Generative artificial intelligence (AI) is starting to transform industries, and the banking sector is no exception. Financial institutions are exploring ways to adopt generative AI to enhance customer experiences, combat fraud, and streamline complex, time-consuming processes. Unlike traditional AI, generative AI has the ability to generate new content and solutions based on training data, opening up exciting opportunities for innovation.
This groundbreaking technology has the potential to significantly reduce costs, boost efficiency, and redefine customer service in banking. However, the use of gen AI in banking processes also comes with risks, such as generating inaccurate information or compromising sensitive customer data. Below, we’ll explore its key use cases, benefits, and potential challenges in the banking industry.
Key Points
• Chatbots driven by generative AI can provide personalized customer service, reducing the need for in-person visits or phone calls.
• Generative AI may enhance fraud detection by analyzing large datasets to identify unusual activities, improving security measures.
• Generative AI can automate document processing, reducing manual effort and time, and freeing staff for more valuable tasks.
• Predictive analytics powered by generative AI may improve financial forecasting and can aid banks with strategic decision-making and portfolio optimization.
• Enhanced credit scoring and loan underwriting through AI may speed up credit decisions and can reduce bias in lending.
Personalized Customer Service and Chatbots
One of the best potential use cases of generative AI in banking is in customer service, particularly 24/7 chatbots. Chatbots are nothing new, of course, but chatbots driven by generative AI are able to provide more specific, actionable insights for customers so that they don’t need to visit a branch in person or spend valuable time on the phone trying to resolve issues.
Intelligent chatbots (also known as virtual assistants) are capable of going beyond the prior generation of scripted chatbots, which are more limited in what they can help customers with before pulling in true customer service agents. Generative AI can be trained on vast quantities of data and resources, allowing it to understand and generate natural-language text while taking context into account.
However, there is also the potential that a gen AI chatbot could share inaccurate information with a customer. As a result, many financial institutions are taking a cautious approach, initially implementing AI chatbots in non-customer-facing interactions or to help customer-facing employees offer insights and support to customers.
Fraud Detection and Risk Management
Another potential use of generative AI is to improve banks’ fraud detection and prevention strategies, and better protect customer bank accounts. The technology has the ability to analyze massive data sets and thus more easily detect when something is out of the norm, which could indicate fraudulent activity. By nature, generative AI becomes more accurate over time. Thus, the more data the system is fed, the more often it can help fraud departments catch (and stop) bank fraud, including account takeover and even money laundering.
Gen AI could also be used to simulate potential cyber-attacks, which can further enhance fraud detection algorithms. This proactive approach could help improve online banking security and significantly reduce a bank’s risk of loss.
On the flipside, use of gen AI in banking also introduces new risks to data privacy and cybersecurity (further explored below), which need to be effectively evaluated and managed before banks fully embrace AI as a tool to manage security threats.
Automated Document Processing and Analysis
Banks deal with an enormous amount of paperwork, from compliance forms to contracts and legal documents. Generative AI can help automate document processing by analyzing text, extracting relevant data, and categorizing information, significantly reducing time and manual effort. For example, gen AI can quickly summarize regulatory reports, prepare drafts of pitch books, and generate financial reports. Similarly, gen AI can reduce the need for human data entry, freeing up staff to tackle higher value-added tasks.
At present, however, using gen AI in this way also introduces some risks. For example, the AI could potentially misunderstand or misinterpret important information and lead to errors in decision-making. In addition, AI systems may not always follow all the applicable laws and regulations when handling documents. As a result, many financial institutions are exploring use of AI as an aid to employees engaged in document processing and analysis.
Predictive Analytics for Financial Forecasting
Banks rely heavily on accurate forecasting to make strategic decisions, manage risk, and optimize their portfolios. Generative AI has the potential to enhance traditional predictive analytics by processing massive datasets, identifying patterns in customer behavior, and forecasting financial trends with higher accuracy.
In investment banking, for example, generative AI has the power to analyze historical data to predict stock performance or project economic trends. Retail banks could potentially use gen AI to anticipate customer needs, such as identifying a change in borrowing behavior or a growth in demand for high-yield savings accounts. In the coming years, these predictive capabilities could help banks to make more informed decisions and better tailor their products and services to customer needs.
