Top Four AI Use Cases in Fintech
AI in fintech has the potential to help enhance the customer experience, streamline risk mitigation, and improve investor returns. Understanding the benefits, challenges, and case studies can help you navigate the decision-making process.
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More chaptersAI is having a significant impact on the financial sector. Institutions like banks, insurance companies, and credit agencies are using AI to streamline their workflows and services, including credit risk assessment, consulting, and investment strategies.
The potential for AI to help financial institutions be more efficient, able to provide better customer service, and more competitive is significant. But the hype surrounding AI in fintech makes it hard to know exactly what outcomes it can achieve. In this blog post, we’ll cover the various AI use cases that could apply to your business.
Key Takeaways:
- AI in fintech refers to technology that can identify historical trends and patterns to perform tasks independently, such as evaluating a recipient’s creditworthiness.
- The most common case studies where AI in fintech can be deployed include fraud detection and risk mitigation, tailored investment advice, and credit risk assessments.
- Partnering with a technology partner like Orient Software can ensure your AI in fintech solution is implemented safely and correctly.
What Is Artificial Intelligence in Fintech?
This term refers to the use of AI-driven technology to perform various tasks in the financial sector. The main technologies that power these solutions are machine learning, natural language processing, and predictive analytics.
ML is the system that learns from quality data, powering complex algorithms that enable it to think and behave like a human. NLP allows the system to understand the meaning of human language and converse with humans (this is what powers most modern chatbots).
Predictive analytics makes it possible for a system to evaluate historical data, identifying trends and patterns that allow it to make logical conclusions. For example, predictive analytics can help forecast market trends and flag suspicious account activity.
When put together, these technologies drive the primary components of AI in fintech. In 2024, an estimated 44 billion U.S. dollars was invested in AI in the financial sector, with banking institutions leading the charge by investing 31.3 billion U.S. dollars alone.
What Are the Benefits of AI in Fintech?
AI in fintech has the potential to benefit the financial sector in many ways. These benefits could be seen in avenues like risk management, customer service, internal productivity, and smarter decision-making. Let’s explore these benefits in greater detail.
Reduce and Eliminate Risk
Credit and risk assessments determine a potential borrower’s creditworthiness. Traditional credit scoring methods evaluate static credit reports, including bank statements. This often leaves underbanked individuals – people with limited access to or history with mainstream banking services – vulnerable. Since they rely on alternative financial services to make ends meet, they’re less likely to be approved for services like loans and credit.
AI credit risk assessments go one step further than reviewing static credit reports. They also evaluate financial data from alternative sources, including transactional history from Buy Now, Pay Later (BNPL) apps. This gives banking institutions a deeper understanding of a recipient’s creditworthiness, even if they have limited history with mainstream banking services.
More Personalized Financial Services
Financial institutions can gain a competitive edge by personalizing the customer experience. This isn’t just an opinion, either. Research shows that 72 percent of customers say personalization influences their choice of bank.
Such examples of personalization in fintech include AI-powered virtual assistants that offer personalized financial advice, timely reminders, and spending insights. The resulting digital transformation helps make it easier for customers to feel seen and heard, and reach their unique financial goals.
Automate Repetitive Tasks to Increase Efficiency
Automation lies at the heart of the AI revolution in finance. AI agents can be deployed to perform previously manual tasks automatically, such as invoice processing and compliance checks. Not only does this help boost speed and efficiency, but it also introduces potential cost-saving opportunities.
Identifying lucrative and viable automation opportunities is vital to maximizing an AI investment strategy.
At Orient Software, we can analyze your existing workflows and technical stack, then propose a custom AI solution that helps deliver a positive return-on-investment (ROI). Whether your priority is to enhance customer service, ensure compliance, or mitigate risk, we offer targeted solutions tailored to your needs.
Facilitate More Informed Decision-Making
AI solutions are only as reliable as the data you train them on. Otherwise, they are prone to making false assumptions, missing trends and patterns, and producing incorrect insights. Poor-quality outputs naturally lead to poor decision-making.
When trained on quality data gathered from credible sources, AI in fintech can help facilitate more informed decision-making. That means smarter trading and investment strategies, more accurate fraud detection, and fairer credit scoring results.
