Top 19 Use cases of Generative AI in fintech
The advent of generative artificial intelligence (AI), a ground-breaking branch within the AI domain, has sparked waves of enthusiasm and innovation in the dynamic realm of financial technology (Fintech). Its transformative potential stands poised to revolutionize the very fabric of Fintech operations.
This article embarks on a captivating journey through the myriad applications of generative artificial intelligence. Unveiling the top 16 instances where generative AI showcases its profound impact on Fintech, we delve into the fascinating landscape of this technology and its ability to reshape the sector.
Built upon decades of progress, generative AI stands as a monumental leap forward in the expansive field of artificial intelligence. Diverging from conventional AI techniques, it goes beyond mere classification and identification of data, venturing into the realm of creating entirely novel content — be it text, images, videos, computer code, or synthetic data. This unique capacity to generate new data presents organizations with unprecedented opportunities to streamline operations, boost productivity, trim expenses, and allocate resources more effectively to core tasks.
Our exploration takes us through the synergies between generative AI and pivotal domains such as computer vision, asset management, neural network architectures, and data augmentation. Fintech enterprises, by harnessing the power of generative AI, can revolutionize their approaches to visual data analysis, optimize asset portfolios, design cutting-edge neural network architectures, and enhance dataset augmentation. The result is a heightened efficacy of AI models, paving the way for a new era of innovation and efficiency in the Fintech landscape.
Join us as we navigate the intricate intersections of generative AI and Fintech, unlocking the potential for transformative advancements in visual data analysis, asset management, neural network design, and dataset augmentation. The marriage of generative AI with these critical facets offers Fintech organizations the key to unlocking unparalleled efficiency and staying at the forefront of technological evolution.
Over the past 20+ years, some major technologies, such as Artificial Intelligence (AI), Blockchain ( and distributed ledger technology, or DLT), Cloud, Big Data, Internet of Things (IoT), and others as shown in the diagram above, have expedited the overall development of Fintech, driven the business model reconstruction, and impacted the competitive landscape of the financial industry. Today’s technological advances are expected to reshape tomorrow’s financial landscape and longer-term future.
Before getting into the details, let’s clarify what generative AI is. The exciting topic of generative AI blends creativity with machine learning capabilities. It entails teaching models to learn from the data that is already available, and then using that training to produce new content. These models are capable of producing sounds, graphics, text, and more. Because the produced outputs can frequently not be distinguished from those made by people, generative AI is a potent tool for innovation and content creation.
What is Generative AI?
Generative AI, a subset of artificial intelligence, focuses on creating novel information by learning patterns from a dataset and applying them to generate new content. Unlike traditional AI, which relies on existing data, generative AI has a unique ability to produce entirely fresh and innovative material, pushing the boundaries of what is possible. This capability sets it apart and opens new possibilities beyond the limitations of conventional AI models.
How does Generative AI work?
Generative AI employs neural networks and advanced algorithms to detect patterns in training data, enabling the creation of fresh and unique outputs based on these patterns. Models like variational autoencoders (VAEs) and generative adversarial networks (GANs) form the core of generative AI. They are designed to generate new samples following the underlying distribution of the training data.
For example, GANs consist of two neural networks: a discriminator distinguishing between produced and genuine data, and a generator producing new samples. During training, these networks engage in a game where the generator aims to deceive the discriminator with fake samples. This adversarial training enhances the generator’s ability to produce coherent and realistic outputs.
Once trained, the generative model can generate new material by sampling from the learned distribution. For text generation, a model can be trained on a substantial corpus of text to create sentences or paragraphs that resemble the training data.
The Role of AI in Fintech
Al has become a vital part of the FinTech industry, revolutionizing the way financial services are offered. Automation is one of Al’s primary responsibilities in FinTech. Al-powered chatbots and virtual assistants are being used by financial organizations to help with basic financial activities, answer questions, and offer round-the-clock client service. This raises client satisfaction levels overall while simultaneously increasing efficiency.
Another area where Al has had a big influence is fraud detection. Large data sets may be instantly analyzed by machine learning algorithms, which can spot trends and abnormalities that might point to fraud. This assists financial institutions in identifying and stopping fraud before it has a major negative impact.
Al is also a key player in offering tailored financial guidance. Al algorithms are able to provide customized recommendations for saving, budgeting, and investment strategies based on the analysis of consumer data. People are able to make well-informed financial decisions based on their own goals and circumstances because to this degree of personalisation.
