survey reveals") that flags each risk in isolation, depending on a strict set of parameters. The problem is that this makes them rigid; criminals can slip through the net easily, and decision-makers miss the full picture.

This approach also creates a staggering number of false positives (95% on average). Often, perfectly innocent behaviour, such as repeated cash withdrawals, or large transactions, are flagged as potential [money laundering](https://www.financedigest.com/money-laundering-made-easy-by-uk-financial-system.html "Money laundering made easy by UK financial system"), wasting investigators’ time and energy. Meanwhile, real criminals are getting away with multiple fake identities, bankrolling trafficking organisations, and [laundering money](https://www.financedigest.com/working-with-both-human-ai-and-graph-technology-to-combat-money-laundering.html "Working with Both ‘Human’ AI and Graph Technology to Combat Money Laundering") via shell corporations, undetected and unhindered.

To tell them apart, [banks need](https://www.financedigest.com/what-banks-need-to-do-right-now-to-improve-their-digital-services-without-sacrificing-security.html "What Banks Need to Do Right Now to Improve Their Digital Services Without Sacrificing Security ") to assess each transaction with its full context. Only by knowing who the parties really are (who sent the money to your customer, and who did your customer send the money on to), their patterns and behaviours, what industry they operate in, among other factors, can [banks uncover hidden connections to risk and filter transactions effectively](https://www.financedigest.com/banking-turmoil-will-not-have-knock-on-effect-on-commodities-trafigura-cfo.html "Banking turmoil will not have knock-on effect on commodities -Trafigura CFO").

This will allow them to discern whether the transaction they are looking at is a legitimate rent payment, or someone trying to “wash” their illicit income under the guise of renting a fake property.

Currently, this [data is assembled](https://www.financedigest.com/the-importance-of-xy-data-in-pcb-assembly.html "The Importance of XY Data in PCB Assembly") by human investigators post-alert generation and is not being used in detection, which is wasteful.

- **Data is going to waste**

[Organisations have an abundance of data](https://www.financedigest.com/can-financial-services-organisations-harness-data-and-bi-to-catch-up-in-the-digitalisation-race.html "Can financial services organisations harness data and BI to catch up in the digitalisation race?  "), but it’s blocked from being truly useful as it’s fragmented and siloed. Without connecting [data to entities of interest](https://www.financedigest.com/new-gleif-challenge-facility-extends-ability-to-trigger-updates-of-legal-entity-identifier-data-to-all-interested-parties.html "New GLEIF Challenge Facility Extends Ability to Trigger Updates of Legal Entity Identifier Data to All Interested Parties"), a data point lacks value. Anyone can set up a bank account, but if that person’s previous history, current connections, and [business activity](https://www.financedigest.com/euro-zone-business-activity-contracted-again-in-aug-outlook-bleak-2.html "Euro zone business activity contracted again in Aug, outlook bleak") are high risk, then it’s worth investigating.

By connecting these large, underused and often disparate datasets, organisations can unlock context, and gain insight into the connections, relationships and behaviours the data represents. Bringing into the mix external data [sets – such as aggregated corporate](https://www.financedigest.com/green-bonds-are-set-to-drive-corporate-esg-debt-out-of-slump-in-2023-barclays.html "Green bonds are set to drive corporate ESG debt out of slump in 2023 -Barclays") registries, law enforcement watchlists or known criminal network members – decision models can begin to piece the puzzle together.

This provides a broader understanding of entities – be it events, people, and companies – framing the knowledge and giving it perspective. Having full oversight of this context means that investigators can use their [time more effectively](https://www.financedigest.com/how-can-financial-services-companies-compete-more-effectively-in-times-of-uncertainty.html "How can Financial Services Companies compete more effectively in times of uncertainty?"), focussing instead on genuinely risky transactions.

