The earliest concepts of Artificial Intelligence date back to the 1950s. However it wasn’t until this past decade that AI has really been able to flourish due to the exponential growth in big data coupled with enhancements in computing power, both of which have helped pave the way for AI systems to be applied to nearly every industry.
In the upcoming years, industry professionals expect the growth of AI to continue. According to market research firm Tractica, annual global revenue of AI software is forecasted to grow from $9.5 billion in 2018, to $118 billion annually by 2025. As we see a rise in annual revenue we can also expect to see a continued growth in the number of ways artificial intelligence is applied to real world problems.
In addition to substantial revenue growth, AI focused organizations have also seen a significant jump in venture capital dollars being invested into AI based endeavors. Since 2013, venture capital investments in AI startups have regularly increased, with a compound annual growth rate of about 36% according to PWC.
Recently one of the most effective ways that computer scientists have been harnessing the power of AI is in the field of Anomaly Detection. As per Wikipedia, “anomaly detection is the identification of rare items, events or observations which raise suspicions by differing significantly from the majority of the data”. These anomalies could either point to a risk lurking in the background or an equally important opportunity that can be tapped for increased profitability.
Anomaly detection and AI are becoming increasingly relevant in the financial services industry with its ever growing types of flows and volume of transactions; as well as the difficulty to detect potentially anomalous transactions using traditional rules based approaches. According to a report published by Boston Consulting Group, since the financial crisis in 2008 financial institutions had paid over $321 billion USD in fines due to an abundance of regulatory fines and failings in risk management including operational oversights causing substantial losses for the firms.
With increased regulatory requirements around effective risk management, firms have been increasing their regulatory technology and 2nd line of defense program budgets. Fintech startups are leading the innovation frontier by solving these problems using AI, rather than by taking traditional rules based approach for detecting such issues. AI models are proving to be more effective in detecting anomalies in financial transactions and user behaviors. A firm’s investment in these advanced technologies pays for itself in very little time with its ability to manage operational risks more efficiently and prevent huge losses. Quantel AI can help firms leap frog competitors with the adoption of such technologies. Here at Quantel AI we have successfully developed our own flagship Risk Management and Anomaly Detection solution called EagleAI™.
EagleAI™ has the ability to detect even the tiniest of ‘needles in a haystack’ by using advanced AI models and data engineering techniques designed to process hundreds of millions transactions a day at an enterprise scale, EagleAI™ is able to observe patterns under different conditions to become an expert at detecting anomalies in real-time. EagleAI‘s models automatically weed out noise and alerts users only on the signals that needs their attention. EagleAI™ gives an organization the ability to get timely alerts on potentially large errors, compliance violations, as well as flagrant user behavior and gives firms the opportunity to fix these problems before they balloon into a significant loss or cause damage to a firms’ reputation. If you are interested to get a demo of EagleAI™ and how it can help your firm, please get in touch at firstname.lastname@example.org or call 917-499-9396.
- Introduction to EagleAi TradeWatch
- EagleAi Use Case Series: Fraud Risk – Undetected spoofing practice at a trading firm brings disrepute to the firm along with hefty fines
- EagleAi Use Case Series: Compliance Risk – A junior developer accidentally removes critical compliance checks which goes unnoticed for a very long time
- EagleAi Use Case Series: Market Data Risk – Bad market data causes a broker to reject orders from a client incorrectly which leads to a financial loss