AI in Fintech: How is AI Used in Fintech Industry?

The continuous evolution of technologies reshapes the functionality of each labor sphere. The emergence of Artificial Intelligence (AI) is a breakthrough due to the alleviation of the working processes it demonstrates. Risk management, predictions, big data analytics enable fintech and business sectors to minimize errors in decision-making and generate client-oriented solutions. This article will demonstrate AI in fintech market, its benefits, use cases, Agilie’s success, and AI-relevant challenges. 

What is AI in Fintech Examples?

AI is a separate section of computer science dedicated to building smart machines. Such systems can perform work that typically requires human intelligence. The best examples of AI are Siri, Alexa, email spam filters, self-driving cars, and the like.

Fintech is a collective concept that includes all the existing technologies banks and other financial establishments use. In the linked article, you can explore the fintech requirements for running an efficient business in the US regulatory landscape. This notion is central in fintech app development. Artificial Intelligence can be subdivided into the subsequent components: machine learning (ML), neural networks, and natural language processing.

Learn about Fintech definitions, evolution, and examples in our article.

Machine Learning

ML is a special approach that allows specialists to “teach” computers without programming. Machine Learning is a subtype of Artificial Intelligence. ML represents learning to classify objects and events independently and determine their relationships.

There are the following examples of machine learning in AI:

• Personalized advice

Utilizing big data analytics, ML enables the financial technology sector or businesses that apply fintech solutions to generate original and personalized advice based on client behavior. 

For instance, on Amazon, machine learning evaluates big data on customer demographics, purchasing behavior comparing their preferences to those clients in the same demographic group to build a relevant product recommendation. 

• Predictive analysis

ML focuses on Big Data: a computer uses historical data as input to predict new output values. Arteria AI helps banks with optimizing the contract's specificities. By using ML, Arteria AI eliminates issues and risks for the potential negotiation process. 

• Spam filtering

ML is beneficial for filtering fraudulent activities and preventing the customers from spam and hacking attacks. PayPal applies ML algorithms to detect user location and seller IP address to ensure the possible transaction will be safe. 

Neural Network

Neural learning represents the computing architecture type, which resembles human brain functionality. This AI-based feature focuses on the collection of processing units (nodes) that pass data to each other just like neurons in the brain pass electric impulses. 

NeuralWare provides network-based empirical modeling solutions for multiple issues. Those issues include pattern recognition, classification, and predictions. 

Natural Language Processing

Natural Language Processing (NLP) is the AI-centric branch that focuses on providing machines with the ability to perceive and understand texts like human beings. 

Deutsche Bank (GER) applies NLP to eliminate greenwashing when analyzing the carbon-related discourse within the provided reports. 

Benefits of AI in Fintech?

Artificial Intelligence in fintech produces the following benefits:

• Saving time. Employees can only be involved in some processes if the computer performs the operations.

Example: Automated financial reporting can reduce time and minimize errors in calculations and data analysis compared to the manual data proceedings.

• Uninterrupted working process. Human resources are limited, while machines work as long as needed and round the clock.

Example: In neobanks, financial AI chatbots work all round the clock by providing the users with routine tasks like money transfers, checking balances, and solving customers’ issues in a maximized linguistically natural way. 

• Reducing costs. It’s impossible to imagine how many people you need to serve all clients manually. Moreover, it’s worth mentioning complex tasks, which, without modern AI  fintech solutions, may take a decent amount of time and cost an arm and a leg.

Example: Due to the automated repetitive tasks, Artificial Intelligence can benefit fintech sectors by generating strategic business solutions by evaluating the possible threats, challenges, and finding their preventive measures. The employees can focus on generating creative decisions on product/software/service optimization.

• Augmenting the capabilities of individuals. For example, specialists operate faster using modern tools such as AI software. Thus, the company's crucial decisions are made in the shortest possible time.

Example: From a fintech standpoint, AI-powered insurance analysis can benefit the Healthcare industry by generating need-based insurance packages. AI-based techniques can consider client solvency and help medical staff and insurance agents build the most cost-effective schemes for client treatment coverage.

• Improving customer experience. The AI transformation in Fintech is obvious: users can operate online and make payments quickly. Furthermore, financial companies have begun to understand customers' requirements better thanks to AI.

Example: In neobanks, AI-based automated budgeting tools can improve user experience by advising them a savings model, in which they will automatically transfer a small part of the money to the savings balance. The AI considers the user’s overall monthly income when deducing the balance. 

• Fraud elimination. The computer monitors each account and internal system 24/7. If it notices something suspicious, it blocks transactions immediately.

Example: The AI-powered push notifications for unusual account activity (e.g., logging in from a different device) can prevent the owner from a potential hacking attack. 

