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Uniting against financial crime: the crucial role of data sharing

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Financial crime poses a serious threat to the UK economy, costing billions annually and undermining financial stability. The National Crime Agency estimates that the cost of money laundering in the UK stands at anything between £36 billion and £90 billion, costing an eye-watering £290 billion to the economy – it’s clear that action is crucial.

This leads to a difficult question. How can the flow of money through our financial system be deemed credible if much is derived from criminal activity? The answer lies in improved data sharing.

The current landscape

In our daily lives, data sharing is everywhere – whether it be with the company that delivers your groceries, your car's navigation system, or the search preferences stored on your smartphone. Recognising the pivotal role of big data, we must establish the necessary infrastructure to harness its true potential for managing, controlling, and delivering value.

The recently passed Economic Crime and Corporate Transparency Act (ECCTA) will make it easier for anti-money laundering (AML) regulated businesses to share customer information with each other for the purposes of preventing, investigating, and detecting economic crime.

Historically, there have been several barriers to information sharing financial crime data between financial institutions, including privacy and data protection concerns, reluctance to share data that could potentially erode competitive advantage, and no way of sharing that information in a consistent, standardised manner.

CAIS and fraud Information

Experian has long-established expertise in the area of data sharing and management. In the early 1980s, we introduced Credit Account Information Sharing (CAIS) allowing UK banks to share payment behaviours on their customers.

This data sharing was central to understanding UK consumers’ levels of indebtedness and their ability to repay credit agreements. By understanding who could make their repayments and who could not at an aggregated level, models could be developed to train all available data to accurately predict a consumer’s propensity to default.

From that beginning, fraud data sharing has been pivotal to developing predictive models that could calculate an individual’s likelihood of being a fraudster, helping financial service firms to protect themselves and their genuine customers from criminal activity

A financial crime bureau

Just as bureau data was central to spearheading credit data sharing 40 years ago and fraud data sharing 30 years ago, we believe the same will be true for economic crime data. Embracing a new fraud and AML bureau will give organisations the opportunity to share economic crime data sharing for the benefit of all.

Experian estimate that the creation of a single, national economic crime database could improve coverage by between 15% and 30%, and in return help reduce UK fraud losses by up to £700m per annum.

A singular bureau would revolutionise AML systems. It would increase confidence in matching individual’s information, reduce false positives, and help address the perceived barriers to participating and sharing data. Currently each organisation interested in sharing fraud information is tasked with choosing their preferred data consortium, giving rise to cases where the business feels the choice is too much and so decides not to share at all.

By harnessing the power of technology and data management, authorities can disrupt and dismantle criminal networks, an ambition which can only be achieved through having a fuller, complete picture of what is happening.

Moving forward

Looking ahead, creating a single bureau for fraud and AML data could significantly improve the ability to detect and prevent financial crime. By centralising data and streamlining analysis, such a bureau could reduce losses by hundreds of millions annually and make it easier for organisations to participate in data sharing efforts, fostering greater collaboration for the greater good.

External

This content is provided by an external author without editing by Finextra. It expresses the views and opinions of the author.

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