Tag: #aml

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FCA Enforcement Trends – 2 Takeaways

At the end of July the FCA published it’s annual report and latest operating metrics which includes details on enforcement trends.

Two quick take-aways:

1. Financial Penalties Have Reduced

Total value and size of fines has dropped from those reported in period 2021/22. The value is down from £313m in 2021/222 to £199m in 2022/23 with numbers increased from 11 to 24. The penalties were dominated by the £107.7m for Santander UK, announced in December 2022, for repeated anti-money laundering failures.

2. Investigations Are Taking Longer

The average time to complete investigations is now 41 months. Increasing from 34 months in 2021/22 and just 25 months in 2020/21. Regulatory and criminal investigations rates have slowed considerably.

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New PSR rules will change the shape of fraud and AML teams

The new PSR rules, announced this week, radically change the liability landscape for firms and will fundamentally change the way they need to structure their fraud and AML compliance processes.

With some simplification, let me explain this statement …

There’s always two sides to every transaction – a sender and a receiver.

Fraud prevention

Fraud prevention systems have, in the main, been built to focus on transactions that are sent. They’ve been built to mitigate the liability of potential losses and to protect customer account holders – the people or companies sending the transactions.

Fraud prevention systems are the ‘hares’ of the compliance world. They run in realtime, interdicting payments, to stop losses before the transaction leaves the sending institution.

Fraud prevention systems don’t, as a rule, consider or focus in detail on received payments. This is not because the institutions don’t believe that their own accounts may be risky , but because the liability for any loss on a payment has always historically sat very squarely with the payment sender.

Anti-Money Laundering

So what about received funds? Typically these are monitored by firms from an AML “proceeds of crime” perspective in order to detect suspicious activity as part of the ins and outs of account flows. These systems also look at sent transactions as well.

AML monitoring processes are the “tortoises” of the financial crime compliance world. They are typically slow and batch based. They look at longer term patterns of account and customer activity to identify money laundering risks. They don’t interdict or stop transactions. Their raison d’etre is to report suspicious activity rather than to prevent it from happening.

So what’s changed?

In a bid to tackle the rising rate of Advance Push Payment (APP) fraud, the Payment Systems Regulator (PSR) announced last week new rules for Faster Payments will mean that both sending and receiving firms are incentivised to act to take action on fraud. Both will become liable for the losses and will split the costs of reimbursement 50:50. 

Firms will need to adapt their fraud controls to look at both sent and received payments. This change will drive the continued convergence of Fraud and AML (FRAML!), both from technology and operational perspectives. If you are monitoring both inbound and outbound transactions in realtime to prevent fraud, why not do the same for AML? Why have two systems that are looking at the same data if they can be rationalised into one? Why have two teams when one combined approach could offer the best of both worlds?

To finish …

The new PSR rules set a new regulatory direction, firms hold a responsibility to monitor all transactions and customer account activity from both an AML proceeds of crime and a fraud prevention perspective. The 50:50 reimbursement split may be seen by some as controversial but will lead to greater levels of cooperation within and between firms that will help drive new models for fraud and AML.

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How much are your AML controls worth?

Always an interesting question to ask given that appropriate anti-money laundering controls are requirement to hold and retain a banking license, or any other business license to operate in a regulated sector.

This week the answer is $13.4bn or perhaps $200m depending on whether you measure opportunity or cost.

This week TD Bank pulled out of a $13.4bn acquisition of First Horizon. Preventing the Canadian bank from becoming the sixth largest lender in the US and costing the TD $200m in a cash payment to First Horizon as a penalty to not complete the deal.

In response to the news, First Horizon came off even worse with shares plunging to a new low and a market capitalisation falling to less than $6bn.

The deal was ultimately scuppered following multiple delays following the Office of the Comptroller of the Currency and the Federal Reserve raising concerns over TD Bank’s handling of unusual transactions and its timeliness in reporting suspicious activity to them.

Other than a recent small value OFAC fine in 2021 ($115k) , TD has brushed more with regulators on themes relating to investor and consumer protection.

Either US regulators were being protective of their domestic market or perhaps there is yet more to unravel on this story …

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U.S. Department of Treasury Sanctions Review

On Monday (18 October 2021) the U.S. Department of Treasury released its 2021 Sanctions Review.
 
The report presents some welcome directions: enhanced multilateral coordination, avoidance of unintended consequences, clarity and reversibility, and the need to modernize. 
 
But it also reminds us why sanctions implementation continues to be an enormous industry pain point, slowing customer on-boarding, creating friction in the payment chain, and creating a compelling need for new technologies and approaches.
 
A 933% increase in OFAC sanctions designations in the last 20 years!

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5 RegTechs to Watch

I was asked today which companies I think have the opportunity to change the way that we think about RegTech. Here’s my list and the reasons why.