Challenges to Consider When Using AI in Fintech
There are many considerations to make when incorporating AI into your existing workflows. These considerations typically relate to:
- Data and privacy concerns: The finance sector is beholden to various rules and regulations. Therefore, AI must be implemented in such a way that complies with relevant industry and regional regulations. It must also contain robust cybersecurity solutions to prevent data loss or tampering from internal or external threats.
- Ethical concerns: AI models trained on low-quality data risk leaning into pre-existing biases and prejudices that make access to financial services difficult for vulnerable people. Therefore, AI models must be trained to produce only fair and balanced outcomes, especially when it comes to loan approvals.
- Lack of transparency: As AI becomes more prevalent in society, more people want to know how these models work. What data are they trained on? Where does this data come from? Financial institutions that fail to address these issues risk coming across as dishonest and suspicious, tarnishing customer relationships and brand image.
Use Cases of AI in Fintech
Even though we are still in the early stages of AI adoption, there are many cases where the technology can be used.
Which use case for AI is right for you will depend on your unique needs. At Orient Software, we take the time to identify your current situation and pain points. We then identify avenues where automation can help deliver real business value.
Here are the areas where AI is being used most in fintech:
Fraud Detection and Prevention
Trying to manually spot and address fraudulent activity is usually not possible. There are simply too many customers and too many variables to consider when it comes to fraud detection and prevention.
AI fraud detection can automatically identify historical trends and patterns, then use that data as a benchmark to establish “normal” account behavior. Any behavior that exceeds certain thresholds, such as transaction volume or location, can trigger a warning and notify the customer or financial institution. Remediation can then occur to quickly address the issue before any financial loss occurs.
AI fraud detection can also help combat deepfake fraud, such as voice cloning, of which these fraud attempts surged from 500k (2003) to 8 million (2025).
Customer Service Chatbots
AI-powered chatbots are being deployed across many industries, and the financial sector is no exception. They leverage technologies like ML, NLP, and predictive analytics to understand customer inquiries, provide real-time responses, and escalate complex matters to human workers.
By deploying AI chatbots, financial institutions can service customers outside of normal business hours. This helps improve the customer experience, as research shows that 62 percent of respondents prefer engaging with an AI assistant to waiting for a human agent.
Credit Scoring and Risk Assessment
Traditional scoring and risk assessment methods often leave underbanked individuals ineligible for loans and credit. AI credit scoring models can evaluate historical data from alternative sources, including utility bills and digital transaction histories, to broaden the perspective when determining creditworthiness.
This increases the odds of underbanked people being approved for financial services, even with minimal access to mainstream financial services. Lenders also benefit from AI credit scoring, as it widens the pool of potential customers they can service.
Trading and Portfolio Management
Although still in the early stages, AI is being used in trading and portfolio management in significant ways.
One such example is quantamental investing, an investment strategy that combines quantitative and fundamental investment approaches to help improve returns. It combines computing power with human insight, not just to determine trends and patterns, but also to ensure those predictions align with the matching market value.
When utilized correctly, AI trading and portfolio management can help increase investment returns, mitigate risk, and ensure compliance. It also opens up investors to a wider range of alternative investments, allowing for rapid portfolio adjustments in dynamic environments.
Choose Orient Software for AI in Fintech Solutions
Incorporating AI into your fintech business can be stressful. But it doesn’t have to be. At Orient Software, we have over 20 years of experience delivering innovative solutions to the financial technology sector.
For one client, Plenti, one of Australia’s leading technology-powered lenders, we addressed their need for overcoming legacy system limitations, rapid scaling, and rising security and performance demands. How? By completing a full-scale system modernization, Plenti’s technology infrastructure will be future-proofed while ensuring scalability, performance, and security.
Orient Software also assisted Xeppo, an Australian fintech leader, by addressing expertise gaps in their in-house team, addressing scalability needs, and addressing new feature requests. We delivered a tailored solution under a staff augmentation model, enhancing their existing capabilities while working closely with their in-house team to deliver iterative improvements.
When it comes to the innovative solutions that we offer to the financial sector, that’s just the tip of the iceberg. Contact us today. Discover how our data and AI services can help your business thrive in the automation era.
Shannon Jackson-Barnes
Writer
Shannon Jackson-Barnes
Writer
