The concept of artificial intelligence (AI) is broadly defined as the ability of a machine to imitate intelligent human behavior. In other words, it’s about computers that learn - then make decisions accordingly.
The proliferation of data, as well as the huge investments in storing and analyzing it, means that advances in AI technology are gathering pace at an incredible rate. Scientists and researchers are already looking beyond machine-assisted knowledge and operations, and into cognitive computing and human-like reasoning capabilities.
The human brain, a product of millions of years of adaptive evolution, has become the basis on which neural networks are developing. This ‘deep learning’ is the most advanced form of machine learning, and is what powers today’s highly complex AI operations. So, what use does AI have in the financial services industry? Almost too many to count.
The rise of complexity has created a real need for intelligent systems that can detect hidden correlations in big data and model them into insightful business intelligence. In today’s networked markets, learning algorithms have become commonplace.
From predicting customer shopping behavior to picking stock portfolios – the possibilities ae endless. To me, AI’s most exciting and obvious application to the financial industry lies in compliance and security. Many RegTech companies are now using these next-generation systems, with their machine-learning capabilities in compliance, on-boarding, and other financial governance solutions. The payoff is good, AI realizes new efficiencies and reduces false positives, which have been menacing financial institutions.
Current rules-based systems do not account for the rapid advances in technology, which is required for managing this explosive growth of data and the imbedded risks. Meaning we can’t rely on pre-programmed scenarios to identify suspicious activities in networks. Instead, we need systems that can study the data flows and learn suspicious behaviour in transactions, then take action by themselves.These decision support systems deliver cooked results that users can validate. At EastNets, we have recently developed an anti-fraud solution that solely relies on machine learning to predict fraudulent transactions in the SWIFT payment environment. By analyzing account activity patterns in real time, any changes of behavior that would be considered out of the ordinary can be flagged. This could literally stop fraud in its tracks, or at the very least give to anti-fraud teams vital seconds to catch criminals in the act. Implementing AI also has the added bonus of passing huge savings to compliance operations at financial institutions. Intelligent machines have zero downtime, 100% productivity and efficiency and work around the clock.
For an industry that provides solutions for business intelligence, resilience and financial risk management, AI offers us deeper insights and higher product efficiencies – a real game-changer for knowledge-based businesses. With crimes such as money laundering costing the global economy between one and two trillion dollars annually, or nearly 2% of global GDP, industry players can expect more global unification of financial networks and enforced compliance requirements, resulting in a higher probability of sanctions against non-compliant participants.
All of this underscores the need for institutions to commit greater resources to remain compliant, particularly through investing in technologies that embrace the latest technology – such as artificial intelligence. It is up to GCC financial institutions to act now, and be proactive in using all the tools available to them, or risk falling further behind not just their international competitors, but the criminals themselves.