The Quantum Team, October 5th 2024
Artificial intelligence (AI) is an umbrella term that describes machines simulating the capabilities of human intelligence. AI has been part of computer science for decades but, in the last 10 years it has made significant strides.
What is AI in payments?
AI is important to the finance world due to its capabilities in understanding complex mathematical relationships and its inherent disposition to handle complex situations – thus the inference to simulation of human intelligence.
AI Technologies Transforming Payments
AI weaves sophisticated components or sub-fields into the finance world bringing enhanced security, automation and customizing customer experience; predictive analytics, machine learning (ML), natural language processing (NLP), biometric authentication, generative AI, blockchain technology and robotic process automation (RPA) are combined to deliver tailored customer experiences from fraud detection and predictive analysis to real-time risk assessment, thus transforming the way we transact and how we conduct business.
Predictive Analytics
Today, predictive analytics is used extensively in the payments industry to anticipate and address future challenges more effectively. As a result, companies can make more informed decisions and implement proactive measures.
Since the 80s AI technologies, such as ML, were used extensively in the banking industry and financial institutions to reduce the risks of human mistakes and, as in the case of the renowned hedge-fund company, Renaissance Technologies, a petabyte data warehouse was developed to analyze statistical probabilities for the trend in securities’ prices in any market. This represented significant milestone for AI in the finance world.
Machine Learning
Machine learning algorithms analyze and learn from large datasets to detect patterns and make predictions. In the case of fraud detection, ML use AI algorithms developed using historical data to identify suspicious activity. This permits the development of risk rules that prevent or allow activities based on historical patterns such as purchases, logins and identity verification. Fintech companies and insurers use ML to avoid fines – imposed by Regulators – especially during high volume processing while trying to stay competitive. ML helps distinguish legitimate users from fraudsters thus preventing fraudulent profiles from succeeding.
Natural Language Processing
NLP is transforming the financial services industry by enhancing customer support 24/7 and streamlining regulatory compliance. NLP powered chatbots, also known as AI agents, allow people to communicate with computers in a natural and human-like way, mimicking person-to-person conversations. The AI behind NLP bots is advanced and powerful; they understand, analyse and respond to the human language. In addition to engaging customers, NLP systems can interpret the complexity and subtleties of financial language with greater precision thus being able to navigate complex legal documents and providing financial institutions with the ability to match regulations with policies while extending this to customer support when required or facilitating compliance.
Biometric Authentication
Traditional identity verification methods such as PINs, passwords, and security questions, are vulnerable to cyber-attacks and hacking. Phone banking has already implemented advanced voice biometric technology to verify your identity with your unique voice. Facial recognition, voice recognition, finger/palm print and behavioural biometrics – how users physically interact with their devices such as typing patterns, swipe gestures, pressure applied, and more, which form unique user profiles.
Generative AI
Generative AI technology has already generated huge interest in the financial services sector. It refers to the use of artificial intelligence techniques, particularly generative models, to solve problems and enhance processes within the financial services industry. The efforts of a team of financial advisors, data scientists and financial analysts working for many hours to analyze trends, predict prices and develop strategies can be reduced to a fraction of the time using Generative AI. Financial decisions are driven by algorithms that traverse huge amounts of data that would otherwise be invisible to the human eye.
Blockchain Technology
Artificial Intelligence and blockchain technology integration with the financial industry is a powerful merger poised to radically reshape established financial institutions. Blockchain technology provides a robust framework which enables traceable, auditable, and tamper-proof financial transactions combined with AI’s predictive analytics, machine learning, and natural language processing capabilities will enable financial institutions to streamline operations, reduce costs, mitigate risks and deliver accuracy and security in financial transactions which is one of the many challenges in the finance industry today.
AI can add a layer of conversational banking to financial services, allowing users to initiate transactions in over 200 languages. Meanwhile, blockchain ensures that these transactions are securely stored in a decentralized finance structure. With this setup, electronic money issuers can seamlessly integrate cryptocurrency wallets, facilitating blockchain-based transactions on their platforms.
Robotic Process Automation
Robotic Process Automation (RPA) relies on bots to emulate human tasks, specifically in labor-intensive areas of a business’s process – accounts payables and receivables, asset management, others; the robots perform the same work that humans would, using the same interface and following similar steps. RPA introduces efficiency, cost savings, reduced (human) errors, speed, and 24/7 availability.
Will AI become the bedrock of finance in the future? It is predicted that an AI dominated future will radically change some industries, but not all; that it will benefit the larger players and make individual players smarter but, there is the risk that it may make the world dumber.