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Applying technology to fight money laundering


With the onset of global financial crime institutions now face a dramatic shift in relation to the risk of money laundering; what was once a relatively small and private issue has now become a high profile, high-impact, media event.

Testimonies to this are the record fines being imposed for regulatory breeches, particularly in the US, and the ongoing class actions for alleged negligence in cases of known money laundering. The growing internationalisation of the problem is also bringing new levels of scrutiny; the Financial Action Task Force's ranking of national efforts brings its own pressure to this form of crime.

Whilst the risks have fundamentally changed, in many cases the controls have not. In most instances this relates to the inability for humans to deal with high-volume high-speed transactions in which transfers can be made instantly, remotely and globally. In this environment one possible approach is the use of automated controls.

The globally accepted policy for effective money laundering prevention is to know-your-customer; the so-called KYC principal. However in an increasingly electronic marketplace this is becoming almost impossible to achieve through manual procedures.

The last few years have seen a significant adoption of e-business models. A core feature of which is the remote interaction with customers. The human contact, or face-to-face 'know-your-customer' control is progressively being undermined. As the e-economy accelerates we will no longer have the privilege of meeting our customers, instead a new form of knowledge will be needed; one based on "knowing" a customer electronically.

There are no definitive and explicit rules that state if under condition x and y then the activity in question is money laundering. If such criteria were available tight control systems would already be in place and prevention would not be the major issue that it is. Even the interpretation of control requirements into operational approaches is a non-trivial task.

Since there are no rules that determine what is and what is not money laundering, simplistic rule based systems or threshold approaches are not sufficient for prevention purposes. In fact such rules tend to generate enormous numbers of trivial alerts that inundate banking staff with irrelevant administration. If there are no rules that define money laundering, how can a rule-based approach mitigate the risk? Equally given the volume of business activity how is it possible to achieve comprehensive compliance using human analysts?

One approach is to rely on a new generation of "smart" systems capable of contextual computing. What this allows is the ability for systems to judge activity in the context of that activity, taking into account specific instances based on what is normal or unusual. For example, individual customer's activity would to be judged against their historic behaviour. This introduces risk assessment based on the circumstances of the activity; something hitherto impossible.

This is no panacea; a system cannot assert suspicion, it can alert what is high risk, abnormal and worth interpretation. Human expertise is still required to determine if that activity is "suspicious" and in need of reporting. Operationally this results in an automated control being applied systematically and consistently across the business, ranking genuinely unusual and high-risk activity so as to focus compliance staff on demonstrably high-risk incidents.

Money laundering prevention is a non-trivial task that is increasingly high profile and high impact. The risks to officers and shareholders of a company can be severe and costly.

One approach to combating this threat is the development of automated systems that build and manage individual behavioural profiles. These generate and manage electronic summaries of a customer's behaviour so that a system is capable of judging the risk of activity as it happens and in the context of who is doing it. This approach starts to address the anonymity of modern e-service and starts to introduce an electronic form of know-your-customer.

By knowing the customer the system can rank high-risk behaviour and proactively task compliance officers with genuinely high-risk cases; moving well ahead of traditional audit-based approaches. These methods are helping organisations get much closer to their customer's business habits and preferences; something which can be used as much for improving a customer's service as it is for assessing risk.

SearchspaceŽ is the leading provider of a new generation of enterprise software that uses Artificial Intelligence to automate business actions - such tasks range from fraud detection, compliance reporting, through to customer management. Using this architecture our Anti-Money Laundering system is seen as the leading approach to electronic KYC. Searchspace's clients include such leading organisations as the Bank of New York, Royal Bank of Scotland, Barclays, Bank of Scotland, Archipeligo and Pacific Exchange and Lloyds of London. The company has offices in London, New York and Frankfurt.


Jason Kingdon
Chief Executive and co-founder




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