By Roger Walton, Chief Revenue Officer of Resistant AI
‘Fintech’ will go the same way as ‘internet companies’ in 2001
In 2001 people talked about ‘internet companies’, which had a buzz about them. Now the internet is everywhere, and the term has fallen by the wayside. In 2023, we expect the term ‘fintech’ to go the same way; soon it will seem outdated. Financial technology is already everywhere and in 2023 the focus will be on how to embed it.
Payments will no longer be about completing a transaction. Consumers won’t think ‘now I need to pay’ because the payment will be embedded into what they are doing – it won’t be a separate step. Take Uber as an example: an individual books it in their app, the car arrives and takes them to their destination. The payment automatically happens in the background without them needing to give it a second thought – or reach for their wallet.
Embedded finance will need an increase in embedded Financial Crime
The explosion in embedded finance has been accompanied by a massive increase in automated transactions, which in turn has seen a rise in automated financial crime. With the rise of embedded finance, fraud and anti-money laundering (AML) will also need to be embedded. If this isn’t properly addressed in the year ahead, there will be a spike in nefarious activity.
Many non-financial companies are now offering financial services as part of their offering, which criminals are constantly testing for vulnerabilities. Companies are embracing automation and want to gain as many customers as possible, but in their haste to be the next ‘super app’, many are onboarding their customers and then monitoring their behaviour. We think they should properly assess the risk profile of their customers at the point they onboard them – not after.
These issues also apply to the proliferation of decentralised finance and digital currencies, which are also expected to increase in popularity in 2023. These platforms are also expected to see a rise in fraud and money laundering, which will accelerate the need for an ‘ongoing trusted identity’ for these providers.
The recession will lead to an increase in financial crime
An economic downturn is looming, with rising interest rates and inflation. Fraud always goes up in a downturn and there are many people who are only one or two pay cheques away from financial distress. In this environment, it may be more tempting for them to commit fraud. We also expect to see an increase in money muling – where a person receives money from another person to launder it for them – which we also heard a lot about during the pandemic.
There is a perfect storm brewing with the economic environment, the technology that is available and the increase in financial crime. Fraudsters are highly-organised and have access to the same tools as financial institutions; they even have weekly ‘standup’ meetings just like the developers at financial institutions. Over the coming year the criminals will get even more organised. They can currently onboard themselves in dozens of different ways and create hundreds of loan applications to test the defences of financial institutions simultaneously, and their methods will become even more sophisticated. Dealing with this threat will require an even smarter response.
Anomaly detection will emerge as the preferred approach for AML
Detecting financial crime has typically been done using a rules-based approach where a model of typical criminal behaviour is built and then transactions are compared against it. However, this will soon seem like a ‘whack-a-mole’ approach. New forms of technology and new techniques of committing crime are constantly changing, and the rules for these models can’t keep up.
We believe that using artificial intelligence and machine learning to detect anomalies is the way forward and will be the approach that financial institutions adopt in the future. Instead of building a rigid model of what bad behaviour looks like, anomaly detection looks for anything that is out of the ordinary and can adapt to the changing methods of the criminals. This behavioural monitoring approach also works for detecting fraud and money laundering and removes the need for financial institutions to treat them separately, in different teams using different models and software.
