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The role of AI in helping banks to tackle the next fraud epidemic

The role of AI in helping banks to tackle the next fraud epidemic 3

The role of AI in helping banks to tackle the next fraud epidemic 4By Sujata Dasgupta, Global Head of Financial Crime Compliance Advisory at Tata Consultancy Services, discusses the current state of financial crime and why regulatory tech and artificial intelligence are becoming essential in tackling these threats.

Financial institutions are no strangers to money laundering and fraud. However, the rate and complexity at which financial crime operations are evolving means that compliance teams are feeling the pressure to tackle the current issues and anticipate future tactics. Just recently, UK Finance reported that fraudsters swindled UK banking customers out of £1.3bn in 2021.

The pandemic has revealed what is yet to come 

The pandemic provided a chance for fraudsters and cybercriminals to take advantage of the situation to attempt crimes against financial institutions and their customers – BAE Systems reported that 74% of financial institutions experienced a significant spike in threats linked to COVID-19.

Now, as we are starting to emerge from the pandemic, but the population is struggling with the cost-of-living crisis and rising inflation, financial institutions are seeing a new wave of crimes. For example, Lloyds is seeing a rise in scammers posing as financial providers and offering individuals struggling with their finances fake advance fees for loans. In May this year, these sorts of scams had increased by 90%, with victims losing £231 on average, and the number of cases is expected to rise.

Where do compliance teams come into play?

Technology has transformed how banks and other financial institutions handle their operations to survive in the market and enhance customer experience, from onboarding to payments. However, many of these organisations’ financial crime compliance units still heavily rely on lengthy manual processes, especially when it comes to carrying out checks for money laundering, terrorist financing, fraud, bribery, and corruption.

Many compliance teams continue to be apprehensive about using artificial intelligence (AI) to identify suspicious activity. The question is often whether using AI tools generates fair and accurate data.

Data governance is a crucial aspect to consider when using AI to fight financial crime. The Bank of England and the UK Financial Conduct Authority (FCA) noted in their 2022 report of discussions from the UK Artificial Intelligence Public-Private Forum that while existing governance frameworks and structures provide a good starting point for AI models and systems, they are not yet adaptable enough to deal with each different case of attempted fraud or money laundering.

When used effectively, regulatory technology, or RegTech, has the ability to make handling routine tasks more efficient for financial crime compliance teams, freeing up analysts’ time for investigations and other tasks that require data-driven judgement and decision-making. Machine learning and algorithms can meanwhile be responsible for tasks such as data collation and processing, as well as other tasks early in the process.

AI also provides other benefits, such as monitoring intricate patterns or behaviours for escalation and investigation, as well as detecting and uncovering incompatible relationships and reducing the incidence of false-positive transaction monitoring hits.

More collaboration is needed

Globally we have seen recent developments that encourage the introduction of AI-based RegTech. At the European Union level, for example, The European Commission has emphasised the benefits of using AI-powered platforms in tackling financial crimes in the region. In April 2021, the Commission published its proposal for a new AI regulation, which was the first step in creating the world’s first legislative framework for artificial intelligence.

On a national level, the FCA continues to work alongside UK financial institutions to provide more guidance and discussions around the adoption of AI. For example, the organisation has pledged to launch a review alongside other institutions to increase the use of RegTech over the next ten years.

There has also been more international collaboration between governments. For example, the US and UK governments recently announced plans to encourage the development of new machine-learning technologies and information-sharing partnerships between financial companies to combat money laundering. More co-innovation between financial institutions and governments is needed to tackle current and future financial crime risks.

Fraud, money laundering, and other crimes are continuing to become more complex. Banks and other financial institutions must embrace using new and innovative technology to tackle these threats within their compliance teams or risk becoming victims of one of these financial crimes. Being able to detect suspicious activities quickly, as well as understanding the patterns behind them, is crucial to this process. As it grows more sophisticated, adopting AI will increasingly be crucial to support financial institutions with this.

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