There are some great larger organizations, but I’ve deliberately focussed on less well-known or smaller players. And I’ve also tried to pick those that are doing something quite different or operating in an area that is on the cusp of change.

In no particular order:

Neterium : Sanctions Screening

Fircosoft (LexisNexis Accuity) remains the dominant player in transaction screening, but otherwise, the sanctions screening market is extremely fragmented both at the enterprise level (BAE, Actimize etc), platform market (Temenos, FiServ), and with many small vendors (FinScan, ComplyAdvantage). Even though they are all building and promoting their own engines, the market has remained pretty much unchallenged in terms of technology and approach for the last 10 years. But the problem space has changed significantly.

Historically, customer onboarding didn’t require a high precision filter as processes were very manual. With automated workflows and customer experience the new priority, this is no longer the case. In the transaction space investment in sanctions was seen as a sunk cost by treasury teams and not something they were keen to change or improve – it’s now a cost that banks want to shift and the friction that filters create is unacceptable, again impacts customer experience, and is a principle reason why payments are so slow.

There’s an opportunity for the right product to disrupt the space and the Neterium team has the credibility of having done it before. If they don’t get there, then someone else will. If it is not Neterium then I would look to APAC or UAE given the additional challenges that those geographies have on character sets.

HelloFlow: KYC On-Boarding

A tiny startup that is taking a very different customer and application-centric approach to the customer onboarding process. Given their size, they may struggle to get a foothold in the market, but the demo on their website offers a glimpse of how onboarding flows can be easily automated. This could accelerate onboarding for fintech challengers, and ease adoption and adaption pains as they grow into new markets and create new products. More radically, the technology could allow big banks to catch up with the challengers in terms of agility and customer experience.

Tookitaki: AML Transaction Monitoring

So fundamentally AML transaction monitoring is really in need of a reboot, and I’ve not seen anyone yet that really has the vision to make a real change in this area. Today the trend is to improve, rather than replace the underlying transaction monitoring systems, and there are two ways people are working to do this.

The first approach is to streamline investigations through robotic process automation (RPA) and data consolidation, ensuring the analyst has a complete, informed, and re-prioritized view of risk. There are many vendors doing flavors of this: Blue Prism, DataRobot, Arachnys, Quantexa – all with their own merits.

The second is to optimize detection performance.

Tookitaki is in this second group, competing with the consultants who see it as a process problem (PWC, Deloitte etc), the toolkit vendors that see it as an artificial intelligence challenge (C3AI, SAS), and others that may be more focussed on sanctions than AML (e.g. Silent 8).

Tookitaki seems to be ahead in terms of its approach, analyst presence, and overall potential. They also have stronger regulatory ties which give them advantages given the sensitivity of this space.

Ravelin: Fraud Prevention

With a move to online, the fraud problem becomes a burden carried by the merchants and not by the banks. Ravelin was one of the first to offer API integration at merchant checkout that both addresses the merchant fraud problem and enhances the customer experience.

Compared to the fraud models that are applied by the banks, who only see transaction value, place, and time info, Ravelin can monitor the customer activity in more detail. They can understand purchase history, consider the modes of fraud associated with particular goods and services, scan IP information, and even consider customer dwell times.

Given that the old card issuer / merchant acquirer models are under threat from EPI in Europe and direct-to-account payment initiatives are being pushed by everyone (even the card schemes!), it would seem that there’s an opportunity in this area and those that offer easy integration and a complete holistic view of risk will win out.

Apiax: Regulation

The bridge between regulation and what gets implemented at an institutional level is a difficult one to build and has historically been filled by a combination of specialist advice, from big consultancies, legal firms, or specialist consultants and online news and training services such as those offered by Thomson Reuters, LexisNexis, and ACAMS. Consultants are engaged to build the compliance processes at your organization to align with regulations and the news services keep you up-to-date on changes.

The challenge with these approaches is that they do not make the mapping from regulation to implementation easy. And also don’t future proof an organization as regulatory requirements change, new products are introduced, or business shifts to new geographies.

Apiax is trying to become the new bridge. Joining regulation, to rules, to implementation. In theory, their approach could allow a FinTech to set up shop and be compliant with regulation without ever having to have engaged with lawyers or employed domain specialists to guide their implementations. Although this would probably not be viable in practice, many startups and even established institutions could accelerate time to market with this sort of approach.

Final Thoughts

I continue to be excited and inspired by the rate of change in the RegTech space. There are numerous other companies that I know of that could easily have made the above list, and probably even more that I’ve yet to encounter.

The above represents my own opinions, so please take it at face value.

Finally, if you want to have a conversation on any of this please get in touch.

