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Did you know that you can turbocharge your Fraud and AML technology without a costly and time-consuming system replacement?

Getting better results from your transaction monitoring system doesn’t have to be a major headache. You can make big improvements without replacing or even upgrading your current system.

At Sygno, we’ve designed a software solution that plays nicely with other models. It can easily be integrated within your existing transaction monitoring engine and can generate its first model in a matter of hours.

No architectural changes are needed, which means that you can expect tangible results quickly. Full implementation can take as little as eight weeks, including your internal model validation time.

We can do this because our solution approaches transaction monitoring in a new way. Using automated machine learning to extract patterns of normal customer behavior, Sygno’s detection model is much better at spotting anomalies, which helps to reduce false positives and increase true positives.

It’s simple, but also effective. Unlike most Fraud and AML technology, Sygno can turbocharge your transaction monitoring without adding layers of complexity. We also provide our clients for each of their models a detailed report that explains exactly how our model was generated, which means your compliance team can easily present it to regulators.

The simplicity of our solution also means less workload for data scientists and case analysts. Freed from the burden of an unnecessarily complex system that generates too many false positives, they can focus on higher-value tasks.

Financial regulators in search of a solution to “de-risking” are starting to ask if artificial intelligence could be the answer.

The Financial Action Task Force (FATF) has noted that banks resort to de-risking due to out-dated legacy systems and a poor understanding of the relevant threats.

“This is where new technologies can provide the most added value”, the Financial Action Task Force (FATF) wrote in a recent report.

The practice of de-risking is mostly a response to anti-money laundering and countering terrorist financing (AML/CTF) obligations. It arises when banks refuse to do business with individual customers or even entire customer categories that they deem to be associated with higher AML/CTF risks.

Banks under scrutiny

Banks justify these types of decisions on various grounds, but such broad actions are now coming under scrutiny. In January 2022, the European Banking Authority (EBA) published an opinion on the scale and impact of de-risking, and called on national regulators to take further action.

In its opinion, the EBA noted that de-risking isn’t warranted under EU law and can be a sign of ineffective AML/CTF systems. Banks should manage these risks better rather than resorting to such crude measures.

Some regulators have acted swiftly. The National Bank of Belgium issued a circular in February reminding banks of their obligations to properly manage risks and forbidding them from excluding entire categories of customers.

The circular also clarifies that banks are free to charge higher prices to riskier customers to reflect the extra costs associated with added due diligence.

New technologies

However, the problems may run deeper than a lack of basic due diligence, as FATF has pointed out. The traditional rules-based approach tends to be static, cannot analyse data at a large scale and rarely provides a real-time overview of risks, which is why banks resort to overly defensive, box-ticking practices such as de-risking.

Solutions powered by artificial intelligence and machine learning could be the answer to the need for more granular insights. Such approaches allow banks to identify, assess and manage AML/CTF risks much more efficiently, as well as enabling a richer level of analysis and insights.

As regulators take the impacts of de-risking more seriously, it may only be a matter of time before they require banks to adopt new technologies to improve their management of AML/CTF risks.

De-risking impacts

In Europe, the EBA sees de-risking as a threat to the EU’s financial inclusion goals and is also worried about anti-competitive effects.

As the FATF states, individuals affected by de-risking are often from vulnerable, minority backgrounds, such as asylum seekers from countries associated with AML/CFT risks. Excluding such individuals from access to basic banking services raises human rights concerns and may have unintended consequences that increase AML/CFT risks.

De-risking may also conflict with EU laws requiring banks to provide objective, non-discriminatory and proportionate access to payments institutions, which are a common target of de-risking.technologies to improve their management of AML/CTF risks.

De-risking may also conflict with EU laws requiring banks to provide objective, non-discriminatory and proportionate access to payments institutions, which are a common target of de-risking.

Proactive response

Banks that are moving proactively to embed machine learning and artificial intelligence into their monitoring frameworks are well placed to benefit. Addressing the inadequacies that lead to de-risking can ultimately lead to a more accessible and fair banking system and free up compliance resources that can be put to better use.

Managing huge (and growing) volumes of alerts is creating an avalanche of challenges for banks.

