Financial crime poses a significant threat to the wealth management industry. Cyber criminals and scammers — aided by advances in technology — are becoming more sophisticated, and the large pools of wealth in sectors like superannuation ever more attractive targets. Fortunately, advances in data and technology also offer ways for the industry to fight back. There are several data-led approaches to reducing the risk and impact of financial crime, and it is more pressing than ever for the wealth management industry to adopt them.
The growing threat of financial crime
The continued growth in financial crime is driven by several factors, including:
- Digitisation — the wealth management industry has digitised. Cyber criminals can now attack a greater number of services organisations, across a greater number of platforms, than ever before.
- Artificial intelligence (AI) — despite its many potential benefits, AI can also be used to help bad actors develop more creative and convincing scams. Bad spelling and grammar used to be a tell-tale sign of a scam, now scammers are more likely to be identified by their flawless use of all three types of dashes (-–—).
- Geopolitical instability — “scam factories” pop up in conflict zones and places with weak governance. For example, reporting on civil-war riven Myanmar in recent months drew attention to scam centres in Laukkaing. Where geopolitical rivalries exist, scammers and cyber criminals can even be found operating with state backing.
The heightened threat landscape has also prompted activity from government and peak bodies. In September, the Australian Government unveiled a new Scams Prevention Framework. Days later, ASFA, the superannuation peak body, announced the launch of the ASFA Financial Crime Protection Initiative.
Wealth management organisations face not only financial losses from financial crime, but potential damage to their reputations and customer trust. While no strategy is perfect, there are a number of data-led approaches to minimising financial crime that we think are worth consideration.
Screening
Screening can almost be considered a hygiene measure. This approach involves cross-referencing people, transactions, and other activities against key datasets. These include:
- Politically exposed persons (PEPs) lists.
- Lists of sanctions published by the Australian Government, US Department of the Treasury, European Union, United Nations Security Council etc.
- Watchlists and blacklists published by international bodies such as Interpol and the Financial Action Task Force (FATF).
- Lists of individuals in adverse media.
- Lists of state-owned enterprises.
The old-school way to do this kind of screening was using spreadsheet, but for most medium to large organisations this is no longer adequate. Modern screening tools can automate the comparison of large volumes of data against frequently refreshed lists, and generate workflow items and reporting to support the resolution of any issues.
In 2020, Westpac was fined $1.3 billion for failing to identify and prevent suspicious transactions. Among other shortcomings, the bank had allowed suspicious transactions to be made by customers who had previously been convicted of child exploitation offences — and were listed as such. This is exactly the kind of scenario that screening is designed to avoid.
Data analytics
Screening is effective when financial crime involves known risks. However, many financial crimes invariably involve people and entities that don’t appear on any list. Data analytics already has a long history (at least in tech terms) of being able to help organisations detect and prevent financial crime in these situations.
A range of different analytical methods exist. Broadly, these fall into three categories:
- Rules-based and expert systems.
These involve experts defining rules and criteria that, when breached, indicate that illicit activity may be taking place. Think collections of if-statements. - Analysis of unstructured data.
Monitoring unstructured data — emails, documents, call recordings — has historically been difficult to automate. Use of text and voice recognition tools, couple with natural language processing capabilities, mean that this data can now be screened for content that might indicate financial crime. Often these methods are used in conjunction with the kind of screening discussed earlier. - Predictive modelling.
A range of artificial intelligence and machine learning methods can be deployed to detect patterns related to financial crime. These can consider a range of factors, including past behaviour, correlations between seemingly disparate events, and other contextual information. They can do this at a scale and volume that would be impractical for a team of people.
Global payments giant, Visa, recently made headlines with their purchase of Featurespace, an AI-powered fraud detection firm, in what could be seen as an endorsement of this kind of approach. Indeed, banks have been using this kind of technology for decades, as anyone who has received a message from their credit card provider about a suspicious transaction can attest to.
Data sharing between industry participants
Perhaps the most potent strategy in combating financial crime is collaboration across the financial services industry. Criminal networks often exploit gaps between institutions, moving money across borders and between financial entities to avoid detection. Without cross-industry communication, criminals can evade isolated monitoring systems.
By sharing data on known fraudsters and suspicious activities, institutions can create a more comprehensive picture of financial crime threats. ASFA’s Financial Crime Initiative is particularly focused on fostering this type of collaboration within the superannuation industry. The initiative encourages institutions to share intelligence and insights in a structured, secure manner, thus preventing criminals from slipping through cracks in the system.
The Australian Government’s Scams Prevention Framework also emphasises this collaborative approach. By encouraging financial institutions to share data securely and efficiently, the framework aims to see that the entire sector stays ahead of emerging threats.
The fight against financial crime in the wealth management industry requires a multi-faceted, data-driven approach. Screening tools, advanced data analytics, and cross-industry collaboration are essential in mitigating the risks posed by increasingly sophisticated cyber criminals. Screening against known risks, predictive modelling and data sharing between financial institutions can help to identifying and mitigate threats. By leveraging these technologies and strategies, the wealth management sector can better protect itself from financial crime and maintain the trust of clients and stakeholders.
This article was produced as part of The Quarterly – Q1 FY25
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Key Contributors:
Kevin Fernandez is General Manager, Market Strategy and Propositions at Novigi, and is based in the Melbourne office.
Sophie Bowen-James is an analyst in the Market Strategy and Propositions team at Novigi, and is based in the Sydney office.