As you know, it’s become imperative that your financial institution takes its digital transformation to the next level so you can compete effectively for the future. And in terms of competing effectively, artificial intelligence is increasingly the newest frontier in digital transformations, playing an ever-more important role in stealing a competitive edge.

Manage the burden of compliance

It’s estimated that the regulatory burden facing financial organisations has increased five-fold since the 2008 global crisis. The costs associated with compliance have risen accordingly (by as much as 60 per cent according to some estimates [1]) and the fines for failure have skyrocketed (over $10 billion globally for breaches of anti-money laundering (AML) legislation alone in 2020).

Keep pace with changing regulations

Gathering accurate compliance information from multiple systems and producing reports is a mammoth task, especially given the wide variety of technology that’s used at most organisations. When put in the context of near-continuous legislative change, compliance becomes a nightmare, setting up chain reactions that cascade through your systems.

Yet, these compliance challenges are fundamentally data problems, and this is where natural language processing (NLP) and intelligent process automation (IPA) excel.

NLP is a branch of AI-based deep learning that’s focused on interpreting natural everyday language. It can ‘read’ and classify huge numbers of documents quickly, extracting useful information – client data, products and processes – that might be subject to regulation and regulatory change. IPA can then be wrapped around that to manage the process of staying up to date with the latest legislation as it grows and evolves.

Manage your storm of data

Artificial intelligence (AI) can also be used by financial organisations to stay on top of the vast amounts of data that are generated by their compliance systems. For example, large volumes of false positives are typically produced by traditional, rules-based alert systems. You can dramatically improve the efficiency of your operations – and reduce costs and manual effort – by deploying AI as the front-line defence in analysing and prioritising those alerts.

Manage your chaos of errors

Humans make mistakes. Any process that depends on human interaction is therefore exposed to potential error. In the financial services industry, that can be very costly indeed. AI and machine learning (ML) applications are therefore being used, more and more, as crucial preventative safeguards. They can watch over huge amounts of data and spot alarming trends and patterns, and they can also shed light on blind spots and raise the alarm when mistakes are identified.

Protect against fraud with anomaly detection

One of the key strengths of AI is its ability to analyse and spot inconsistencies across huge sets of data. These might be sliver-thin – completely invisible unless you view the data set as one, yet impossible for humans to spot from that perspective. By feeding enough valid data into ML tools – transaction histories, for example – normal patterns of data can be understood and learned. Anomaly detection algorithms can then be developed that pick up inconsistent events, and isolate and flag them.

This is especially useful for detecting fraudulent activities across multiple systems. For instance, AML rules demand that transactions of 10,000 euros or more be reported on. Fraudsters may well therefore attempt to keep the value of their dealings below that limit to evade scrutiny. Yet, AI and advanced analytics make it possible to identify subtle patterns across multiple systems that may be suspicious, and they can do that in some instances even if the events have never happened before.

Protect against the growing threat of cyber attack

Many IT professionals see cyber security as the number one application of AI. In a 2020 report [2], 88 per cent of security experts said they believed AI-powered attacks will become common in the coming years. Also, 53 per cent said they expected AI-powered attacks to start manifesting themselves to the public within the next year (4% said it was already happening). So, if that prediction was correct, then AI attacks are already with us.

That’s a major problem. But AI can be used to counter those challenges too. Security information event management (SIEM), which is similar to anomaly detection, can be used to detect threats from across organisations. AI methods can also efficiently scan across multiple systems and compare different information sources to detect potential vulnerabilities. AI can be applied to fend off viruses. It can model user behaviour and detect irregularities in people’s actions. And, of course, you can use it to automatically analyse your entire network and systems, to pre-empt and fight off cyber-attacks.

Build your army of intelligent workers

Of course, your artificial intelligence will only ever be as smart as the people you have running it. Whatever the application, you need qualified people that understand how to use these technologies, and how to unleash their power to function beyond human capabilities.

Yet, those skills exist only at the very leading edge of today’s technological advances and are difficult to recruit. Moreover, their applications all build on your business’s established compliance standards and security posture – critically sensitive responsibilities. So there are major benefits from building up such skills within your organisation, among existing people that are already in related roles.

Despite the seeming complexity of the technology, with the help of the right experts – in both training and the AI itself – you can reap the benefits of upskilling and achieve continuous growth in your organisation. You’ll be able to build your own army of AI experts to run your intelligent applications, and get them up and functioning quickly.

For more information on what’s possible, speak to a QA expert today

 

[1] '4 Ways AI Can Future-proof Financial Services’ Risk and Compliance’, Databricks, 2021

[2] ‘The Emergence Of Offensive AI’, Forrester, 2020​