Transaction monitoring systems that produce a huge volume of alerts can be a big challenge for banks and other financial institutions. And the challenge is more than a simple matter of costs.

Let’s highlight some of the most serious adverse consequences:

One of the most fundamental issues is analyst retention. Nobody wakes up in the morning excited to slog through another day of exceedingly mundane and repetitive tasks with limited tangible results, but that is often the daily experience for analysts in the anti-money laundering area. Hiring and retaining good people for such roles is therefore tough. And retention of good analysts is important as the work requires skill and experience to verify these complex structures.

Closely associated with this is the issue of quality of investigations. It can be difficult to ensure that the quality of investigations is still sufficient when dealing with so many false positives due to analyst fatigue. The FCA has warned for years about poor practices that “generate large numbers of resource intensive false positives” and result in firms “discounting actual target matches incorrectly as false positives due to insufficient investigation”.

Which leads to the issue of missing crime. Catching real criminals is the core goal of transaction monitoring. When analysts are spending too much time on false positives, and financial institutions have to make choices about which cases to investigate, there’s a real risk that criminal activity will go undetected. This can lead to difficult conversations with regulators.

Last but not least, there is customer friction. False positives that block legitimate transactions are an obvious pain point for customers. Clients are also being asked to provide increasing amounts of information when being investigated. No businesses want to annoy their good customers for what are often clearly legitimate transactions, so there will always be pressure from the business to mitigate this. When prolonged, this may weaken support for otherwise understandable risk measures.

To make matters worse, the volume of alerts is only increasing and always at a faster pace than the resources available to cope with them.

Where to draw the line. You’ll never be able to do everything. And with so many challenges, it’s obvious that everybody wants to reduce the volume of false positives – while decreasing the risk of missing true positives.

There are no magic bullets

But there are better ways of managing the challenges. One of them is to focus on the expected behaviour of your good customers to catch the bad and reduce clear false positives.

Or as the SWIFT Institute recommended recently:

“Financial institutions should seek to monitor for consistency of client behaviours and report suspicion that arises, rather than seeking to find suspicious activity as a primary activity.”

Focusing on good customers is key to a more effective AML transaction monitoring process

Transaction monitoring has become an endless arms race turning out very costly, cumbersome and which produces very little concrete results.

It’s time for a rethink. Instead of fighting a losing battle trying to model criminal behaviour, as we’re used to, why don’t we model the behaviour of good customers?

Training a model to catch financial criminals is like trying to find a needle in a haystack – except the needle is constantly changing to avoid being found. A much more effective approach is to train your model to recognise the hay.

After all, there’s substantially more hay than needles and it has no incentive to change, making it much more predictable. The model therefore doesn’t need to know what needles look like – it just needs to know whether something isn’t hay.

Just like any person finding out about a structural crime saying “why didn’t we see this one, it looks very different from what you would expect in this situation”. Well, they weren’t looking for crime in that way, only focusing on known criminal phenomenon.

This is why we talk about modelling good behaviour. What can you expect in a situation from a totally legitimate customer?

Look at the haystack, not the needle

Instead of trying to spot criminals, this approach focuses on normal customer behaviour with a model that’s trained on legitimate transaction data. This might sound simple, but it works. Looking at customers instead of criminals shines a light on previously undetected patterns. Banks that have implemented this type of model have often substantially increased their number of previously undetected crime and cut their false positives by 80%.

This rethink also brings other benefits. Instead of relying on dozens of incomprehensible rules, being tweaked and tuned over time, this risk-based approach is more straight forward and therefore easier to explain to regulators. Think white box, not black box.

Compliance costs

The stakes are high. Most news on transaction monitoring has been about banks incurring fines, but that’s only the tip of the iceberg. LexisNexis estimates the global cost of AML and sanctions compliance to be more than $210bn a year. Most of this money is spent on cumbersome administrative processes targeting totally legitimate customers while doing their daily business.

Despite these staggering efforts, criminals continue to thrive by finding the loopholes in the system. It simply seems impossible to keep pace with knowing how they operate, turning the efforts in an endless cat-and-mouse game with very limited results.

By focusing your model on the good customers, you can focus your efforts again on the real criminals, rather than the other way around.

Start modelling good behaviour today