Investing in artificial intelligence (AI) is no longer a choice but an imperative for financial services organizations. AI’s unparalleled potential to revolutionize the industry mandates its adoption. From enhancing customer experiences with personalized services and sophisticated chatbots to improving fraud detection and cybersecurity through real-time monitoring and adaptive algorithms, AI is transforming every facet of the financial sector. It promises better risk management with predictive analytics and automated compliance, heightened operational efficiency through intelligent automation, and revolutionary changes in investment and trading. Furthermore, AI fosters financial inclusion, drives innovation, improves data management, and emphasizes ethical practices. Embracing AI is essential for financial institutions aiming to stay competitive, compliant, and secure in an increasingly digital marketplace.
Trends and Innovations in AI
The financial industry generates vast amounts of data internally and externally to drive critical decision-making and unlock new analytical insights and opportunities. Research indicates the average consumer makes at least two daily transactions, with an estimated 724 billion credit card transactions worldwide in 2023. While cloud computing can help process and store increased information, AI’s most impressive capability in the sector is enhancing operational efficiency to free up professionals’ time for more strategic initiatives.
Recent studies show that the overall effects of AI boost worker productivity by up to 40 percent and reduce costs by approximately 30 percent through automation and advanced data analytics. AI can quickly analyze vast amounts of historical and real-time data for faster, more accurate decision-making based on customer behavior and market trends while performing predictive maintenance to reduce downtime.
Proper utilization of AI also offers an enhanced customer experience via:
- Chatbots: Rather than employees working around the clock, chatbots are available 24/7. They interact with customer queries and complaints in natural conversational language to automate customer support and provide customers with a more efficient means of interacting with the institution.
- Personalized marketing efforts: Based on an analysis of behaviors, investments, and account status, the institution can offer credit cards or other tailored recommendations geared toward travel, dining out, or activities preferred by specific customers.
- Fraud detection: Financial institutions incur over four dollars in legal and other assorted costs for every dollar lost to fraud. With proper utilization of AI, banks and other institutions can detect suspicious behavior or identify abnormal patterns in real time, allowing the institution to proactively prevent fraud rather than reacting after the breach. For customers, this means added security and peace of mind.
- Risk management: AI-driven credit scoring raises accuracy while reducing the time necessary for approvals or denials. It also performs stress testing by building complex models that analyze whether an organization is particularly vulnerable in specific areas while evaluating balance sheer resistance and continuously adapts and analyzes feedback to improve services.
Real-World Examples
Leading financial institutions have established AI models to assist in various business areas. In 2018, Bank of America launched Erica, its virtual financial assistant. Just six years later, Erica surpassed two billion client interactions. Among other tasks, the virtual assistant keeps clients informed about deposits, assists with money transfers, and even sends birthday wishes to clients.
JPMorgan Chase’s COIN, short for contract intelligence, can analyze legal documents pertaining to different attributes of credit contracts by learning from the bank’s contracts. Reviews and analyses of these documents once required 360,000 hours annually. Today, COIN can digest each document’s information in seconds.
Mastercard’s new AI fraud detection technology scans transaction data across cards and merchants at highly efficient rates, alerting Mastercard to new, sophisticated fraud patterns. The technology doubled the detection of compromised cards while reducing false positives and identifying at-risk merchants.
Capital One’s Eno is a virtual assistant that combines several features of other AI models. While Eno’s initial and primary use was to answer routine account questions, it also provides fraud detection, alerting customers to suspicious activity. Perhaps its most interesting feature is the ability to generate a virtual card number for online shopping. Customers with security concerns can use Eno to generate a secure card number while shopping online, keeping their authentic card number safe and secure.
HSBC developed an AI-equipped anti-money laundering tool (AML AI) to detect suspicious patterns in current and historical data that resemble money laundering activities. Because the tool reduced the number of alerts by 60 percent and identifies suspicious activity, HSBC’s investigation teams can “spend more time reviewing genuinely suspicious cases, resulting in twice as much identified financial crime in our commercial banking operations and almost four times as much across our retail banking.” These examples demonstrate the diversity of uses for AI models in the financial world. Many institutions plan to expand their offerings, particularly in cybersecurity.
Potential Pratfalls of AI
For all the advantages organizations realize with proper utilization of AI, lacking a clear vision or strategy for implementation can serve as a stumbling block. This can lead to frustration and incomplete development of the AI model or its capabilities. A recent report from Deloitte stated that while 94 percent of executives believe AI will transform their industries in the next five years, almost three-quarters of companies aren’t fully capitalizing on their respective AI implementations. Potential trouble spots include:
- Insufficient data quality: Without proper data, an organization can create an inaccurate model. In financial services, this is particularly harmful when evaluating customers’ creditworthiness.
- Data governance: Data governance refers to the practices an organization uses to ensure proper customer data usage. The risks are evident within a financial institution. Insufficient security or privacy has dire consequences in an industry where fraud and identity theft are two of customers’ biggest fears.
- Hiring the right talent: Continuous learning is an emphasis for professionals, and likewise will be so for AI models. With the right data engineers and talent, AI models will remain current and relevant, but without the proper people to filter and teach the model relevant information, an institution can quickly find itself burdened by an outdated model.
AI can positively impact every aspect of financial services, from customer service and retention to risk management, cybersecurity, and informing customer decisions. Each financial institution is responsible for investing in AI to be better positioned for this sector’s future. Organizations differ based on their size, customer base, and risk profile, but the time has arrived to cease viewing AI as a luxury and recognize it as a mandatory element of the organization’s future.
About the Authors
Dhanveer Singh is senior manager, software engineering, at a leading bank holding company. Dhanveer has more than 16 years of expertise as a technology leader, building products and platforms from the ground up, and architecting and delivering highly scalable, reliable, and secure systems on public cloud platforms like AWS. Proficient in complex architectural patterns, APIs, microservices, and high-throughput systems, he collaborates on strategic initiatives, balancing technology, product, and design to impact customer lives. Connect with Dhanveer on LinkedIn.
Neeharika Meka is a software development manager at an industry-leading cloud service provider. She has more than 16 years of global IT industry expertise in leading high-performing engineering teams to deliver scalable, secure, and innovative software solutions. Neeharika has expertise in full-stack development, cloud computing, generative AI (Gen AI), and Agile methodologies and in building cost-effective enterprise-scale applications using modern architecture patterns (serverless, microservices, event-driven). Neeharika has a proven ability to formulate a partnership vision, strategy, and technical standards. Connect with Neeharika on LinkedIn.
Disclaimer: The author is completely responsible for the content of this article. The opinions expressed are their own and do not represent IEEE’s position nor that of the Computer Society nor its Leadership.