AI in Fintech Use Cases

How is AI used in fintech? It’s impossible to mention all existing use cases, so we have cited the most vivid ones.

Chatbots

Bots with Artificial Intelligence can provide users with certain links and forms they request. A good example is users who wish to find out details about commissions. They must enter their request and get a link with a comprehensive description. Thus, consultants can focus on solving other non-standard problems instead of wasting their time on the simplest tasks.

The Future of Business Administration Journal specifies that AI-powered chatbots contribute to the fintech industry in the following dimensions: 

• Accuracy of response

Chatbots provide clients with reliable information according to their financial demands.

AI-powered Eno from Capital One Bank (The US) provides customers with information like transaction status and answers questions about the account.

• Personalized recommendations

The chatbot gives clients personalized answers and advice on their choices and history.

AI-driven chatbot Erica from the Bank of America (The US) ensures the users with planning, spending, and saving the financial input. 

• Self-learning 

The chatbots are about independent self-improvement. The machine learning algorithms consider conversation logs and text data to improve the chatbots’ unbiased quality of service provision. 

JPM Bot from JPMorgan Chase (The US) contributes to the clients’ investment solutions through maintaining the qualified conversation in Chinese and English. 

• Availability

Chatbots are uninterrupted and provide services around the clock.

Ally Assist from the Ally Bank (The US) works 24/7, supporting the app and the company’s website. The chatbot utilizes voice to inform the client about tracking transfers. 

• Simplicity in use

The chatbots are easy to navigate due to their user-friendliness.

NOMI from the Royal Bank of Canada (CA) is user-friendly due to its continuous reminders, alerts, and recommendations based on the user’s financial in-app behavior. 

Security

Security is a central element of AI in fintech industry, considering the protection of client’s data and prevention of the leakage of the bank’s or organization’s confidential information. Here are some AI-based approaches in fintech used to reinforce the security:

• Two-factor authentication

In the bank field, market players usually apply two-factor logins (password and biometrics). The organizations like Microsoft and Apple apply this security method to prevent data breaches, representing a decent AI fintech solution. So, AI-based are crucial technologies that speed up the authorization process and enhance its accuracy. 

• RFID identification (SecLAP) method 

The Journal of Applied Sciences introduces the secure and ultralight reciprocal RFID identification (SecLAP) method, which is resistant to cyberattacks, guaranteeing the safety of both forward and reverse data flow. 

Amazon combines RFID and Just Walk Out methods to enhance the client’s banking card security. Customer takes an item of cloth and does to the exist. Then, they apply their card or pam to the Amazon One palm-scanning device, which takes the sum automatically. RFID does not require pin code. 

• Blockchain technology for DDoS protection

Blockchain technology ensures decentralized and robust design against the DDoS attacks. JPMorgan (The US) implements an Ethereum-based AI-powered blockchain network, Quorum, to transmit interbank information via 300 banks. 

Big Data Analytics & Risk Management

The main task of any enterprise is to increase income. This may be achieved by analyzing customers' needs to offer exactly what they wish. Today, it’s easy to force smart computers to learn users' behavior, habits, and expenses.

Knowing user habits and frequent transactions is also a key to cyber security and early fraud detection. The system immediately blocks the account if it sees unusual and suspicious actions.

The ING Bank (NL) experiments with generative AI to define and evaluate risks regarding the credits by analyzing the clients’ banking histories. 

Reducing paperwork

You can’t deny that finance is a highly confusing topic. It usually includes a lot of paperwork and processes to go through. Artificial Intelligence offers solutions to simplify numerous things and even provide some assistance. Accountants and economists start doing their work faster and more accurately.

The AI-driven document processing derives principal information from the unstructured documents and classifies them via their type, which preserves time for sorting, analyzing, and making decisions per information. Enlitic, an AI Healthcare company, applies AI-driven document processing to manage and process radiology images to generate effective solutions. 

Agilie expertise in AI-powered Fintech apps

In recent years, blockchain technology and cryptocurrency have seen significant growth and adoption. From decentralized finance to non-fungible tokens (NFTs), the possibilities seem endless. One area that has seen remarkable growth is the world of Web3 social interaction games. Agilie implemented this approach and created Lolypto - a Web3 game that combines social interaction, NFT staking, and AI technology.

Lolypto's gameplay is based on the "You laugh you lose" concept, where users play with NFT characters, which they call Jokers. Lolypto is a platform that enables users to buy, sell, and stake NFTs using their wallets while incorporating gamification. It allows players to interact with real people and earn real coins through a video chatting game that revolves around the fusion of two ideas - the video chat game "Flinch" and the move-to-earn game "Stepn." With Lolypto you can buy cryptocurrencies via third-party services like Moonpay and Circle.

