Decentralized finance or DeFi is a blockchain-based financial system that uses cryptocurrencies and blockchain to manage financial transactions instead of relying on central financial intermediaries such as banks, brokerages, exchange houses, etc.

DeFi is growing at a very fast phase. The industry has reached $ 100 billion and its users are over a million now, which shows people’s motivation and enthusiasm to participate in such a system.

With such a surge in popularity and adoption rate, people are wondering if DeFi may be the next big thing in global finance. In this article, we’ll take a look at the reasons why decentralized finance can disrupt traditional banking.

Accessibility 

Unlike traditional banking, Decentralized Finance offers users simplified and easier access to banking services. It also removes barriers by eliminating the need to share personal information before accessing services.

For example, with the traditional banking system, a person in need of a loan would have to go through a bureaucratic process and waste a lot of time, however, in the case of DeFi, such operations require only several clicks and are accessible from anywhere and at any time. This approach not only makes the process seamless but also affordable, giving users full control of their assets.

Transparency

Because DeFi is powered by blockchain technology, all transaction data is transparent and available to everyone. Such auditable nature of DeFi allows parties to properly assess risks and inspect portfolios

Therefore, DeFi’s open source infrastructure is always available for audit, with every transaction recorded on the blockchain. This not only serves as an important foundation for financial markets but also plays an essential role for investors.

By understanding what types of transactions are taking place, DeFi provides its users with a high level of trust, a sense of security and authenticity.

Exchange  

DeFi simplifies the exchange process as it involves peer-to-peer transactions without the need for a middle man or middleman. DeFi platforms are equipped with machine learning algorithms that help detect fraud and identify other risk factors.

With the help of smart contracts and analysis on loan sources, credit levels, etc., DeFi platforms speed up the exchange and lending process between two parties. That’s why DeFi exchange and lending is one of the fastest-growing use cases in the industry.

Innovation

Decentralized finance is one of the most innovative industries. Built on blockchain technology, DeFi solutions are innovative by nature. However, in addition to that the industry is constantly looking to expand its capabilities and offerings with innovative solutions.

Exactly such groundbreaking thinking made DeFi a perfect use case for doing everything from lending to insurance. And as the decentralized finance becomes more attractive to large investors and businesses, we are more likely to see more innovative and complex uses of this space.

Conclusion

Decentralized finance is receiving increased recognition from users, as well as potential partners, such as international payment providers. It has grown so much that even traditional financial institutions recognized the potential of DeFi.

Although DeFi is still considered a new and experimental solution, everyone agrees that DeFi is here to stay and has the potential to disrupt traditional banking. 

As in most industries, we see a growing impact of artificial intelligence in the financial sector.

The use of AI technologies has helped banks, fintech, and other financial institutions streamline and optimize operations such as credit risk management, trading, fraud identification, money laundering prevention, and more.

Due to the automation and process improvement that AI brings to the industry, it is no wonder that 70% of all financial services companies use AI and ML in their operations.

With all that in mind, let’s find out what are the main use cases of AI solutions for the financial sector and how businesses can benefit from the implementation of this technology in their companies.

Financial document data extraction 

Today, the financial sector is bombarded with a myriad of documents related to transactions, data security, government and corporate regulations, and much more.

The burden of managing so much paperwork represents a significant challenge for financial institutions as it is costly and time-consuming work, forcing staff to perform repetitive and demotivating activities.

However, with the help of AI, deep learning, and computer vision, financial firms can make manual data handling a thing of the past.

Automating manual data handling with AI means allowing the system to identify and extract all the key data from financial documents at a much faster speed, with greater precision and at a lower cost.

AI chatbots

Quick service is important for your business to stand out in the financial industry. Customers may be interested in opening an account, making a transaction, or depositing money. 

Providing fast assistance to your clients means they can get on with their errands with the least amount of time wasted, which is the key to not only retaining existing clientele, but also attracting new ones.

AI chatbots are a great way to not only serve your customers quickly, but also provide 24/7 support. 

AI chatbots are fully independent virtual agents that use machine learning and natural language processing technologies to answer questions based on predefined scripts and learn from new interactions.

In this way, you can automate the routine request about credit card blocking, balance inquiries, card limits, payment problems, etc.

Customer churn prevention 

Another essential part of customer service in the financial industry is churn prevention, as it identifies customers with a high probability of leaving the company based on their past behavior and historical data.

Businesses began to pay more attention to it, since acquiring new customers can be 5 to 25 times more expensive than retaining existing ones.

By using data related to customer cash balance, transactions, credit information, and then applying machine learning algorithms, financial firms can predict client churn with high accuracy and guarantee customer retention with data-backed decisions and promotions.

Fraud prevention 

Unfortunately, even today, fraud is identified after the incident occurs and in most cases when it is too late. In the financial industry we still hear stories of unauthorized money withdrawals, check forgery or use of stolen credit card information.

However, AI-powered fraud prevention can help banks fight the problem effectively. Machine learning models can analyze data in a matter of milliseconds and review spending history and behavior. 

In this way, the models can instantly learn and detect any kind of irregularities, such as the use of cards from different countries in a short period of time.

All these operations would be impossible for a human fraud analyst to perform as quickly as artificial intelligence systems do, making the solution more effective against fraudulent activities.

