Artificial IntelligenceFintechMobile App DevelopmentTechnology

AI – Artificial Intelligence in The Finance Industry 

artificial intelligence ai in finance industry

Fintech is one of the industries that is skyrocketing due to the growing number of internet users. To increase the speed, security, and scalability of the financial industry, several technologies function in the background. One of the technologies that have significantly changed the financial industry in 2023 and beyond is artificial intelligence (AI). 

Financial organizations are focused on leveraging AI, which would be introduced in areas such as mobile banking, customer experience, cyber security, social banking, payments, branch automation, and operational efficiency.

Due to its remarkable advantages, such as more effective business operations, superior financial analysis, and more consumer engagement, artificial intelligence (AI) and machine learning (ML) are increasingly being used in the finance industry. 

Artificial intelligence is not going out of trend anytime soon. But, what are the best use cases of AI in the fintech industry, how does it change the finance industry, and how can you profit from this new technology? Let’s find out!

This blog will address the technical aspects of bringing AI/ML to the finance industry and outline every aspect of AI in the finance industry.  But before proceeding further, please go through the interesting stats. 

So, without any further ado, let’s get started!

Exciting Stats You Should Know!

According to the report, the AI in the BFSI market, estimated at $3,091.9 million in 2018, is expected to advance at a CAGR of 37.2% during the forecast period 2022–2024.

AI in BFSI Market

Source– psmarketresearch

  • The AI industry will increase by over 13x over the next 8 years. 
  • The US AI market is forecast to reach $299.64 billion by 2026. The AI market is expanding at a CAGR of 38.1% by 2030. 
  • According to experts, the AI market will grow to $190 billion until the year 2025.
  • By 2025, as many as 97 million people will work in the AI space.
  • AI market size is expected to grow by at least 120% year-over-year.
  • According to reports, global spending on AI is projected to be more than $110 billion by 2024. 

AI Finance Industry: What & Why?

The finance industry may benefit from AI by achieving its growth goals, gaining a competitive edge, and being more relevant to its customers. It can indeed also assist them in lowering operational expenses and streamlining internal procedures. By practicing better personal money management, users can gain from this technology. 

According to reports, approximately 80% of banks are aware of the advantages of adopting financial AI. The epidemic accelerated the banking sector’s quick adoption of cutting-edge technologies.

Benefits of Artificial Intelligence in Finance

Below is a list of the top four benefits of artificial intelligence in the financial and banking industry.

01- Better Customer Experience

Comfort is always desired by customers. For instance, the ATM was a success because customers can access a vital service even when the bank is closed. More innovation has been sparked as a result of this level of convenience. Customers can now open bank accounts and verify themselves from the comfort of their sofa using their smartphones.

In the pursuit of faster response time, a decision management system (DMS) can reduce the time it takes to capture Know Your Customer (KYC) information and eliminate errors. Furthermore, with the right business rules software, business decisions can be implemented and deployed without lengthy procedures.

New products and seasonal financial offers may be available on time. Also, new business decisions or rate changes are easily adapted to the system.

Eligibility is automated meaning, customers who don’t qualify don’t get frustrated going through an entire process only to be turned away. Even though there are many different kinds of customers, this kind of technology gives the impression of a personal touch.

Banks can earn customer trust by reducing response time. Additionally, DMS software can reduce facility approval times.

Sometimes bank employees open accounts by mistake, leading to restrictions on customer accounts. That can be very frustrating for a client. Accurately capturing customer information and setting up customer accounts correctly ensures a seamless experience for your customers.

02- Reduce Human Error

Every financial institution has to deal with human error. In one of the largest studies evaluating breaches among more than 130 customers, the IBM Cybersecurity Intelligence Index reported: Human error was a major contributing cause in 95% of all breaches. Whether the mistake is made within data analysis or a misclick on a phishing email, every breach leaves your business vulnerable to a breach.

Financial institutions can use an AI system in conjunction with a SOC team that monitors alerts and then analyzes fraud, risk, and compliance threats to decrease workload and run more efficiently. Automated systems are the first line of defense with abilities beyond the limitations of human capability, reducing the incidence of errors.

03- Enhanced Evaluation of Loans and Facilities

The use of credit scores to assess eligibility for financing is often based on out-of-date information, misclassification, and errors. However, there is now much more information available online that can provide a more realistic picture of the person or business being evaluated.

An AI-based system can give a pass or fail recommendations by considering more variables even when the party, whether personal or business, has little documentation.

The trick is that it’s not always clear why the software presents a particular recommendation. No one inquires after an application is accepted. However, when an application is denied, the institution owes the client an explanation.

Despite their objective design, the systems may exhibit bias. This is because the configuration is only as good as its developers. Fortunately, institutions receive similar funding requests and are aware of institutional bias. As a result, developers are better positioned to input better variables when designing apps and updates.

04- Improved Investment Evaluation

Interest income is only one facet of generating income. As a result, banks are continually looking for lucrative opportunities to invest and earn a healthy return.

Investment recommendations that are in line with these institutions’ risk tolerance can be made with the right investment software. In addition, they can accurately evaluate client financing proposals, as industry-specific information is often difficult to understand.

Human analysts are still in charge of making investment decisions. Investment analysis software makes the process simpler and can handle more variables. If the institution has interests outside its national borders, access to information can take a long time. Assessing a new environment can be challenging, but the right AI software is critical to speeding up the process.

05- Regulatory Compliance and Fraud Detection

The banking industry has had a colorful past that has cost investors millions of dollars. Legislation such as the Sarbanes-Oxley Act of 2002 (SOX) provides heavy penalties for players who violate the rules. Therefore, it is in the best interests of banks and financial organizations to automate compliance where possible.