Find out more about NFT game development in our article.

Based on the client's constraints on time and budget, a minimum viable product (MVP) version was defined, covering all fundamental technical aspects and allowing them to test the overall idea. The development team helped with the rest of the assets to start raising investments.

One unique feature that differentiates Lolypto from other NFT platforms is AI modules. The role of AI in fintech for this game was intense. The first result Agilie achieved was that Artificial Intelligence modules accurately detect whether a user’s behavior is smiling, creating a more immersive and interactive experience. To ensure accurate predictions, the implementation involved a combination of dynamic configuration, ML-kit, and cloud-based solutions that consider lighting, internet speed, the distance between players and the camera, and antifraud measures.

AI and fintech mattered for reinforcing the application’s security. Lolypto operates on the Binance blockchain. The team implemented the AWS environment tools that resulted in securing sensitive user data, particularly wallet information. To simplify cryptocurrency storage and management, Lolypto offered a custodial blockchain-based wallet that enabled users to store their crypto in-game without additional fees, making it an accessible option for NFT newcomers.

Lolypto is a revolutionary AI-based chat game app combining NFTs and Web3 technology, providing users with a fun and engaging way to enter the cryptocurrency world. With its unique features, Lolypto is set to become a game-changer in Web3 social interaction games.

Learn more about Fintech software development services.

Challenges of AI Applications in Fintech and Solutions

There are no ideal systems: even Artificial Intelligence have certain cons despite a whole list of advantages. And the presence of cons doesn’t mean you should abandon modern technologies.

Fallacies and AI Solutions for Fintech Decision-Making

Data Bias

First, you should know that even AIs make mistakes, so you shouldn’t count on infallible work without any control from employees. Data bias can occur due to the incorrect analyzing, proceeding, and cleansing data, leading to misinterpretations. 

Solution: You can discuss a developer of the technology to provide you with guardrail software, which would switch off the AI solution when it begins to produce incorrect outputs. Generative adversarial network (GAN) can produce more stable training for AI software to separate biased information and generate correct decisions and predictions. 

Integration/Implementation

AI providers must understand your company's IT estate, processes, and data sets to develop a proof-of-concept model. The algorithmic bias may occur when the data used for training AI algorithms contain bias. Consequently, the integration/implementation of Artificial Intelligence will lead to destabilization of the company’s network chain. 

Solution: An algorithm audit can solve the issue of algorithmic bias within AI integration and implementation. You should conduct a socio-technical risk assessment of Artificial Intelligence software. Additionally, adherence to the New York City Algorithmic Bias Law (Local Law 144) and the EU AI Artificial Intelligence Act is recommended to know what exact criteria for AI are necessary to maintain smooth integration and bias-free implementation. 

Data Transparency

The last common snag is data transparency. AI-based machine learning requires clear and accurate information arrays to understand data sets and the ways algorithms work, and the ways they can produce/eliminate data bias. 

Solution: You can utilize data processing approaches such as feature engineering or data augmentation to analyze how raw data transforms into AI input. This option will enhance transparency by letting you know the potential source of the data or algorithmic bias in informational interpretation. 

Choose Agilie as a Reliable Partner

Agilie has been implementing fintech solutions since 2010. Our tasks are simple: to help you increase your company’s income by boosting your work and improving the quality of your services.

Dedicated to the values of customer recognition, passion, and efficiency, Agilie keeps evolving in its tech competence, by generating new fintech solutions for the fintech, media, e-commerce, and logistics sectors. 

Our tech expertise include:

• Fintech & Blockchain software development/optimization

Agilie worked on a multi-digital wallet, Chameleon Pay, from scratch. Our team of experts integrated multiple cryptocurrencies, leading to blockchain network universification.

• UI/UX design

Agilie cooperated with Givingli, an app that provides digital congratulations cards on its payment solutions and operational functionality. Our team of experts added the function of ‘attaching gift certificate’ and resolved the scalability dilemma by optimizing the app’s smooth work during the celebration days (Christmas and Mother’s Day). 

Learn more about Agilie’s competence via our success stories.

Conclusion

Fintech and AI are inseparable, considering the advantages it provides to optimizing banking and business operations. Risk management, big data analysis, predictions and security enhancement, represent the benefits of AI algorithms. Agile’s work with Lolypto demonstrated the mastery of integrating Artificial Intelligence into the NFT staking and social interactions. Emotional expressivity AI-driven recognition patterns, as combined with the Binance blockchain network, ensure game recognition and client’s wallet security. Interested in integrating AI into your product - join our journey. 

Contact us to discuss your project and find the most appropriate systems.