AI in Fintech

Artificial Intelligence helps Fintech companies resolve issues regarding security, data-driven analytics, and data processing. Artificial Intelligence is becoming more commonly used in a variety of fintech applications.

Fintech attracts innovation into the financial sector as a whole. Hard to imagine it being the other way around, as Fintech means financial technologies. And AI for Fintech is no exception. The rise of financial technologies has visibly grown over the last couple of years. Traditional banking will eventually find its way into history, as Fintech companies’ revenue will increase. 

AI in Fintech can predict user behavior by integrating a combination of AI tools for Fintech and Machine Learning algorithms, which allows users to get detailed insights into their data. Such tools are also commonly known by the name of AI APIs and are used by Fintech companies.  

The Benefits of AI in Fintech

There is a great line of similarities taking place between AI and Fintech. AI in the fintech market is bringing back the personal approach to banking, previously lost due to the prevalence of transnational banks. Here are a couple of ways how AI for Fintech can improve your business.

  1. Credit Risk Inspection

Credit scoring evaluates the customer’s success and ability in paying off debts. AI Fintech companies use AI-powered credit scoring to make the calculations more accurate. Ranking one’s ability to pay debts requires lots of data to be processed, including total income, credit history, transaction analysis, and work experience. Artificial Intelligence can not only provide data-based, individual and reliable scores, but it can also analyze real-time metrics to give more customers access to additional finances if they need it. 

  1. Banking Fraud Elimination

AI in the Fintech market can detect specific behavior patterns and actions which differ from normal, and monitor them in real time. This allows acting on fraud attempts quicker and more efficiently, saving the staff time.

  1. Increasing Safety

Artificial intelligence in the financial world is the driving force behind many solutions to improve security. Face and fingerprint recognition are only two of many examples of how AI in Fintech can protect the company and user data.

The Value of AI in Fintech

As the digital world is flooded by more and more users every year, the only reasonable decision for a bank would be to join them. Alongside the value of real-time monitoring and enhanced data sensitivity, AI for Fintech solutions can improve customer support and decrease the workload. The worldwide AI in Fintech market was valued at e.g. $8.23 billion in 2021.

AI-powered analytics provide AI Fintech companies with more revenue sources and savings on operations time. In an industry where data is essential for predicting customers’ next steps and optimizing the workflow, AI tools are the key. Speaking about numbers, more than 67% of the zest Fintech market believes that AI will overtake the industry in the near future. 

Statistics show that more users come to banks for personalized finance solutions, yet, only 45% of customers around the world have access to a personal assistant in banking. AI solves this issue by giving the user sensitive and reliable analytics of their finances and future income in real-time, not only expediting the process but also reducing the need for additional staff. 

AI algorithms do not tolerate mistakes. Fintech AI companies are benefiting from it by reducing security leaks, fraud, and errors with Artificial Intelligence Fintech detection tools. 

Solving Problems of Artificial Intelligence in Fintech 

The uniqueness of Fintech industries raises the logical question: can AI alone help with all possible issues?

 –        Data Scaling and Sensitivity

The issue: Both banks and Fintech companies generate and process huge amounts of data every day, including investment data, financial data, networth, cash flow, assets, liabilities, and credit card transactions, alongside other client demographic data. 

Solution: The cooperation between Fintech and Data Science. Combining Data Science and Fintech during the development of AI solutions helps with a data-sensitive approach. Data Science in Fintech allows to structure and organize all data going through the company, as well as to guide it into the needed channels so AI can process it correctly and effectively. 

–       Success Metrics May Differ Between the Banks and Companies

The issue: Within the Fintech sphere metrics are not just data to be calculated. They may differ relying on various factors and the company’s objectives, such as daily active usage and engagement metrics. For example, users who are interested in understanding their long-term financial plan, may not have the habit of checking the financial app every day.

The solution: Data Science for Fintech shows great results in identifying key metrics and patterns of data incoming. Using Data Science and Financial Technology reduces the risk of Fintech and AI tools losing touch with the needed data. 

Opportunities of AI in Fintech

Artificial Intelligence and Fintech when combined can provide the company with plenty of opportunities . Besides already covering fraud prevention and credit scoring, AI for Fintech has a list of other functions such as:

–        Savings and income management

Fintech AI companies use AI-powered assistants to help clients make smart purchasing decisions. This allows the client to feel a personalized approach, while not requiring time and effort from employees. 

–        Convenient data analytics

Data quality is the priority for most Fintech companies, and AI suits this objective perfectly. With quickly delivered insights on metrics changes, market reports and user activity, fintech firms can increase their revenue as reacting to any changes in numbers becomes more convenient.

Additionally, AI does a great job analyzing unstructured data if trained correctly. In Fintech, this can be applied in several ways: first of all, for predicting the product propensity and adapting the marketing strategy by analyzing customer behavior. 

–        Cutting on expenses

After all, using one AI tool is way less expensive than hiring a whole team to collect and analyze data, assist customers with the best financial choices and track all metrics. 

Summary of Using AI in Fintech

AI for Fintech is one of the most promising technologies in the field, with 67% of companies considering it to have a huge impact on the industry in the upcoming years. 

The main reason why AI gains so much attention and usage is, of course, automation, as using instruments powered by it results in boosted productivity. 

–        AI efficiently detects fraud, which has always been a problem in banking, preventing clients from trusting in the system. 

–        AI-powered NLP (natural language detecting) tools provide companies with the opportunity to quickly reach clients through automated chatbots. 

–        The implementation of AI in fintech requires a strong team of engineers with corresponding required expertise. Ideally engineering teams should be paired with the data scientists teams to improve the final results of AI training and deployment.


How is AI used in Fintech?

AI in Fintech is used to detect fraud through abnormal activity monitoring, credit scoring, and personalized finance assistance, as well as for improving the safety of all data stored in a Fintech company.

What is AI Fintech?

AI in Fintech can predict user behavior by integrating the combination of AI tools for Fintech and Machine Learning algorithms, which allows users to get detailed insights into their data. Such tools are also commonly known by the name of AI APIs and are used by fintech companies.

How is AI Used in Finance?

Artificial Intelligence Fintech is used to create chatbots, ensure safety and efferently analyze all data and metrics in real-time.