Al excels in risk assessment as well in the FinTech sector. To evaluate the risk of different financial goods and investments, machine learning algorithms can examine past data and market trends. This informs and assists financial institutions’ decision-making processes and enables them to conduct more accurate risk assessments.
Top 19 Use Cases of AI in Fintech
Use Case 1: Evaluation of Risk
An essential function of risk assessment in the FinTech sector To evaluate the risk involved in financial transactions and investments, Generative Al may examine past data, market patterns, and other pertinent variables. Accurate risk assessment enables businesses to minimize possible losses and make well-informed decisions.
Use Case 2: Fraud Detection
A vital component of the FinTech sector is fraud detection, which guards against financial losses for both clients and companies. Large amounts of data can be analyzed using Generative Al to spot trends that point to fraud. Real-time fraud detection enables businesses to stop financial losses and keep customers’ trust.
Use Case 3: Governance, Risk & Compliance (GRC)
- Skyflow’s LLM Privacy Vault is constructed to help organizations comply with complex data localization requirements. By isolating, protecting, and governing sensitive data, it aids in ensuring that organizations adhere to the necessary governance, risk management, and compliance standards.
Use Case 4 : Tailored Financial Guidance
To deliver individualised financial advice, Generative Al may examine user data, financial objectives, and risk tolerance. By taking into account a variety of variables and offering personalised recommendations, Generative Al can assist people in making well-informed decisions regarding savings, investments, and financial planning.
Use Case 5 : Trading Algorithms
In algorithmic trading, Generative Al can be used to evaluate market data, spot trends, and make trading decisions in real time. Businesses may increase productivity and profitability by automating trading procedures and executing trades based on data-driven insights by utilising the power of generative Al.
Use Case 6 : Dispute Resolution & Claim Processing
- AI Consumer Champion: DoNotPay identifies itself as an AI Consumer Champion, designed to level the playing field against large corporations that also use AI for various purposes like charging fees, collecting debts, and more.
- World’s First Robot Lawyer: The platform hosts what it calls the “world’s first robot lawyer” to help users fight corporations, bureaucracy, and even sue individuals at the press of a button. It also helps users with a range of other legal services like beating parking tickets, finding hidden money, and more23.
- AI Chatbot for Negotiating Bills: DoNotPay has introduced an AI chatbot that assists users in negotiating bills and canceling subscriptions without having to interact with customer service, showcasing a practical use of AI in dispute resolution
Use Case 7: Evaluation of Credit
A crucial step in the loan procedure is credit scoring. Generative Al may assess a person’s creditworthiness by examining a variety of data points, including income, spending patterns, and credit history. Businesses may make lending decisions more quickly and accurately by automating the credit rating process.
Use Case 8: Analytical Forecasting
Generative Al is capable of forecasting a variety of financial factors, including stock prices, market trends, and consumer behaviour, by analysing historical data and market trends. Businesses can keep ahead of market volatility and make data-driven decisions by utilising predictive analysis.
Use Case 9: Customer Experience (CX), Customer Support (CS) & Market Research (MR)
- Appen emphasizes that Generative AI can drive greater customer satisfaction by facilitating personalized interactions based on a person’s unique interests, needs, and preferences. Human feedback is highlighted as a crucial component for building quality generative AI applications.
- The platform has launched products to help clients unlock the potential of Generative AI for exceptional customer experiences, supported by a global team of AI Training Specialists
- AI provides ONDC as a service. Its AI helps in converting existing SKUs to ONDC-compliant taxonomy effortlessly through voice, text or image inputs along with multilingual support.
- Adya uses both third-party solutions and its own internally trained LLM to offer conversational commerce services through social media channels such as WhatsApp, creating a seamless overlay that enhances both communication and transaction experiences.
Use Case 10: Automated Customer Service
Chatbots and virtual assistants are two examples of customer service procedures that can be automated with the use of generative al. Generative Al can assist and respond to client inquiries in a personalised manner by comprehending natural language and context, which increases customer happiness and lessens the strain on customer support workers.
Use Case 11 : Complying with Regulations
FinTech enterprises need to adhere to financial regulations. Generative Al is capable of analysing regulatory requirements and spotting possible inconsistencies or gaps in financial processes. Businesses may stay out of trouble and keep their good standing with regulators by making sure they comply with regulations.