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Cryptocurrency – the best tool for money-launderers

Over the last decade there have been plenty of news stories reflecting the narrative that cryptocurrency is the criminals best-friend. From online drug trafficking at the silk road marketplace, to billions being laundered through bitcoin, to cryptocurrency being the payment form of choice to enable and facilitate ransomware.

The narrative though is changing. Chainanalysis, a blockchain analysis, in their 2021 report suggest that the criminal share of activity in cryptocurrency is declining rapidly – from 2.1% in 2019 to 0.34% in 2020. With some exceptions, increasing regulation of the area and the inherent traceability of most cryptocurrencies means that they could quickly become law enforcement’s best-friend.

A pile of bitcoin

Today, 13 July, in the UK the police announced a seizure of £180m of bitcoin as part of a money laundering investigation, with this seizure hot on the heals of a confiscation in June.

To put this into context, in its annual threat assessment the NCA reported £172 million denied to suspected criminals between April 2019 and March 2020 as a result of defence against money laundering requests. Or the reported £1.6bn of criminal assets recovered between April 2010 and March 2018 – that’s around £200m per year.

So perhaps cryptocurrency really is the best tool for money launderers – to get caught.

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RegTech disconnect between vendors and FIs

On 29 June 2021, the EBA released a new report (EBA Analysis of RegTech in the EU Financial Sector) presenting its analysis of the RegTech in the financial sector. The report presents a number of conclusions that include the need to address knowledge gaps on RegTech amongst regulators, support the harmonization of supervisory treatment and regulations relating to RegTech, and continued encouragement of regulatory sandboxes.

The report has good news for RegTech providers with satisfaction levels for solutions high and IT spend by FIs on the rise for their solutions (increasing at 75% of respondents, remaining stable with 19%, and decreasing at only 6% of respondents).

Presenting results from the perspective of both financial institutions (FIs) and also RegTech providers, the report highlights differences in experiences. It is these differences that are the most interesting and lead to the following observations.

1. RegTechs need to be more sophisticated with product messaging

It is clear from the report that FI requirements and expectation for RegTech has matured. They want to see technology benefits from enhanced risk management, better monitoring and sampling capabilities, and reduced human error. This contrasts with dated messaging from RegTech providers that quote benefits as efficiency and effectiveness and responding to regulatory change.

2. RegTechs need to align offerings to market need

The report highlights the misalignment between where FIs are using RegTech solutions, where they have experience of those solutions, and the proportion of offerings from providers. This shows that AML/ CFT, ICT Security, and Credit Worthiness Assessment are underserved and could present opportunities for RegTech providers.

The large classification of many RegTech providers in the “Other” category (and based on a review of the listing of these categories from the report annex) suggests the need for better market alignment by them. This is especially true as RegTech providers highlight the lack of FI understanding of RegTech solutions as a barrier to entry.

Every RegTech provider will claim the uniqueness of their solutions, but if these solutions don’t meet the expectations of the market place they will be difficult to position and sell and highly unlikely to be successful.

3. Greatest competitor for RegTechs may be internal builds

The overall satisfaction levels for RegTech solutions are high but FIs only just prefer external RegTech solutions (75% overall satisfaction) to those built in-house (70%).

Good news for RegTech based cloud offerings, as Software‐as‐a‐Service (SaaS) solutions had the highest satisfaction level of 83%.

4. Regulation is not the real barrier for RegTechs

90% of RegTech providers consider that the lack of regulatory/supervisory guidance and support as an obstacle to their solutions across different countries. This same view is not held by FIs.

5. RegTechs need to be realistic on deployment times

The report highlights a discrepancy between deployment time expectations. 66% of RegTechs claim deployment times of less than 3 months but the report suggests a 12-18 month deployment cycle is experienced for the majority of solutions by FIs. Although such extended project periods may be due to a lack of technical readiness on behalf of the FIs, RegTech providers should do a better job of setting expectations.

If you’ve found this analysis interesting. Please reach out and I would be very happy to discuss the state of the RegTech market with you.

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Money laundering deadline approaching …

Today, 23 June 2021, the new polymer £50 note will enter circulation, and the Bank of England have announced that 30 September 2022 will be the last day you can use Bank of England paper £20 and £50 notes.

So if you have £5 million of illicit gains stuffed under your mattress then you probably need to get your skates on and start laundering!

Two obvious questions. First, as we transition to digital payments is this the right time to be sustaining the life of a high-value note, more favoured by money launderers than legitimate citizens? Second, as was done in the wake of the Northern Bank robbery, why haven’t central banks used the regular re-issuance of notes as part of a strategy to make crime less profitable? Perhaps, an intelligent feature required as part of the the Bank of England’s Central Bank Digital Currency (CBDC) strategy?

