Implementing artificial intelligence (AI) in fintech brings together one of the most important technology trends of the past decade with one of the most innovative and disruptive sectors. AI used by fintechs promises to change how banks and other financial institutions operate a broad range of mission-critical functions, improving their operations and customer experiences in the process. Such is the opportunity for AI in fintech that the market is forecast to reach $27 billion (USD) in 2026, up from $8 billion (USD) in 2020. But what exactly is artificial intelligence in fintech? How is it being used? And how will fintech AI shape the future of the financial services sector?
In this article Chris Oakley, Subject Matter Expert at Featurespace, provides an overview of everything you need to know about artificial intelligence in fintech.
What is AI in fintech?
AI is used for a variety of purposes in the fintech industry. Often mixed with machine learning, a method of training AI, AI in fintech involves “intelligent” systems that automate or enable solutions for complex problems and processes, and/or uncover insights in data. Applications include Anti-Money Laundering (AML) processes, fraud checks, credit checks, decision support, risk assessments, and more.
How will Artificial Intelligence change fintech?
Over the past two decades, the digital transformation of the financial services sector has driven an explosion in fintech. Financial services companies increasingly use digital-first business models to improve customer experiences and drive operational efficiencies within their own business. Examples include everything from the humble banking app to payments alternatives, such as PayPal and Stripe, and much else in between. Meanwhile, APIs have enabled an era of open banking where banks, fintechs, and third parties can share consented customer data to improve customer experiences.
AI in fintech is the next stage in the digitization of financial services. Through automation and AI-derived insights, financial services organizations, including fintechs, will be able to make greater use of data in unprecedented volumes. Automation will drive new levels of accuracy, and analytics at scale will enable organizations to predict customer behaviors and accelerate back-office processes. In other words, AI for fintech will usher in an era of “self-driving,” real-time fintech services ever more focused on the specific needs of individual customers and therefore exponentially more efficient and effective. It’s little wonder that 70% of fintechs already use artificial intelligence with wider adoption expected by 2025.
Trends in AI and fintech
As the use of AI in fintech gathers pace, organizations need to grapple with a number of challenges. These include:
- Regulatory – Lawmakers are focusing on the use of AI in a broad range of sectors to ensure fair outcomes and consumer protection. The European Union, for instance, is introducing legislation that will place stringent controls on high risk use cases for AI, which would likely include fintech applications.
- Data bias – Any company, fintech and financial services providers included, is faced with the need to weed out historical bias that finds its way into AI model training data. If fintech AI is to deliver equitable outcomes, models should only discriminate on relevant factors (e.g., credit history), not because of poor datasets.
As organizations get to grips with these challenges, they will also be looking for new opportunities for fintech AI. From Featurespace’s perspective, one of the most exciting uses for AI is leveraging it in the fight against fraud and financial crime.
Let’s take the area of anti-money laundering (AML) compliance as a case in point. Legacy AML technology is characterized by vendors with point solutions that largely fall short of requirements and lead to numerous fines for non-compliance. This is now changing as firms embrace AI-enabled tools to take a more proactive posture against money laundering.
The change could not have come at a better time. The increase in digital-first banking and fintech services continues at pace, and so too do opportunities to commit fraud. As more transactions are digitalized, AI can help fintechs and other financial services organizations keep pace with the many different types of fraud through real-time detection capabilities.
How has Featurespace helped fintechs with AI?
At Featurespace, we believe the answer lies in using machine learning and artificial intelligence to track legitimate transaction patterns. For example, identifying fraudulent anomalies which are constantly changing to avoid detection. AI for fintech can also be used to establish patterns of consumer behavior to spot fraud and identity theft more easily.
In Australia, for instance, Featurespace is collaborating with eftpos, Australia’s sovereign domestic debit payments scheme, on a powerful new machine learning-based counter-fraud capability designed to stop online crooks in their tracks. We are helping eftpos make the shift to AI-driven predictive fraud scoring, employing self-learning technology to accurately predict individual behaviors in real time to protect people and organizations from the rising threat of fraud and financial crime.
How do you identify fraud with AI?
How do solutions like eftpos’ work? The key to success in using machine learning models for fraud prevention and anti-money laundering use cases, is to establish a baseline of normal activity.
For instance, when it comes to fraud detection, the number of fraudulent transactions made using stolen data is relatively low compared to the number of legitimate daily consumer transactions. Those represent a solid baseline against which fraudulent activity can be measured and identified. If a user’s behavior varies from this baseline, an AI system can flag the behavior for investigation.
In AML detection, fintech AI enables organizations to move away from rules-based approaches to ones based on behaviors. Using historic transaction data, firms can build rich profiles of individuals and their peers. Real-time, automated transaction monitoring can then be employed to identify any anomalies from these profiles and alert a team to investigate.
Our AI fraud solution
At Featurespace, we empower financial services organizations with the most powerful technical capabilities for fraud detection and anti-money laundering. Our approach centers on the Featurespace ARIC™ Risk Hub, which monitors real-time customer data, using our proprietary machine learning inventions, Adaptive Behavioral Analytics, and Automated Deep Behavioral Networks.
ARIC Risk Hub provides a range of solutions for fraud and AML analysts to identify suspicious activity and prioritize alerts with explainable anomaly detection. Concurrently, ARIC Risk Hub recognizes genuine customers without blocking their activity, thereby reducing friction within the customer experience.
For retail banks, ARIC Risk Hub provides a clear picture of financial crime, including money laundering, with adaptive machine learning. For instance, using ARIC Risk Hub, one major global bank has significantly improved its AML transaction monitoring, identifying 109 percent more high-risk cases while reducing the overall case load by 30 percent. This is a great example of how AI in fintech can significantly improve outcomes for financial services organizations while driving breakthrough efficiencies.