AI and ML solutions in Fintech: Benefits and Challenges

ML, AI and FinTech: How Maching Learning and Artificial Intelligence Help You Benefit

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Today, the financial industry can’t do without the stack of modern technologies namely Artificial Intelligence (AI) and Machine Learning (ML). The systems help to estimate different risks, understand clients’ habits, and reduce costs by automating processes. In this article, we are going to consider the main benefits of applying AI and ML solutions in the financial sphere.

Key Takeaways
The financial technology sector (fintech) deals with the employment of technology in financial services and transactions. With the development of technology, AI and ML solutions have been applied in Fintech to enhance customer experience, lower costs, and boost efficiency. The term "fintech modeling" refers to the application of mathematical and statistical methods to the development of models for the analysis of financial data and predicting future events in the fintech sector. Fintech applications include insurance, loans, and stock trading, which rely heavily on algorithms to increase productivity, cut costs, and enhance customer experience. Automating fraud detection, credit scoring, and loan approvals can increase efficiency and lower costs. While implementing AI and ML solutions in the Fintech industry has many advantages, there are also drawbacks.

The Fintech sector could undergo a revolution thanks to AI and ML technologies. Transparency, privacy, security, diversity, and inclusivity must be prioritized when developing and implementing AI and ML solutions for the fintech industry. Fintech companies can increase customer satisfaction while cutting costs and increasing profitability by automating processes and using algorithms to analyze data more effectively.

What is Meant by ML, AI and Fintech?

Fintech is a collective concept, which includes all the existing technologies that are used by banks and other financial establishments. AI and ML are an important part of fintech. Learn more information about them from the article “What is Financial Technology? FinTech Definition, Evolution, Examples”.

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

ML is a special approach, which allows specialists to “teach” computers without programming. Machine Learning is a subtype of Artificial Intelligence. Its work is based on Big Data: a computer uses historical data as input to predict new output values. This process resembles a baby who learns to classify objects and events independently and determine the relationships between them.

So, let’s find out more about the AI Transformation in fintech.

How does Fintech Benefit from AI & ML?

The significance of AI/ML solutions is difficult to overestimate because they open numerous opportunities to improve and expedite service provision. We would like to mention the following benefits:

  • Saving time. Employees can be not involved in most processes because operations are performed by the computer.

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

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

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

  • Improving customer experience. The AI Transformation in FinTech is obvious: users are able to operate online and make payments in no time. Furthermore, financial companies have begun to better understand the customers’ requirements thanks to AI and ML.

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

AI & ML in Fintech: Real-Life Cases

It’s impossible to mention all existing use cases so we have cited below the most vivid ones.


Bots with Artificial Intelligence are able to provide users with certain links and forms that they request. A good example is users who wish to find out details about commissions. They just need to enter their request and then 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.

A well-known fact: each client asks the robot differently. Machine Learning in Fintech helps to gather the data about user requests and improve language recognition.


In the bank field, market players usually apply two-factor logins (password and biometrics). So, AI and ML solutions are crucial technologies, which speed up the authorization process and enhance the level of its accuracy. Moreover, some banks have implemented facial recognition thanks to the advanced versions of AI and ML.

Big Data & Analytics

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

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

Better Risk management

With a set of AI tools, businesses have another competitive advantage: they can make predictions of future changes in the financial market. As a result, it allows increasing sales, maximizing resource utilization, and augmenting operational efficiency by removing different risks.

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. AI/ML solutions simplify numerous things and even provide some assistance. Accountants and economists start doing their work faster and more accurately.

Other potential gains from AI applications in Fintech are:

  • Improved Credit Quality Assessment;

  • Advancement of financial inclusion;

  • Stronger capital optimization;

  • Better stress testing;

  • Improved market impact analysis;

  • Improved trading and investment strategies;

  • Advanced compliance and risk mitigation.

Agilie expertise in AI-powered Fintech apps

In recent years, the world of blockchain technology and cryptocurrency has 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.

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. These modules accurately detect if a user is smiling or not, making for a more immersive and interactive experience. To ensure accurate predictions, the implementation involves 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.

Security is a top priority for Lolypto, which operates on the Binance blockchain. The team employs AWS environment tools to secure sensitive user data, particularly wallet information.  To simplify cryptocurrency storage and management, Lolypto offers a custodial blockchain-based wallet that allows users to store their crypto in-game without additional fees, making it an accessible option for NFT newcomers.

The key features of Lolypto include the ability to buy and stake NFTs safely, the play2earn concept, an integrated crypto wallet, and the opportunity to meet real people.

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.

The main cons related to AI Transformation in FinTech

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

The first thing you ought to know is that even AI and ML solutions make mistakes, so you shouldn’t count on infallible work without any control from employees. Alternatively, you can discuss a developer of the technology to provide you with a guardrail software, which would switch off the AI solution when it begins to produce incorrect outputs.

Another crucial risk has to do with the integration and implementation. AI providers must understand the IT estate, processes and data sets in your company to roll out a proof-of-concept model. The desire to adopt AI/ML solutions may become a protracted thing when there is a lack of discussion.

And the last common snag is the transparency of data. Machine Learning requires clear and accurate information arrays. Otherwise, the level of errors in outputs is likely to be significantly higher. Thus, if a business isn’t able to trace the quality of data, first of all, it must develop internal tools for that. Also, such a company should have a manual on how to act in certain circumstances.

Agilie has been implementing fintech solutions since 2010. Contact us in any convenient way to discuss your project and find the most appropriate systems for you. Our tasks are simple: to help you increase your company’s income by boosting your work and improving the quality of your services.


How do AI and ML benefit fintech companies?

AI and ML benefit fintech companies in various ways, such as improving decision-making, security, risk management, fraud detection, quantitative and algorithmic trading, personalized banking, workflow optimization and automation.

How can AI and ML help fintech companies with credit scoring?

AI and ML can help fintech companies make better credit risk assessments by analyzing vast data and identifying patterns and trends humans may miss.

How can AI and ML help with fraud detection in fintech?

AI and ML can analyze transaction data, identify anomalies, and flag potential fraud or security breaches in real-time, helping fintech companies to detect and prevent fraud.

How can AI and ML help with personalized banking services?

AI chatbots can provide personalized financial advice, while ML algorithms can analyze customer data to identify their financial needs and preferences and offer personalized product recommendations.

How can AI and ML help optimize workflows and automate processes in fintech?

AI and ML can help automate workflows and optimize processes, such as loan underwriting, customer onboarding, and account management, increasing efficiency, reducing costs, and improving customer experience.

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