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Precision, effectiveness, and the compliance dilemma!

Attending the FinCrime World Forum (virtually) today and listening in to one of the panel sessions, I was reminded how often people confuse system precision with system effectiveness. The confusion is made worse in the world of anti-money laundering (AML) and compliance as the industry lacks a reliable way to measure the true effectiveness of systems.

Precision: Precision is a measurement of how efficient a system is. In the world of compliance, precision is usually termed the false positive rate of a system. In simple terms, this is measured as follows:

False Positive Rate = Bad Actor Alerts / Total Alerts
or
False Positive Rate = True Positives / (True Positives + False Positives)

As an example, if my bank’s AML solution generates 1000 alerts a month in total and if operational teams find 100 alerts related to bad actors (true positives) and these are escalated for reporting or further investigation then the false positive rate of this system would be 100/1000 or 10%. The system’s precision is 10% as it gets the right answer (finds a true positive) on average once for every ten alerts generated (1 in 10).

Improving precision: This, in theory, is easy! Remove the unwanted alerts generated against legitimate customers (the erroneous false positives) and maintain the same number of alerts generated against the bad guys (true positives). Many vendors are now offering artificial intelligence and machine learning methods that attempt to do this.

In the example, if we can reduce the total alerts generated each month to 500, but still capture the same 100 alerts on the bad guys, then the system precision becomes 1 in 5 or 20%. Great news, the precision of the system has improved!

The system is now more precise, investigations can be performed more efficiently as there are fewer alerts to review, but the improved precision has done nothing to change the effectiveness of the system. Before optimization, the system generated 100 alerts against bad actors and after optimization, it still generates 100 alerts against these same characters and so the system effectiveness is unchanged. The system is more precise but no more effective.

A subtlety, and an error I have seen a number of times at institutions that should have known better, is that improving precision can often make effectiveness worse!

Improved precision can mean lower effectiveness: A naive team of data scientists might run an algorithm that reduces the alert rate to 250 alerts each month but now catches only 75 of the bad actor alerts (true positives). The precision is now 75/250 or 30%, which means even more efficiency and potential cost savings but this comes at a penalty in that the system is now less effective. There are now only 75 true positives alerts detected and the system is missing 25 other true positive, bad actor, alerts that it would previously have detected. So be careful!

Now that we’ve discussed how system precision can be measured and what it means for efficiency and false-positive rates, we can turn to the more difficult issue of measuring effectiveness? Now, this is where it gets tricky!

Effectiveness: System effectiveness is a measure of the total number of accurate bad actor alerts (true positives) that are generated by a system as a ratio of the complete set of bad actor alerts that should have been detected. The formula can be expressed as:

Effectiveness = Bad Actor Alerts / All Bad Actor Alerts
or 
Effectiveness = True Positives / (True Positives + False Negatives)

Here’s where we run into the big issue, the one that is at the crux of all compliance debates. People talk endlessly about the need to measure system effectiveness but to know how effective a system is we also need to know how many bad actors there are operating at our bank so that we can see how many we need to detect! There is a tautology here, if we knew who these bad actors were we would not need to detect them! It is only once we know the complete number of bad actors that we can actually assess whether our AML system is 100% or 0.01% effective.

Returning to our example, if we find 100 bad actor alerts each month and there are only 100 bad actors active at our institution then our system could be 100% effective. But if there are millions of bad actors abusing the institution our effectiveness rate could only be 0.01%.

“Without knowing the unknown it is impossible to accurately assess what we do know.”

The Compliance Officer’s Dilemma

Compliance officer’s dilemma: This leads us to the compliance officer’s dilemma which, to paraphrase Donald Rumsfeld, is that without knowing the unknown we cannot accurately assess what we do know. Or to put it another way, without knowing about all the bad actor cases that our systems should have detected it is impossible to get an accurate measure of overall system effectiveness.

In practice, you can use trade-off graphs and other styles of analysis to get estimates of system effectiveness. These work in the way a gold prospector would, and look at rates of return of detection as you dig deeper into the pile of potential alerts that could be generated. Even with these approaches, it is still impossible to know all the unknowns.

Two takeaways …

First, next time you are asked how effective your AML transaction monitoring solution is perhaps you should give the real answer “it is impossible to know” and then qualify it with the evidence that you have as to why your teams look at the number of alerts that they do and the trade-offs that this represents.

Second, as an industry, we should focus on relative measures of effectiveness and look towards the incremental improvement of these over time. You may not know the absolute end goal of the effectiveness of your systems and processes, but the incremental improvement over time means that wherever that goal is you will be moving in the right direction.

Finally, if you have found this interesting you might also like my article on the challenges of non-verifiable judgements and why fast feedback loops are essential to improve the performance of compliance (and other) systems.

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