Trading algorithms 

Similar to the example mentioned above, in trading too, AI can analyze a large amount of data, detect patterns, and execute trades with high precision and speed. On top of that, as the amount of financial data increases, AI algorithms are getting smarter and smarter. Therefore, when it comes to trading, AI is a more profitable solution for financial institutions due to its ability to multitask and make accurate predictions.

Conclusion 

The ability of AI to power and automate operations is invaluable to the financial industry. Many financial institutions began using AI to predict results, improve the customer experience, and take their services to the next level.

As artificial intelligence in the financial market is expected to reach $ 26.67 billion in 2026, it is important that banks, fintechs, and other companies follow the trend and keep up with the competition.

If you want to incorporate artificial intelligence into your business, schedule a free consultation with our AI experts and let’s discuss the potential of our partnership and how our solutions can drive your projects.

Anyone with a good understanding of the banking industry knows the crucial importance of data entry and data extraction for financial institutions.

Precisely with data entry, financial organizations can keep all relevant information in one place so that it is available quickly and efficiently. In this way, these institutions can create bank statements, loan agreements, credit and debit reports, etc.

However, all those who have had to work in the banking sector are also aware of the inconvenience and difficulty that data extraction, an important but repetitive task, can cause.

And it is very likely that they know it first-hand since this is a task that almost every bank worker has had to deal with at some point in their career.

And what happens when tedious and unwanted duties collide? Increases the probability of human error.

This has posed a serious problem for the financial sector, as data entry and extraction is a major operation. Therefore, the industry has been trying to find a perfect solution that minimizes human error and takes the stress out of work.

Challenges of manual data entry and extraction:

• Repetitive and demotivating – because extracting data is repetitive and generally not very interesting work, it can lead to bad working habits such as distractions, keyboard shortcuts mistakes and typos.
• High probability of inaccuracy – due to the high number of documents that have to be handled each day, this work requires strict adherence to the rules and protocols that govern it. This often leads to data entry errors can lead to significant financial problems and reputational damage if they are not detected in time.
• Costly – because the data entry process takes time, financial organizations have had to hire additional staff. As a result, they have increased their operating costs and limited themselves in terms of investment opportunities.
• Time-consuming – not only does this task take a long time, but it usually requires the help of more than one person. And when you factor in all these constraints, the need for a rapid and precise extraction tool is obvious.

AI as the perfect answer to financial data management

The fact that automation can help increase a company’s productivity is no longer news.

However, automating tasks with an intelligent system that does not require human intervention but can mimic and even surpass human performance is what has revolutionized companies today.

Exactly with the help of subsets of AI – Optical character recognition (OCR); Deep Learning and computer vision –  financial companies can address the problem of manual data handling.

Before we get to how it’s done, let’s define all these subsets:

• OCR – A technology that recognizes and transforms text from digital documents, such as photos, into machine-readable text data.
• Deep learning – A subset of AI motivated by the human brain that mimics it with artificial neural networks to solve complex problems with large amounts of data.
• Computer Vision – a field that deals with the way computers see and understand images and videos. Computer vision includes OCR but also incorporates additional features such as facial recognition.

Now that we know what each of them represents, let’s look at the detailed process of how these technologies address the challenge of data extraction.

1. The first step for the AI-powered machine to work efficiently is to provide it with the necessary data of texts, letters, figures, numbers, etc. This creates a kind of character dictionary for the system that is later used as a reference.
   
2. For the next step, the desired document is uploaded to the system and the system tries to identify the characters that are present in the file. This is done by analyzing each symbol for its characteristics, such as curves, corners as well as other details and referencing it with the system´s database.
   
3. In addition to the symbol dictionary, the system also understands words as a whole. Therefore, if some letters are difficult to identify, the OCR system checks its dictionary of words to find the closest match.
   
4. The intelligent system can also identify the type of document (financial statement, invoices, annual reports, etc.) and customize its information extraction process, increasing the precision of the extracted data.

What are the results and benefits of this process? 

• Increased document processing capacity – The process we mentioned above takes several seconds for a good AI-powered system. Imagine the number of documents that you can digitize and extract data from in a matter of minutes.
  
• Happier employees – Employee satisfaction became one of the main objectives of businesses since it is directly related to the performance of the company. Freeing your staff from uninteresting tasks and giving them meaningful work will benefit your entire organization.
  
• Greater precision in data extraction – Automating the data extraction process eliminates human error and provides greater accuracy for businesses.
  
• Cost reduction – Financial companies are one of the leading organizations engaged in document handling.  The costs associated with the artificial intelligence system and automated document data extraction are justified and more profitable in the long run.

Conclusion

The financial sector should pay more attention to recent innovations in the industry, as they can be a turning point in terms of gaining a competitive advantage.

One of the most important innovations is the inclusion of AI to streamline and optimize various operations such as data entry and extraction. 

The use of artificial intelligence in a banking context can make a significant difference by allowing accurate and fast data extraction without the need for additional staff.

This technology also allows users to extract only the required information from documents, saving time that would otherwise be spent sifting through large amounts of data. And ultimately, the incorporation of AI into this industry will

And while it may seem like a big compromise, in reality, it could prove to be a cheaper solution that makes the document data extraction process more accurate, faster, and can free up your staff from uninteresting labor.