Use Case 12 : Processing Loans
Processing loans requires a lot of paperwork and manual labour. Through document verification, loan agreement generation, and borrower data analysis, Generative Al may automate the loan processing cycle. Businesses can decrease processing times, boost productivity, and enhance customer satisfaction by optimising the loan processing procedure.
Use Case 13 : Portfolio Management
By examining risk profiles, investment methods, and market data, Generative Al can help with portfolio management. By offering real-time information and recommendations, Generative Al can assist investors in making educated investing decisions and optimizing their portfolios.
Use Case 14 : Boosting Cybersecurity
In the realm of financial technology, safeguarding against various risks and vulnerabilities is paramount, and generative AI plays a pivotal role is fortifying digital architecture. It’s applications span across crucial areas such as DDoS prevention, DNS security, PKI-based identity, blockchain security, and general cybersecurity measures. Businesses can enhance their defences, ensuring the confidentiality and integrity of sensitive financial data with the incorporation of generative AI capabilities.
Moreover, generative AI contributes to bolstering user authentication through features like two-factor authentication, Ethereum integration, and natural language processing (NLP). These advancements not only secure cryptocurrency ecosystems but also optimize communication interfaces for a more robust cybersecurity posture.
- An exemplary company in this space is Zeron, a platform to bolster cybersecurity and risk management by illuminating hidden risks, ensuring regulatory compliance, and aiding in navigating cyber risk scenarios within organizational contexts. It integrates with other security tools, automates ticket routing, and offers real-time reporting to enhance cybersecurity decision-making and operational efficiency.
Use Case 15: The Development of Financial Services
As financial services have developed, Asian Super Apps—also referred to as TechFins—like WeChat have become more prevalent than Fintechs. Before adding financial services to their extensive offers, these platforms first appeared as social messaging or mobile apps. Many nations have adopted this profitable business model, including China, Korea, Malaysia, Japan, and Kazakhstan. It is critical to distinguish this idea from platform-based ecosystems and markets that only include financial and non-core services. Notably, despite their wide range of capabilities and widespread use in retail and other industries, well-known companies like Paypal, Revolut, Apple, and Amazon do not qualify as Super Apps.
Use Case 16: Efficient Asset Administration
FinTech businesses can optimize their asset management methods with the help of Generative Al. Organizations may examine enormous volumes of financial data, market trends, and risk profiles by utilizing generative Al to provide insights and make wise investment decisions. With the use of this program, portfolio managers may better allocate their assets, detect possible investment opportunities, and control risk—all of which improve performance and yield higher returns.
Use Case 17: Development of Computer Vision
In the FinTech sector, generative alpha can completely transform computer vision skills. Businesses can swiftly analyze visual data and enable automated picture recognition, object detection, and facial recognition by utilizing generative Al. This solution streamlines procedures and strengthens security measures by enabling better fraud detection, automated document processing, and improved user verification.
Use Case 18: Creative Structures for Neural Networks
In the FinTech world, generative alpha is essential to the creation of neural network architectures. Through its utilization, businesses can enhance the neural network’s design and parameters, leading to an improvement in the accuracy and performance of the models. Predictive powers are improved and more complex financial analysis and forecasting is made possible by the investigation of innovative network designs made possible by generative alpha. These architectures include deep neural networks, convolutional neural networks (CNNs), and recurrent neural networks (RNNs).
Use Case 19: Improved Augmentation of Data
In the FinTech sector, generative alpha can enhance pre-existing datasets, adding value to the data that is accessible for training and validation. Generative Al assists in overcoming constraints imposed by sparse or unbalanced datasets by producing artificial data points. By assuring better generalization to real-world settings and diversifying the training data, this application enhances the performance and robustness of Al models. FinTech organizations may increase risk assessment, identify anomalies, and create more accurate predictions with enhanced data augmentation.
In conclusion, generative artificial intelligence (AI) is reshaping fintech by revolutionizing risk assessment, fraud detection, tailored financial guidance, and more.
From automating customer service to ensuring regulatory compliance, generative Al’s applications extend across various fintech domains. Its contributions to computer vision, creative neural network structures, and enhanced data augmentation further underscore its transformative impact. The synergy between generative Al and fintech holds the key to unparalleled efficiency and innovation, shaping a future where creativity, automation, and data-driven insights converge for a more efficient and secure financial landscape.