A rise in non-compliance
In its 2017/18 annual report, the Financial Conduct Authority (FCA) claims it is currently investigating around 75 firms and individuals for AML issues – an increase which it believes is due to a change in its overall approach to opening investigations earlier and more quickly where serious misconduct is suspected.
Many of these investigations apply the FCA’s civil and criminal powers under the Financial Services & Markets Act and the The Money Laundering, Terrorist Financing and Transfer of Funds (Information on the Payer) Regulations 2017.
In its anti-money laundering (AML) annual reports 2016/17 and 2017/18, the FCA highlights that the common causes of non-compliance with AML obligations include:
1. Governance weaknesses
The FCA found that some customer-facing staff have no responsibility for, and little effective training in assessing the money laundering risks posed by customers.
Some compliance and financial crime staff at smaller firms also failed to test the effectiveness of AML controls, allowing weaknesses to go unidentified.
In addition, the FCA discovered firms whose internal auditors had not tested the effectiveness of AML controls over high risk business, and had not ensured remedial work was conducted and tested when weaknesses had been identified.
One way to overcome this is to ensure that external compliance consultants or internal audit functions should rigorously and regularly test and validate AML policies and models using model-driven validation.
By using ongoing model-driven validation to test AML scenarios, rules and risk scores, AML specialists have to demonstrate to senior management and regulators not only how their models are performing against expectations, but how risk exposures fit within defined bands of acceptability.
Fortytwo Data’s Anti-Money Laundering (AML) Augmentation Platform is one such system that can be deployed with rules created in minutes to successfully aid model-driven validation projects.
2. Longstanding and significant under investment in resourcing
This can lead to stale rule sets that have been forgotten and continue to work in the background while policies have moved on. There can often be a reluctance to change rule sets because of a lack of resources.
If there has been longstanding and significant underinvestment in resourcing, then an organisation’s AML software is likely to be inefficient. The most cost-effective and quickest way to address this is to augment and reinforce the existing AML platform, although a complete platform replacement may be preferred.
Fortytwo Data’s AML Augmentation Platform enhances all of the most critical functions by bolting onto a firm’s existing methods of data collection, storage and manipulation. It therefore provides all the effectiveness of a completely new system, with none of the inconvenience and time delay. It also increases the lifespan of current AML systems and enables users to experience the platform before making a complete switch to a new technology.
By adopting big data technology and machine learning, Fortytwo Data’s AML Augmentation Platform leverages the very latest technology that can be applied to AML obligations.
3. Ineffective risk-based approach with imperfect know your customer (KYC) due diligence and monitoring standards
This can result in unsatisfactory identification and monitoring of customers who are politically exposed persons or high risk for other reasons. It can also lead to lack of transparency on ultimate beneficial owners (UBOs).
While there are globally defined standards for performing sanction screening and due diligence, every company implements different variations according to their risk appetite. Regardless of approach, sanction risk management must be case specific and seek to reduce false positives and false negatives in the screening process.
Fortytwo Data’s Sanction Screening Augmentation platform combines the latest technology to reduce false positives in the sanction screening process by up to 93%. Its ongoing account activity review module can automatically check an entity’s activity against expected behaviour as defined at onboarding. Anomalies are highlighted to the user by a simple traffic light system.
Achieve AML compliance with Fortytwo Data
If you would like information and advice about how Fortytwo Data can help you achieve and maintain compliance with AML obligations, please get in touch on 020 8242 4828 or email email@example.com.