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Fighting financial crime with smart technology

This is the first blog in a short series by Deloitte Partner, Amanda Lui, written to showcase how the latest technology can help superannuation and wealth management organisations reduce financial crime.

Whether they like it or not, financial services organisations are at the front line of financial crime –facilitating the movement of money while bearing the enormous burden of detecting, mitigating and preventing any financial crimes. And although emerging technologies are driving new products and delivery models to engage and interact with clients in new ways; criminals are also investing in technology and innovating to win big. But their motivation is to conduct complex and layered transactions to exploit weaknesses and avoid detection. So the question is, who will innovate better, and quicker?

It’s only to be expected that consumers have heightened expectations on how they engage with financial institutions as we continue to move to a digital world, as well as the assurance that their assets are well protected. The pressure is always on to protect customer data and outsmart cyber criminals with bad intentions. At the same time, wealth management and superannuation firms make appealing targets for criminals looking to gain access to large portfolios of funds, especially those not regularly monitored by their underlying clients – unfortunately until it’s too late.

In recent years, an acceleration of regulatory scrutiny and significant enforcement action in the Asia Pacific region has shone a spotlight on the governance of these issues, from risk management to board and senior management accountability. We have hand-picked a number of case studies in Asia Pacific to uncover how technology can transform financial crime risk management in financial institutions.

Keeping up-to-date with client information and meeting ongoing due diligence obligations is often challenged by outsourced administration functions and underinvestment in in-house operating models. The result? Highly manual, labour intensive work with room for human error.

This is further exacerbated by the fact that some products, like superannuation, are not required to perform customer identification procedures at the beginning of the customer relationship. Instead, this only happens when certain triggers occur, such as rollover or a customer cashing out their product. Yet customer identification is a critical input for baseline information to assess risk; and if it’s not available from the start, this makes it difficult to confidently determine where certain activity or behaviour should be deemed suspicious.

This results in an over-reliance on screening measures, such as conducting regular checks of the customer portfolio against lists to determine if they’re politically exposed persons (PEPs), sanctioned entities, or individuals.

This combination of initial underinvestment, growing portfolios of customers, and increased backlogs for review means we often see organisations trying to quickly stand up traditional solutions of large operations teams in order to respond to regulatory enquiries or identified compliance deficiencies. This requires substantial investment in training and can still be an arduous task without the baseline customer information. For superannuation firms in Australia, the stapling of member accounts introduced by the Your Future, Your Super changes may make clients more “sticky” to products than they already are, which can both present opportunities to interact with a captive audience but highlights the need for robust and flexible ongoing due diligence practices.

One of our clients in Hong Kong SAR benefited from using Artificial Intelligence (AI) and Natural Language Processing (NLP) to alleviate the manual nature of these tasks.

AI and NLP allows systems to recognise and interpret meaning from the human language, and process large volumes of data to ‘judge’ whether or not individuals or organisations are suspicious. This helps to reduce administrative burdens while ensuring a consistent approach is conducted throughout the organisation.

Our case study demonstrates how these technologies solved an average of 35% of alerts (up to 50% in some jurisdictions) without manual intervention – while still providing adequate ‘explanations’ on the decision-making process.

Are you reading to explore how technology can save time and combat financial crime?

Find out more about Deloitte’s Financial Crime solutions.