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The Unlimited Potential of Artificial Intelligence to Drive Banking Toward Infinite Adaptation
Published by Fintech Americas on May 23, 2023
This article explores how banks can use Artificial Intelligence and highlights success stories from Latin America and around the world.
The adoption of Artificial Intelligence (AI) is accelerating rapidly, increasingly becoming a “general-purpose technology” with a major influence on long-term progress. In fact, its disruptive impact is expected to surpass even that of the Internet over the past two decades.
AI was first conceptualized in 1955 as a branch of Computer Science focused on creating “intelligent machines” capable of imitating human cognitive abilities such as learning and problem-solving. What differentiates AI from traditional software is its ability to improve its own processes autonomously — commonly referred to as machine learning.
Over the past decades, AI applications within the financial industry have had a significant impact worldwide, including across Latin America, helping banks improve efficiency, security, customer experience, and cost reduction. According to McKinsey & Company, AI could potentially unlock $1 trillion in incremental annual value for banks.
A survey conducted by the Economist Intelligence Unit found that 77% of bankers believe the ability to unlock AI’s value will determine whether banks succeed or fail. AI encompasses a broad range of technologies, including natural language processing and robotics. Financial institutions can use these technologies to create a new generation of personalized products and services tailored to the evolving needs of individuals and businesses.
Below, we explore how banks can apply Artificial Intelligence and review several successful use cases from Latin America and around the world.
Applications and Uses of Artificial Intelligence in the Financial Industry
Process Automation
AI is being widely used in banking to automate processes such as document review and customer identity verification. This improves efficiency, reduces wait times and operational costs, and frees employees to focus on more complex tasks. AI can also automate routine functions such as balance inquiries and password resets, enabling faster and more accurate customer support.
Fraud and Crime Prevention
Artificial Intelligence is extremely effective at improving fraud detection efficiency. AI-based systems can analyze vast amounts of data in real time and identify suspicious patterns that may indicate fraudulent activity.
AI can flag unusual transactions — such as payments initiated by unexpected customers, transactions involving unusually high amounts, or payments sent to countries with which the company has never previously done business. It can learn to classify transactions correctly and alert teams only when there is a genuine threat of criminal activity.
In addition, AI enables banks to detect and monitor unusual employee behavior, such as logging into systems outside normal working hours.
Enhancing Customer Experience
AI capabilities are ideal for increasing customer satisfaction and personalizing products and services for each individual.
Chatbots, for example, have become increasingly popular in banking and are being used to provide customer support 24 hours a day, 7 days a week. They offer a high return on investment through cost savings, making them one of the most widely adopted AI applications in banking. Chatbots can efficiently handle common tasks such as checking account balances, accessing statements, processing transfers, and more.
AI algorithms can also provide customers with personalized financial advice, tailored product and investment recommendations, and predictive insights into future financial needs.
Additionally, analyzing each customer’s spending patterns can reduce delinquency rates by warning users when they may not have enough funds to meet future obligations. This allows banks to provide personalized support while helping customers avoid missed payments.
Strengthening Cybersecurity
AI can detect and prevent data theft and cyberattacks in real time by automatically blocking suspicious transactions and monitoring access patterns to confidential information.
AI can also identify customer spending habits and automatically respond to anomalies or suspicious transactions, such as cross-checking geolocation data to verify user locations and prevent crimes like identity theft.
Better-Informed Decision-Making
AI-based systems can analyze massive volumes of data in real time, enabling financial institutions to identify patterns and trends that would be impossible to detect manually, significantly improving risk management.
For example, algorithms can analyze transaction histories and identify early warning signs of future problems. This makes AI an essential tool for making smarter, data-driven decisions.
Predicting Credit Risk
AI can also be used in banking to predict customer credit risk and support more accurate lending decisions, ultimately reducing default rates. This is achieved through automation based on factors such as age, income, expenses, average balances, and debt levels.
As a result, banks can provide safer lending products while reducing the time and resources required for credit evaluations.
This technology can also help alternative lenders assess customer solvency by analyzing both traditional and non-traditional data sources. This enables innovative lending systems backed by strong credit scoring models, even for customers with limited credit histories.
Real-World AI Use Cases in Banking
Banorte
Mexican bank Banorte integrated Maya, an Artificial Intelligence assistant, into its mobile banking platform. Maya is capable of performing up to 17 banking operations through conversational interactions with customers.
Maya can handle more than 300 chat-based inquiries and execute various transactions, including transfers, tax payments, service payments, card management, mobile token activation, and more.
In 2021, Maya was recognized by Accenture and EFMA as one of the nine best banking innovations in the world in the AI Analytics category.
Bradesco
In Brazil, traditional banks are following fintech startups in simplifying access to financial services. Bradesco is helping customers manage account balances through BIA, an AI assistant powered by IBM Watson and integrated with WhatsApp.
Customers can check balances, locate branches, and access various services using hashtags through the Bradesco app or WhatsApp. The bank plans to gradually expand the service to more than 7 million users.
Bantrab
Guatemalan bank Bantrab took a major step toward customer personalization through data utilization. This was achieved using Azure Data Lake, Microsoft’s analytics service that supports more accurate decision-making through micro-segmented information.
By combining these insights with Artificial Intelligence, Bantrab began offering products tailored to each customer’s lifecycle and individual circumstances. This allowed the bank to attract new customers and increase average disbursement amounts by 25%.
That same year, Bantrab received a Fintech Americas Financial Innovators Award in the category of Big Data, Analytics, and Artificial Intelligence.
HSBC
HSBC partnered with AI startup Ayasdi to combat fraudulent activities such as money laundering. Automating transaction data analysis improved efficiency and reduced costs by eliminating false positives in fraud detection.
As a result, the number of investigations decreased by 20% without reducing the number of cases escalated for further scrutiny.
ING
In 2018, ING launched a new tool called Katana, which uses Artificial Intelligence to help bond traders make faster and more accurate pricing decisions.
Katana learns from the history of hundreds of thousands of transactions and provides pricing predictions or recommendations for bond purchases and sales.
Initial testing results showed:
- a 25% reduction in trading costs;
- four times greater frequency of offering customers the best available price;
- faster decisions in 90% of transactions.
J.P. Morgan
J.P. Morgan implemented software called COIN (Contract Intelligence), which automates document review for specific types of contracts.
COIN uses image recognition and machine learning to identify patterns and categorize repetitive clauses.
The software has reviewed thousands of contracts in seconds — work that previously required more than 360,000 hours of legal review. In addition to reducing costs, the program has proven to be more accurate than humans at reviewing these documents.
The possibilities for Artificial Intelligence in banking are virtually limitless, and the industry has only begun to uncover a fraction of what this technology can achieve. As new AI-driven opportunities for improving efficiency continue to emerge — including technologies like ChatGPT — it is essential for financial institutions to remain open to the waves of transformation ahead.
Artificial Intelligence has shaken the foundations of the financial industry in a very short period of time, yet the pace of change continues to accelerate exponentially. As a result, adaptation has become the new paradigm replacing the traditional concept of digital transformation: a continuous evolution with no beginning and no end.
Banking professionals may find it challenging to constantly stay ahead of emerging technologies in order to ensure long-term business survival and success. That is why conferences like are so valuable — bringing together hundreds of industry leaders to exchange knowledge while experts help participants remain at the forefront of innovation.
Recently, brought together 1,000 banking and financial services leaders from Latin America and around the world, with Artificial Intelligence serving as one of the event’s central themes.
The official conference report is now available and includes key moments, insights, discoveries shared during presentations, attendee testimonials, and much more.
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