**A new model: Intelligence through context**

The revolutionary method of combining data context with artificial intelligence capabilities is better known as [Contextual Decision Intelligence (CDI).](https://www.quantexa.com/platform/) Three key steps form its backbone:

- **Entity resolution**

[Entity resolution](https://www.quantexa.com/entity-resolution/) fixes the data problems in AML and is the process by which hordes of data points spread across multiple systems become connected into a trusted, centralised view. This [delivers a single internal view of the customer](https://www.financedigest.com/how-partnerships-fuel-fintech-innovation-and-help-banks-deliver-a-better-customer-experience.html "How Partnerships Fuel Fintech Innovation and Help Banks Deliver a Better Customer Experience"), a single external view of counter parties (from transactions) and both views enriched with 3rd party data sources.

This offers investigators and the detection engines a complete, meaningful view of data that accurately reflects real-world people, places, and organisations – and the relationships between them. Entity resolution helps [streamline the huge amounts of data](https://www.financedigest.com/reckon-introduces-link-to-bank-data-to-help-small-businesses-streamline-the-reconciliation-process.html "Reckon introduces link to bank data to help small businesses streamline the reconciliation process ") that investigators and the detection engines must sift through, working out whether multiple records are referencing the same real-world person, organisation, address, phone number, bank account, or device.

- **Network generation**

Once all entities are resolved and enriched, and higher-quality data is available, the next step is [network generation](https://www.quantexa.com/platform/network-generation/). Algorithms analyse information and create a dynamic, easy to navigate view of relevant connections, entities, and data for a specific decision.

This [reveals previously hidden](https://www.financedigest.com/insurtech-reveals-hidden-costs-of-home-working.html "Insurtech Reveals Hidden Costs of Home Working") connections and behaviours that would’ve gone unnoticed. It is proving to be very effective at detecting external shell and shelf companies that are either the originator or beneficiaries of a transaction or linked through ownership and/or corporate structure.  This will give decision-makers a far better idea of whether the people they are investigating are simply regular [business owners](https://www.financedigest.com/small-business-owners-are-highly-optimistic-about-2017.html "Small Business Owners Are Highly Optimistic About 2017 "), or part of something more sinister.

- **AI and [machine learning](https://www.financedigest.com/make-time-to-reshape-the-finance-function-with-automation-and-machine-learning.html "Make time to reshape the finance function with automation and machine learning") analytics**

After creating the network, [AI and machine learning](https://www.quantexa.com/platform/scoring-analytics/) create a constantly improving process that can be trained to the specifics of any company’s data sets, vastly improving accuracy, and reducing false positives. Many predictive machine learning algorithms use historic cases to statistically identify patterns. However, many anti-money laundering methods have very few previously known examples.  Therefore, it’s crucial that investigators make sure their algorithms can take advantage of the contextual data they have created by using these [rich networks to input into the analytical](https://www.financedigest.com/how-p2p-analytics-can-help-you-stop-fraudsters-getting-rich-at-your-expense.html "How P2P analytics can help you stop fraudsters getting rich at your expense") components they need.

**If it’s broken, fix it**

Ultimately, despite increased regulation, [financial crime rears its head](https://www.financedigest.com/the-hidden-financial-woes-of-international-students-heading-for-the-uk.html "The Hidden Financial Woes of International Students Heading for the UK") in myriad ways. As [technology advances](https://www.financedigest.com/the-bicycle-brake-components-market-to-be-exalted-with-advancements-in-technology-at-a-cagr-of-7-between-2020-2030-2.html "The Bicycle Brake Components Market To Be Exalted With Advancements In Technology At A CAGR Of 7% Between 2020-2030"), criminals have become more sophisticated in their approaches to hiding the proceeds of crime. With the [recent growth](https://www.financedigest.com/automotive-hmi-system-market-recent-industry-developments-and-growth-strategies-adopted-by-players.html "Automotive HMI System Market: Recent Industry Developments and Growth Strategies Adopted by Players") in money laundering, it’s clear that whatever the trend is, criminals will find a way to exploit it. To withstand the onslaught, [organisations must make sure they have data](https://www.financedigest.com/tackling-the-complexity-of-data-within-financial-organisations.html "Tackling the complexity of data within financial organisations") and context on their side.


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