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How Implementing AI Can Help You Win the Fintech Game

How Implementing AI Can Help You Win the Fintech Game - Global Banking | Finance

According to BCG, the number of fintech startups has almost tripled in recent years. That’s why business owners entering the fintech game must constantly overcome obstacles in the form of increased competition, regulatory updates, and rising user expectations. Many players are forced to fall out of the race, giving way to more promising сompanies that have found ways to strengthen their offerings. Artificial intelligence can be the very force that will enable your startup to move forward successfully. Let’s delve into exactly how the implementation of AI technology can help you to achieve your goals.

AI in the Fintech Market Insights

The financial sector was one of the first to use artificial intelligence to optimize business processes such as customer segmentation, fraud detection, etc. Today, AI is one of the key technologies fueling the fintech ecosystem, and according to the Research and Markets forecast, this market will grow from $7.25 billion in 2021 to $24.17 billion at an average annual growth rate of 27.6%.

More and more fintech companies leverage AI and machine learning algorithms when developing new fintech software products to achieve a wide range of business goals. According to CB Insights, the most popular use cases of AI in fintech include credit scoring, personal finance, asset management, insurance, predictive analytics, regulatory processes, compliance, and fraud detection. Let’s look at some categories in more detail to provide you with ideas on how you can implement AI in your project. 

AI Assistants and Chatbots Level Up Customer Support

No matter how digitally advanced your product is, users always have questions, and the speed and quality of solving their queries affects the customer experience. Therefore, the automation of customer support is one of the most basic tasks for every company, and the AI ​​system with its ability to efficiently process large volumes of data and self-learning looks like an excellent solution. 

AI chatbots and AI assistants can automate regular tasks, improve security by detecting suspicious activity, guide users through complex operations, and even manage their income and expenses more efficiently. For example, Bank of America has developed its own virtual assistant named Erica, which can help users lock or unlock their debit card, check their balance, track performance and even find the best investment options.

Natural Language Processing (NLP) and Natural Language Understanding (NLU) are the two main technologies behind AI assistants and chatbots. NLP allows you to process and interpret user commands, while NLU allows the system to understand context and intent. Together, these technologies enable smooth communication between users and AI assistants. Unlike rule-based chatbots, which are able to answer only according to a pre-configured scenario, AI-based chatbots are more flexible and capable of producing an answer based on processed data.

Financial Forecasting Improves Decision Making

Having an idea of ​​what future supply, demand, revenues, expenses, and cash flow will be, managers and CFOs of companies can make better business decisions. AI helps make business forecasting more accurate with advanced machine learning algorithms. By collecting and processing data in an automated mode, such a system allows predicting trends and forecasts to help fintech companies to more quickly navigate changing market conditions. 

With accurate revenue forecasts, financial companies can plan investments and expenditures as well. For example, Bank of America recently announced the launch of a machine learning-based CashPro Forecasting platform that will help companies predict future cash needs.

To implement machine learning forecasting, first of all, you need to clearly formulate goals, collect internal and external data (reports, trends, GDPs), and create models that can solve these tasks. Experienced engineers will be able to do this as effectively as possible by applying the appropriate algorithms and approaches to the optimization of ML models.

Fraud Detection With AI Enhances Security

Fintech companies are the number one target for fraudsters. According to the Identity Fraud Report 2022, fintech was one of the two sectors most suffering from identity fraud in 2021. More sophisticated attack methods are forcing companies to look for more effective ways to counter them. The use of AI plays one of the leading roles in this.

AI technology is unsurpassed in finding anomalies, moreover, such a system is able to respond to suspicious activity in a timely and automatic manner. ML models are trained on the basis of available data on the main types of fraud, so the system learns to determine which transactions are considered risky and which are legitimate. 

For example, AI solutions can learn about users’ spending patterns and predict their future behavior. If the transaction doesn’t correspond to the usual behavior of the user, whether it’s the amount or the location from which the transaction is carried out, the system can request additional verification or notify the bank employee about possible fraud.

Machine Learning Helps You Meet Regulatory Requirements

Regulatory compliance is another challenge fintech companies face. AI can significantly improve the compliance process thanks to the automation of such operations as document processing, customer onboarding, monitoring changes in regulation, etc. Machine learning and NLP technologies make this happen.

Here are just a few examples of how AI and ML in financial software development can simplify a company’s path to meeting regulations:

  • Streamlined KYC process. OCR with machine learning effectively extracts data from passport or ID photos for fast onboarding of new customers.
  • Transaction screening. Automatic tracking of suspicious activities helps mitigate risks associated with fraudulent transactions and meet AML requirements.
  • Trade monitoring. AI-powered systems can improve the accuracy of detecting suspicious trades by analyzing the behavior of traders.

With the right level of accuracy, AI systems can significantly reduce a company’s compliance risks, but such projects still require experienced engineers to achieve great model performance.

Final Thoughts

By optimizing at least the four processes mentioned above, fintech companies have a much higher chance of increasing their efficiency and beating their competitors. The adoption of artificial intelligence provides a number of advantages that can improve company efficiency and provide an exceptional customer experience.


Maksim Bieliai

BA Team Leader, Fintech Market Analyst at MobiDev

Global Banking & Finance Review


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