Enhanced Credit Scoring and Loan Underwriting
Traditional loan underwriting requires a thorough review of applicants, which can take time, but AI is capable of taking mountains of data about an applicant and making a credit decision quickly, possibly in as little as 30 to 60 seconds. Gen AI can also assess a broader range of data, including non-traditional data sources like utility payments, employment history, and social data, to produce a more comprehensive view of a borrower’s financial health. This could help banks make wiser, less risky decisions when reviewing credit card and loan applications.
In addition, gen AI has the potential to help reduce bias in decision-making by providing a data-driven, objective assessment rather than relying on traditional methods that might overlook certain individuals. This could potentially open more opportunities for lending to underserved markets. On the flip side, however, generative AI models trained on biased data may also perpetuate and reinforce existing social biases (more on that below).
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Potential Risks of Using Generative AI in Banking
While generative AI offers numerous benefits, it also presents challenges and risks that must be addressed to ensure responsible and secure use of this technology. Here are some concerns to keep in mind.
• Hallucinations and inaccuracies: Generative AI systems, especially large language models, may generate responses that are inaccurate or completely fabricated, known as “hallucinations.” In banking, where accuracy is critical, this can lead to misinformation and potentially damaging consequences, such as incorrect financial advice or inaccurate loan terms.
• Regulation and compliance issues: Generative AI is still largely unregulated. As rules and regulations evolve to address generative AI, banks will need to ensure their AI systems comply with standards on data privacy, fairness, and transparency. Failure to comply with regulations could result in legal repercussions and significant fines.
• Security: There are concerns about AI accessing sensitive customer information, particularly personally identifiable information (PII), which could violate customer privacy. In addition, widespread use of AI in banking could make it a potential target for cyber-attacks, where hackers may attempt to manipulate or deceive the AI systems.
• Existing technology: Established banks may have a wealth of legacy technology systems that might not work with generative AI. Replacing this old tech can be costly and take some time.
• Bias and fairness: AI models can unintentionally inherit biases present in the training data, leading to unfair treatment of certain customer demographics. If not managed properly, this bias could result in discriminatory practices, such as unfair loan rejections or inappropriate credit scoring. This could impact the bank’s reputation and compliance with regulatory standards.
The Takeaway
Generative AI has the power to transform the banking industry — and the world as a whole. As a fast-growing and ever-evolving tool, it’s important that gen AI advancements are balanced with risk mitigation to ensure the technology meets regulatory requirements, doesn’t violate customer privacy or rights, and works with the existing tech stack. That said, when used effectively — to make processes more efficient, to make better decisions, and to help customers resolve issues more easily — it has the potential to benefit both banks and their customers.
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FAQ
Can generative AI help improve banking security?
Generative artificial intelligence (AI) may enhance banking security primarily through fraud detection. By analyzing large volumes of data, generative AI can easily spot transactions that seem out of the ordinary and flag them as fraud in real time. This allows banks and consumers to react immediately and, ideally, prevent the fraudulent transaction. AI systems can also simulate potential cyber-attacks and, in turn, learn how to anticipate and respond to these threats. This proactive approach can help banks better protect customer accounts and improve overall security in banking transactions.
Can generative AI help with financial advice?
Yes, generative artificial intelligence (AI) can enable chatbots and virtual assistants to provide people with personalized financial advice. By analyzing individual customer data, including spending habits and financial history, AI-powered tools can offer customized advice about how to spend, save, invest, and tackle debt. Generative AI can also simulate market trends and forecast potential financial outcomes, offering data-driven insights to customers.
While these tools are helpful, it’s essential for consumers to verify AI-provided advice with financial professionals, as gen AI may produce inaccurate or overly generic recommendations without a nuanced understanding of goals and risks.
What are the ethical concerns of using AI in banking?
There are several ethical concerns surrounding the use of artificial intelligence (AI) in banking. For one, AI systems require vast customer data, creating the potential risks of misuse or unauthorized data exposure. Another concern is that use of AI in lending decisions could result in some bias against particular groups of people, due to biases in AI’s training data or insufficient data. Transparency is another ethical challenge, as use of AI tools by banks can make it difficult for customers to understand decision-making processes.
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