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by Art Malkovich, Onilab
Artificial intelligence is disrupting various industries and transforming how businesses handle tasks and solve problems. In the payments sector, AI’s role revolves around automating processes, detecting fraud, assessing risks, and improving customer service. Leading credit card network Visa and digital transaction platform Block are adopting AI to cut costs and personalize the customer experience.
While AI’s benefits are impressive, there are still concerns about its implementation regarding data security, privacy, and limited skill sets among payment teams. In this article, we’ll weigh the pros and cons of this cutting-edge technology, describe its major use cases in the payments industry, and analyze the key components that make AI so effective.
Technologies Supporting AI in Payments
AI-driven tools have become indispensable assistants in various industries, streamlining processes and boosting productivity across the board. With GitHub Copilot, developers save time writing and reviewing code as the tool generates suggestions and completes lines based on the context.
Another example is Salesforce CPQ’s AI-powered features, which automate the arduous configure-price-quote tasks, allowing sales teams to close deals faster. Enlitic helps radiologists interpret medical images (X-rays, MRIs, CT scans, etc.) and spot abnormalities more quickly.
AI has become the driver of innovation in the payments sector because of the powerful technologies and advanced algorithms it leverages. Let’s explore each of its building blocks in detail and analyze how they boost the efficiency and security of financial transactions.
Machine learning (ML)
ML algorithms are trained on large amounts of data (transaction history, user behavior, credit scores, etc.) to uncover patterns that may indicate fraud. They constantly learn and adapt based on new information, becoming more accurate in recognizing suspicious activities. For example, ML technologies can flag a user’s transaction for review if it deviates from their typical spending behavior (the amount is too high or the vendor is unfamiliar).
Natural language processing (NLP)
NLP technologies power chatbots and virtual assistants, allowing them to understand, interpret, and generate context-based responses to basic user queries without human intervention. Besides, NLP algorithms can decipher customer sentiment, assist in language translation, and extract insights from unstructured text data (social media posts, reviews, and so on). These capabilities turn NLP into a valuable tool for streamlining communication and personalizing customer service in the payments industry.
Neural networks
These smart models imitate human brain functioning and are widely applied in AI payment systems to improve security features like biometric data processing (facial recognition, voice authentication, fingerprint scanning, etc.) when verifying users and advanced fraud detection (recognizing subtle anomalies to prevent unauthorized transactions).
Predictive analytics
Powered by statistical and ML algorithms, predictive analytics models enable payment systems to anticipate customer needs and identify potential fraud. By analyzing customers’ purchase histories and behavior patterns, they can suggest preferred payment methods at checkout.
Additionally, predictive analytics examines past transaction anomalies and learns to detect suspicious patterns before they occur. With such a proactive approach, businesses can reduce potential losses and increase payment system security.
Top Use Cases of AI in the Payments Industry
AI’s ability to process large volumes of data, automate tasks, and streamline processes can make a difference across multiple areas in the payment sector. Let’s look closer at AI’s primary application scenarios and how they contribute to fast, secure, and frictionless transactions.
Fraud Detection
PwC’s 2022 research reveals that a whopping 51% of organizations suffered from fraud from 2020 to 2022. The diversity of malicious activities makes it impossible for humans to stay vigilant night and day. That’s where AI comes to the rescue. The smart models sift through data arrays and recognize anomalies based on previously analyzed patterns much faster, saving financial companies lots of time and resources.
McKinsey suggests that AI-based fraud prevention systems might reduce fraud losses by 60%. Moreover, AI tools prove useful in decreasing false declines as they can apply extra verification for activities marked as potentially suspicious.
Risk Assessment
AI predictive modeling utilizes machine learning algorithms to process data and forecast outcomes. For example, AI can more accurately evaluate an individual’s credit risk, taking into account various data points. As a result, banks can make informed lending decisions, minimize potential losses, and maximize profits.
Case in point: One of the world’s leading credit card providers, American Express, often dubbed Amex, adopted machine learning to optimize its credit risk assessment. Amex confirmed that it reduced the cycle time from 30 days to just a few minutes and improved its risk models by 20-30%.
Reasons for Integrating AI in Payments
Implementing artificial intelligence in payment processing benefits businesses and consumers alike. Let’s list the rewards this cutting-edge technology offers.
Enhanced security
Secure transactions are one of the biggest challenges in the payments industry. AI tools greatly assist in resolving this issue. They can recognize unusual patterns or suspicious behavior and generate alerts to prevent fraud.
In addition, AI systems can bolster security measures by implementing multifactor authentication or biometric scanning in case of malicious activity. These extra steps ensure that only authorized users can access sensitive information.
Improved efficiency
Due to AI technology, fast and seamless digital payments have become the norm. With AI tools, financial organizations can automate numerous repetitive tasks, like data entry and verification, so human agents can concentrate on more complex and strategic activities.
Based on historical transaction data, AI-powered solutions can also anticipate the volume of payments and potential fraud risks, enabling businesses to optimize resources and minimize disruptions and downtime.
Personalized recommendations
Personalization is at the core of AI-driven digital payment experiences. AI algorithms tap into customer transaction details, purchase preferences, and spending patterns to offer relevant products or services. For example, if a consumer spends a lot on traveling, AI can suggest a credit card that gives extra reward points for travel-related expenses.
Data-informed decision-making
AI thrives on data. The more information you feed it, the more accurate insights into market trends and customer behavior it provides. Harnessing AI’s power enables businesses to spot areas for improvement and stay competitive in the changing payments industry.
Challenges AI Poses to the Payments Sector
Despite the apparent benefits of AI in payments, there are some risks companies should consider when adopting this technology. The main hurdles to fully embracing AI-powered tools include:
Data privacy concerns
As mentioned earlier, AI algorithms rely heavily on data to function properly. This can pose serious privacy risks in payments as AI models may access customers’ sensitive information, such as credit card and bank account details, personal info, and transaction data.
With this in view, financial organizations must be transparent about gathering, storing, and utilizing information. Additionally, they must comply with data privacy laws and regulations, such as GDPR (General Data Protection Regulation) and the PCI DSS (Payment Card Industry Data Security Standard), to foster trust with clients and avoid legal consequences.
GPDR protects personal details, like a customer’s name, email, and billing address. This regulation applies to businesses that process information from EEA (European Economic Area) residents. Meanwhile, the PCI DSS extends to all organizations that handle payment card data.
Security risks
Around 47% of financial institutions leverage AI to combat fraud. While efficiently identifying malicious activities, AI-powered tools can also be used for harmful purposes. Cybercriminals may deploy AI technology to discover software flaws and enhance their attacks. Therefore, businesses must keep tabs on the latest AI developments and adapt their security measures.
Insufficient skill sets
AI is a valuable tool for supporting the payments crew rather than replacing them entirely. Human agents must understand AI’s capabilities clearly to ensure it works properly and ethically. Meanwhile, some payment companies may lack the knowledge to efficiently manage AI solutions. Therefore, investing in training programs or partnering with third-party experts would be wise.
The Future of AI in Payments
Navigating the digital payments space is getting more complicated because of evolving technologies, new regulations, and growing security concerns. While artificial intelligence is making waves in the payments industry, businesses should consider its strengths and weaknesses.
When adopting AI solutions, aiming for a balanced approach is essential. Artificial intelligence is a tool designed to improve human capabilities and streamline decision-making processes. Thoughtful integration of AI into their workflows ensures financial companies create a secure and efficient payment environment.