Contextual monitoring is about not having a fixed definition of money laundering – interview with Quantexa

Could you share with our readers what Quantexa does, the idea that triggered the foundation of the company and the problems in the industry it is striving to solve?

Quantexa is an enterprise intelligence scale-up that was founded in 2016 with the aim of helping organisations find hidden connections within their data to enable better decision making. Our background, as founders, is in the anti-financial crime area, fraud, AML and focused on financial institutions and governments. Even though we started our journey in fighting financial crime, when we concluded our Series A investment, our investors noticed our broad range of capabilities: solving the problems in AML and fraud, while also helping organisations to generate revenue and enabling them to make better decisions related to risk.

We had our first successes with organisations like HSBC. Now, we have expanded into other industries and solution areas, where we are currently helping companies apply the same technology to fight the bad guys, find good opportunities and increase revenue.

As banks have difficulty defining their AML transaction monitoring requirements, Quantexa recommends these institutions to use contextual monitoring in fighting fraud. What is contextual monitoring?

Contextual monitoring is a new approach that uses big data techniques and advanced analytic capabilities (machine learning and AI) to detect high-risk anomalous behaviour. It starts by identifying and connecting all available data (multiple internal systems, external sources (Corporate registries, BVD, DNB, etc), internal and external watchlists and intelligence data) about your client and their counterparties at a given point in time. It essentially replicates the laborious research, orientated search and data collation parts of an investigative process, but does so in an automated, yet fully transparent and understandable manner in software. This frees up analyst time to concentrate on quantifying the risk of an alert and in many cases, this will have already been identified by contextual monitoring and highlighted to the investigator. This enables analysts to explore the connections and transactional flows in wider data sets so they can understand the full extent of the risk a case presents.

Existing monitoring systems essentially monitor transactions solely by looking at their value and volume – they have no real idea who initiates the transaction (ultimate originator) or where it ends up (ultimate beneficiary). For example, some scenarios in the current platform include the rapid transfer of funds, where money comes in and money goes out in quick succession. They can be legitimate transfers, like purchasing/selling a house or receiving an inheritance, or risky ones. Contextual monitoring is able to distinguish between the legitimate and illegitimate transfers.

At its heart, contextual monitoring tries to understand and model the actual behaviour specific to the problem we are trying to solve, including complex areas like trade-based money laundering, markets AML or correspondent banking. For monitoring trade finance related AML, we accurately model the relationship between an importer and an exporter. One importer can have multiple relationships with different exporters, so you must understand each of those relationships in isolation and then bring them back together to observe if something is unusual.

Contextual monitoring is about not having a fixed definition of money laundering; money laundering is different for a retail bank, a corporate bank, or a cross-border bank, to capital markets and trade. We are building specific models that use our underlying technology to allow each type of business to add an analytic layer on top. The end result is a process that offers greater accuracy and improved effectiveness, reducing false positives and finding potentially high-risk activity. Contextual monitoring has been proven to identify new cases not found by any existing transaction monitoring systems and at much greater levels of accuracy.

A cornerstone of global anti-money laundering controls are the KYC processes/requirements. What is the difference between effective client identification/source of their wealth and poor KYC standards?

At the moment KYC is more like a snapshot in time; you go to your bank, you say you would like an account and they will KYC you – proof of address, proof of nationality, etc. You are screened against names and then given a risk rating.

If you represent a low risk, the bank might come back to you in 5 years to revise your KYC, whereas if you pose a greater risk you would be reviewed in 3 years, and for a high risk it might be 1 year.

KYC is effective at identifying risk when you come to the bank. Currently, organisations are trying to move towards a continuous event-based KYC environment where you don’t only re-review your customer on a 5-year basis but, based on contextual monitoring, you could re-review your customer’s data after 8 months if it is indicative of being high risk.

The KYC process needs to evolve so that you have a holistic view of your customers’ data and behaviour (i.e. aggregated customer information enriched with data on who they are connected to) to determine whether or not things are “interesting”. This is really important, especially when we are dealing with high net-worth individuals or high-risk industries such as casinos and money service bureaus, jewellery and diamonds, or even politically exposed people from, or related to, a risky jurisdiction.

Congratulations on the successful Series B funding! Could you give our readers more insights into further investments and developments of Quantexa after this funding?

Thank you very much, we are delighted with the result and look forward to working with Dawn Capital. Quantexa plans to use the funds to accelerate and broaden our go-to-market strategy, including increasing the company’s capacity to develop and build new products and take them to market directly or through a partner ecosystem.

We are going to invest in expanding our solution offerings into credit risk, market risk and liquidity risk, as well as developing new solutions in the Anti Financial Crime area. Other investment areas include training and enablement – we have created the Quantexa Technology Academy, where not only Quantexa staff but also our partners’ and banks’ staff, learn how to use our technology. There are currently over 50 bank staff that have gone through our academies and are trained on Quantexa to work on their own projects and build their own solutions using our technology. Our partners have also committed to having over 100 people trained by the end of 2018.

How do you see the anti-money laundering space evolving in the next 5 to 10 years?

I think the current landscape will change almost completely. Existing platforms don’t find the bad guys, they produce tens of thousands of false positives, and they require an army of people to investigate them – this is not effective or sustainable. The industry is calling out for transaction monitoring 2.0, the next generation of AML monitoring systems which will transform the current approach and potentially turn things on their heads.

We are observing the adoption of AI and robotics emerging as key components of an AML framework, and over the next 5 to 10 years these technologies will converge with resolved entities, networks analytics and contextual monitoring at the core of a new approach to AML. This will lead to a change in the way organisations look at money laundering and their approach to fighting it.

There will be a change in not only the detection techniques used but also the way that alerts are investigated. Intelligence-orientated investigation approaches will emerge and there will be a greater collaboration between financial institutions, law enforcement and governments – essentially, they will share their knowledge and understanding of criminal elements. As a whole, there will be more collaboration between not just organisations and banks but across industries. Moreover, private/public partnerships will emerge to help facilitate the information exchange.

We are already seeing a change from regulators and law enforcement in adopting new approaches to increase the amount of criminal assets seized. An example is the unexplained wealth orders in the UK and other places in Europe, to change the burden of proof and force the subjects to prove they acquired this wealth legitimately. This is critical when looking at foreign PEPs who have been able to buy a property worth GBP 5 million in London with his salary of GBP 27k per year.

These new technologies, new approaches and increased collaboration will enable organisations to reduce false positives, catch the bad guys, and also help to balance the compliance budgets with the social responsibility of banks to be inclusive and support the home and foreign economies.

About Alexon Bell

Alexon Bell is a hands-on anti-money laundering (AML) and compliance practitioner with over 16 years’ experience working in the financial sectors. Leading AML investigations during the renowned Panama Papers scandal, Alexon works with banks to tackle new threats and regulations in the fight against organised crime.
Having held leadership roles at Actimize, SAS and Oracle, Alexon has a wealth of experience in helping customers deploy and optimise AML and Know Your Customer (KYC) screening solutions.