AI in Fintech: Top 8 Use Cases with Examples

Further, AI systems also make processes compliant with the changing regulatory compliance. Mastercard has launched its latest Decision Intelligence technology for analysis of historical payment data for each customer. The applications of artificial intelligence in fintech for improving fraud detection would also help in detecting new types of fraud. For example, AI could help in detecting instances of exploitation of sign-up bonuses for creating new credit card accounts.

One report found that 27 percent of all payments made in 2020 were done with credit cards. The market value of AI in finance was estimated to be $9.45 billion in 2021 and is expected to grow 16.5 percent by 2030. The longevity of AI/ML models relies on continuous maintenance to preserve their accuracy and relevance in an ever-changing financial landscape. Employee resistance to AI adoption, often due to job security fears or a lack of understanding, necessitates comprehensive training and change management strategies. Compliance with ever-evolving banking regulations is a complex task when integrating AI and ML, demanding careful navigation of legal frameworks.

Data Collection and Analysis

As the future beckons, partnering with Kanerika ensures you’re ahead of the curve, leveraging cutting-edge solutions. Dive into the future of generative AI in financial services and banking with us. Navigating the vast sea of financial documents, from annual reports and invoices to earnings calls, is a daunting task for banks. Our seventh in the series of use cases of generative AI in financial services and banking covers invoice and document analysis.

• It’s a win-win situation for a more efficient and equitable educational experience.
• Conversational AI, driven by natural language processing and machine learning capabilities, emulates human interactions.
• This includes the deployment of machine learning models, natural language processing algorithms, and other AI-driven solutions.

Although AI has the potential to revolutionize the finance and banking industry, several challenges still need to be addressed before wider adoption can occur. Facial recognition provides a more secure and convenient banking experience for customers, eliminating the need to remember complex passwords or worry about personal information being stolen. Moreover, facial recognition can also prevent fraud by detecting attempts by criminals to impersonate a customer using stolen personal information. Automating middle-office tasks with AI has the potential to save North American banks $70 billion by 2025.

What are some of the benefits of artificial intelligence in banking?

Process Automation is one of the major benefits of AI in the banking and finance sector. Artificial intelligence applications in banking are gaining popularity in auto-insurance or credit lending operations. Unlike legacy banking systems, AI in finance and banking is processing insurance claims and checks the credit eligibility within seconds all through an automated process.

Another limitation of Generative AI is that it can produce incorrect results if it’s fed with poor or incomplete data. For example, Generative AI should be used cautiously when dealing with sensitive customer data. It also shouldn’t be relied upon to stay compliant with different government regulations, such as the General Data Protection Regulation (GDPR) or the General Data Protection Regulation (CCPA). In the video, DeMarco delves into how Carta’s remarkable growth and expansion of product lines have been supported by its strategic adoption of Generative AI technologies.

Risk Management

According to a report by Juniper Research, AI-powered fraud detection solutions will save banks and other financial institutions $17.5 billion annually by 2026. The report also found that AI-powered fraud detection solutions can reduce fraud losses by up to 50%. External global factors such as currency fluctuations, natural disasters, or political unrest seriously impact the banking and financial industries.

• Quantitative trading is the process of using large data sets to identify patterns that can be used to make strategic trades.
• As a result, a whole range of service-oriented organizations can benefit from intelligent chatbots.
• AI-powered systems can analyze customer behavior, communication patterns, and demographics to personalize debt collection efforts, improving the chances of successful debt recovery while optimizing resources.
• AI and ML based mobile banking apps let finance service providers track and evaluate the credit eligibility of customers in minutes.
• The potential applications span from streamlined customer interactions to supercharged data analysis.

The AI model can help bankers make these decisions by providing access to vast amounts of datasets and using sophisticated algorithms to identify patterns and relationships. Banks are under constant pressure to maintain the highest levels of security possible. AI-powered cybersecurity solutions can help banks detect and prevent fraud and protect customer data. Chatbots help us in many ways, they play a major role in providing a better experience for all services to customers. With the help of these, the customer can get all the information about his bank a/c immediately. Some chatbots also provide services on WhatsApp, where you can easily get all the information related to your account.

Future Of AI In Banking And Finance

Furthermore, we offer a diverse range of services, each tailored to specific needs, making us an ideal partner for AI development. For instance, your bank employees are encouraged to propose AI-driven solutions for streamlining internal processes. This not only improves operational efficiency but also empowers staff to contribute to the bank’s AI transformation. To unlock the full potential of AI, prioritize the ease of access, preparation, and utilization of data. Efficient data handling and management are what transform ordinary AI models into invaluable assets. AI is playing a pivotal role in detecting and preventing fraud within the banking sector.

Due to the cost savings from implementing AI technologies, banks can offer better deals and draw in more clients. Based on McKinsey’s report, 44% of businesses adopt AI technology to lower company costs in areas (source ). Traditional banks, often called incumbent or established financial institutions, face stiff competition from a new wave of players known as neobanks or challenger banks.

AI in banking: Use cases and benefits

The application of AI in credit scoring has improved accuracy and speed, allowing banks to make informed lending decisions and expand access to financial services. Additionally, AI’s contribution to fraud detection and prevention has been instrumental in safeguarding banks and customers from fraudulent activities. In addition to improving customer experiences, AI is expected to play a significant role in risk management in the banking and financial services industry. By analyzing vast amounts of data in real-time, machine learning algorithms can detect and prevent fraudulent activities, reduce the risk of errors, and enhance overall security. The banking and finance industry is data-driven, and AI can analyze vast amounts of data, providing insights that can help financial institutions make better decisions.

Challenged with processing diverse invoices from 600+ partners, manual methods led to payment delays. AI-driven loan decisions, especially denials, need to be clear and understandable. Enter the conditional generative adversarial network (GAN), a specialized form of Generative AI. It crafts user-friendly explanations for loan denials, hierarchically organizing reasons, and ensuring that feedback is insightful and easy to grasp. Accenture’s report, “A New Era of Generative AI for Everyone,” envisions generative AI as an enhancer of human capabilities, signaling a transformative shift in work and business practices.

Examples of Successful Startups in the Finance Industry

The minimum account requirement is only $500, offering many investing possibilities, including cryptocurrency. It also has relatively cheap costs, with most accounts paying just 0.25% and no transaction fees. Since UBS bought Wealthfront at the beginning of 2022, the company is anticipated to grow even more quickly.

Read more about Top 7 Use Cases of AI For Banks here.