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