6 Ways to Use Generative AI in Fintech

Fintech, or financial technology, is the innovative use of technology to improve and streamline financial services. Examples range from simple mobile banking access to complex blockchain integrations. But even for an industry predicated on tech, generative AI in fintech represents a transformational development.

Fintech companies are racing to adopt generative AI, as 77% of financial services leaders surveyed by EY believe generative AI will positively influence the industry over the next five to 10 years. But there are also barriers to implementation: 40% say their firm lacks the proper data infrastructure, and 44% cite a lack of skilled resources.

Capitalizing on generative AI requires knowing where, when, and how you’ll use it. Let’s look at some of the benefits of genAI for fintech companies, as well as use cases companies can apply today.

5 Benefits of Generative AI for Fintech 

Fintech is already an industry dominated by technology and data. But generative AI can unlock new ways of collecting, analyzing, and acting on that data. Here are some reasons why fintech companies should consider this technology.

Revealing Customer Behavior and Market Trends

Generative AI can help companies explore beneath the surface by revealing previously unknown behavioral patterns and other trends. Customer data — including transaction history, browsing patterns, and demographic information — can be a treasure trove of insights. Applying generative AI can uncover customer habits, desires, and needs.

Likewise, companies can use generative AI to analyze market data, including news articles and financial reports. Analyzing market trends can quickly reveal insights that inform proactive decision-making and the customer experience.

Improving Efficiency and Accuracy

GenAI doesn't just unlock hidden insights; it can also improve operational efficiency and accuracy. By automating processes such as fraud detection, risk assessment, and compliance monitoring, generative AI algorithms significantly reduce manual intervention and human error. This automation frees up company resources and allows people to focus on strategic initiatives. 

For instance, fintech companies can use generative AI to analyze transaction data in real time and identify patterns or anomalies that may indicate fraudulent activity. This proactive approach helps mitigate risks, protect customers, and avoid financial losses.

Scaling Personalization 

Personalization has become a cornerstone of customer experience, and fintech is no exception. Generative AI generates granular insights, helping fintech companies provide targeted customer recommendations that align with pain points or objectives.

For example, personalized recommendations for investments, savings, and financial planning can be provided based on an individual’s goals, risk appetite, and financial data. Fintech companies can also deliver targeted notifications and offers using generative AI.

Mitigating Risks 

Generative AI-powered tools can continuously monitor transactions, detect suspicious activities, and flag potential compliance breaches, helping fintech companies mitigate risks and avoid costly penalties. This proactive approach identifies potential risks before they escalate, protecting companies and their customers.

Generative AI can also improve compliance efforts, such as monitoring for suspicious activities that may violate anti-money laundering regulations. 

Driving Innovation and Competitive Advantage

Companies that figure out how best to apply generative AI will create competitive advantage through automation, novel financial solutions, and new business models. 

Consider this example: A fintech company creates an AI-powered lending and budgeting platform that uses algorithms to assess a loan applicant’s creditworthiness. The platform can then provide customized loan recommendations for lenders and personalized budgeting tips for individuals. This level of innovation improves the customer experience and offers more flexibility than one-size-fits-all models that create friction and don’t necessarily reduce risk.

6 Generative AI Fintech Use Cases 

Generative AI is a relatively new and fast-developing field. While this limitless potential is exciting, companies need to find use cases that make sense for the business and generate a return on investment. Here are six ways businesses can use generative AI in fintech.

Intelligent Document Processing

Intelligent document processing (IDP) involves using machine learning techniques, AWS services like Textract and Comprehend, data analytics, and large language models to extract and analyze valuable information from large volumes of documents. This highly scalable process is faster and more accurate than relying on human writers or readers. 

Fintech companies can use IDP to streamline loan applications, mortgage underwriting, and compliance documentation. Traditionally, loan applications have involved manual reviewing of income statements, tax returns, bank statements, and other documents. This approach is time-consuming, error-prone, and often leads to delays in loan approvals. 

With IDP, key information such as income verification, credit history, and debt-to-income ratios can be automatically extracted from documents. This speeds up the application process and enables loan officers to make informed decisions more quickly. 

Summarizing Information

Businesses have access to more data and information than ever before, which complicates their ability to quickly and accurately review it. Generative AI tools can scan and summarize that information faster than a human while presenting key insights in a concise format. This gives executives, employees, and other stakeholders the ability to quickly get up to speed and use the insights to make informed decisions. 

Generative AI uses natural language processing to let people ask follow-up questions, including about companies, sectors, investment opportunities, and trends and correlations. Models can adapt to the needs and preferences of investment professionals. Over time, they learn and refine their summarization capabilities, providing increasingly tailored insights that align with company and investing strategies.   

Evaluating Investment Opportunitites

Generative AI in fintech can summarize many kinds of information, including investment opportunities. This technology can be especially helpful when much of the information available isn't public or well-organized.

Generative AI excels in processing massive amounts of unorganized and unstructured data from diverse information sources. Private equity firms, for example, often need to assess businesses without much public visibility, all while accounting for the macro environment and other factors that impact valuation. AI-powered tools can reveal themes and sentiments that would be difficult or impossible for humans or other technologies to piece together.

Generative AI can also be helpful for companies looking to monitor public-facing information, including earnings calls, press releases, or social media conversations, to understand public sentiment and identify emerging trends. GenAI can also track signals of potential opportunities, such as a small research company's progress in obtaining regulatory approval for a new drug. 

GenAI systems can also tap into institutional knowledge held by analysts and subject matter experts, further improving its effectiveness at analyzing data sets and providing recommendations.

Creating AI Financial Advisors

Generative AI-powered chatbots can provide personalized assistance, answer customer queries, and offer financial insights. They aren’t limited to a simple set of scripts; they can review customer data, transaction history, and other data to provide tailored recommendations. Chatbots can also help users with account management, budgeting, and financial planning.

Investors can also use generative AI to interact with robo-advisors, which provides a cost-effective investment management option. Robo-advisors assess an investor's risk profile, investment goals, and market conditions. They then offer automated investment recommendations, such as asset allocation strategies, portfolio rebalancing, and investment diversification.

Establishing AI-Powered Call Centers

AI-powered call centers assist agents in resolving customer queries more efficiently, with faster resolutions. AI tools can also enable more self-service, freeing agents to work on the highest-priority and most complex customer questions. When combined with AWS services like Connect and Lex, AI-powered call centers deliver precise, consistent, and rapid customer service.

Chatbots built with Lex can offer conversational AI interfaces for natural and intuitive self-service experiences. Voice and text interfaces can be incorporated across key channels. Notably, companies don't incur expenses until speech or text requests are made.

Developing Personalized Marketing Content 

Businesses can combine AWS, generative AI tools, and customer profiles to enhance marketing automation through personalized content. Businesses can react in real time to changes in customer sentiment rather than sending generic, potentially ill-timed messaging that doesn’t resonate.

Amazon SES (Simple Email Service) provides a scalable and cost-effective solution for businesses to send personalized emails. Similarly, Amazon Pinpoint helps businesses engage with customers across multiple channels, including email, SMS, push notifications, and voice. It offers tools for audience segmentation, campaign management, and analytics.

Amazon Kinesis offers real-time streaming data processing, helping businesses react quickly to market changes and adapt their content accordingly. AWS Lambda, a serverless computing service, enables businesses to automate marketing processes and deliver personalized content based on customer preferences and behaviors. 

6 Risks of Generative AI in Fintech 

Despite the buzz, many people have questions about generative AI. There remain risks to be addressed, especially for fintech companies looking to protect themselves and their customers. Many of these risks aren’t new to financial companies, but they take on new forms when using generative AI.

Data Privacy and Security

Fintech companies deal with sensitive customer information, including financial data and other personal details. Make sure your data privacy and security policies incorporate generative AI use. Encryption, secure storage, and access controls are essential components of an effective risk management strategy. 

Bias and Fairness

Algorithms are trained on historical data, which may contain inherent biases that can perpetuate discrimination and unfair practices in financial decision-making processes. Fintech companies must actively monitor and mitigate bias to ensure that AI systems make decisions that are fair and impartial. The responsibility to provide equal, fair access to financial services remains crucial whether using AI or not.

Regulatory Compliance

Fintech companies must ensure that their generative AI systems comply with relevant laws, regulations, and industry standards. These include anti-money laundering and know-your-customer regulations. Generative AI tools can also potentially help with automatically filling out or filing compliance forms, reducing the risk of manual errors or forgetting to file on time.

Lack of Transparency

Generative AI models often operate as black boxes, making it challenging to understand the underlying decision-making processes. However, there’s a growing focus on developing explainable AI (XAI) models, which is especially important for making critical financial decisions like loan approvals or preventing fraud.

Fintech companies should strive to use explainable AI models that can demonstrate how decisions are made. This allows nontechnical stakeholders, customers and regulatory bodies to better understand the reasoning behind the model’s outputs. This approach reduces the risk of noncompliance, builds trust with customers, and can help your company’s reputation.

Adversarial Attacks

Generative AI systems are vulnerable to adversarial attacks, where malicious actors try to manipulate or deceive AI systems. In the fintech industry, such attacks contribute to financial fraud, identity theft, or unauthorized access to sensitive information. Fintech companies must implement robust security measures, such as anomaly detection and continuous monitoring, to detect and mitigate adversarial attacks.

Over-Reliance on AI

While generative AI can improve efficiency and decision-making, AI systems need robust oversight. Fintech companies should strike a balance between automation and human intervention to ensure that critical decisions aren't solely dependent on algorithms. Human expertise and judgment remain essential in complex financial scenarios, especially when dealing with unique or unprecedented situations.

Work With Proven AI Experts

As technology continues to transform the way people live and work, we’ll continue to see new use cases for generative AI in fintech. Companies that find the right use cases for generative AI can generate a competitive advantage while protecting themselves and customers from bad actors. 

As you explore AI use cases, look for experts who have a track record of successful deployments, such as Mission Cloud. As an AWS Premier Tier Services Partner, Mission Cloud can help you navigate the complexities of generative AI while developing solutions that maximize its potential benefits. 

Ready to explore how generative AI can transform your fintech operations? Get in touch with a cloud advisor to discuss your objectives and learn how Mission Cloud can provide you with the support and technical expertise you need.