The use of a decision management system allows for early detection of fraud and full audit documentation. When employees are required to leave their desks in order to provide missing information or explain entries during third-party audit exercises, this can disrupt regular business operations. With the right software and machine learning, the information captured in the system will be accurate and errors will be immediately highlighted or rejected.

As financial institutions increase their vigilance, scammers change their behavior. Since large-sum transactions are flagged for investigation, scammers have learned to deal in amounts just below the detection limit. Without proper analysis, criminal activity may go unnoticed despite meeting prescribed requirements.

In this regard, artificial intelligence truly outperforms humans. Large amounts of data are analyzed by artificial intelligence, which then detects suspicious transactions. Manual analysis of such transactions leads to errors. Without an AI fraud detection system, it’s a field day for criminals to launder money or finance illegal activities.

Applications of AI in Finance

We first mention some of the key areas within the financial industry where artificial intelligence is having the biggest impact and provides additional value over traditional approaches.

AI in Finance

01- Fraud Prevention

Another crucial area where artificial intelligence can have a big impact is fraud prevention. Fraud, we understand any fraudulent activity such as credit card fraud, money laundering, etc. The former has been growing exponentially in recent years due to the growing popularity of e-commerce, the number of online transactions, and third-party integrations.

In the past, organizations used to combat fraud with hard-coded rule sets designed by domain experts. However, the potential danger lies in scammers finding out the rules and then being able to exploit the system. That is not the case for AI-based solutions, which can evolve and adapt to new patterns found in the data.

Many machine learning algorithms specialize in anomaly detection and excel at detecting fraudulent transactions. Such an algorithm can filter thousands of transaction-related features and trigger a warning when something appears to be out of order.

While many traditional AI techniques can already achieve reasonable performance, the industry is constantly pushing for improvements. That’s made possible by more complex algorithms that scale better for large volumes of data.

02- Credit Scoring

One of the crucial applications of Artificial Intelligence in the financial industry is credit scoring. Many financial institutions, whether they are large banks or smaller fintech companies, are in the business of lending money. And to do so, they need to accurately assess the creditworthiness of an individual or another company.

Traditionally, such decisions were made by analysts after conducting an interview with an individual and collecting the relevant data points. However, artificial intelligence allows for faster and more accurate assessment of a potential borrower, using more complex methods compared to scoring systems of the past. To do so, advanced ranking algorithms use a variety of explanatory variables to arrive at the final score that determines whether the person will receive the loan.

An additional benefit of AI-based scoring systems is the potential for unbiased decision-making: there are no human factors, such as the bank employee’s mood on a given day, or other factors influencing the decision. Additionally, it could benefit people without extensive credit histories, allowing them to demonstrate their trustworthiness and ability to repay the loan independently.

03- Algorithmic Trading

Probably nowhere is the adage time is money more relevant than in trading, as faster analysis means faster pattern identification leading to better decisions and trades. By the time some type of pattern is identified and the market reacts, it is too late to act and the opportunity is gone.

That is why so much effort and money are invested in algorithmic trading, that is, complex systems that make split-second decisions and execute trades autonomously based on the identified pattern. Such systems can greatly outperform human traders, also considering that they are not affected by emotions. A Mordor Intelligence report indicates that approximately 60-73% of overall US stock trading was handled by some form of AI-enabled system.

Algorithmic trading systems combine state-of-the-art developments on the machine and deep learning from various fields. While certain parts of these systems may focus on trying to predict asset returns, other components may use a more traditional approach based on econometrics and asset allocation theory.

Something that is gaining a lot of traction recently is the use of alternative data sources to gain an advantage over competitors. Advances in object recognition can help analyze satellite imagery, while the latest techniques in Natural Language Processing (NLP) enable accurate sentiment identification from sources like Reddit, news articles, Twitter, etc.

Algorithmic trading is also gaining popularity among individual data science professionals, who are trying to create their trading systems, either on their local machines or in the cloud. With recent changes in how easy it is to start trading and the increasing availability of APIs from various brokers, there are only more and more people willing to give it a try.

04- Robo-advisory

Since inflation is affecting our savings and it is no longer profitable to keep money in a savings account, more and more people are interested in passive investing. And this is exactly where robo-advisors come into play. They are wealth management services in which AI makes portfolio recommendations based on individual goals, risk preferences, and available income of the investor.

The investor only needs to deposit the money every month and everything else is handled for them, from choosing the assets to invest, buying them, and then potentially rebalancing the portfolio after a while. All of them are to ensure that the client is on the best possible path to achieve their desired goals.

The main advantages of these systems are that they are very easy for clients to use and do not require any financial knowledge. Naturally, cost also plays a role: robotic advisors tend to be cheaper than the services of human asset managers.

05- Process Automation

Lastly, AI has a lot to offer in terms of automation. Using cutting-edge optical character recognition (OCR) technology has the potential to significantly boost the productivity of routine and time-consuming tasks that employees typically carry out. For instance, document digitization, form processing, or the extraction of relevant information from documents.

For the KYC process, many financial institutions use specialized software or develop in-house solutions. To prevent fraud, it is frequently required in finance to present some form of identification. The procedure is made very simple by many neo-brokers and fintech businesses: After scanning your ID with your phone, you take a selfie to make sure it matches.

An AI-based solution checks in the background to see if there is a match. It also checks to see if the ID is real and if the image has nothing alarming. Deep Learning and architectures like Convolutional Neural Networks (CNNs) show very promising results when used with images.

06- Personalized Banking Experience

The banking industry is trying to harness the power of AI to provide a personalized banking experience for everyone. An example could be chatbots, which are becoming increasingly difficult to distinguish from real human consultants. Using advanced NLP techniques, they can understand the client’s intention and try to steer him in the right direction. For example, they can help users change their password, check their current balance, schedule transactions, etc.

Furthermore, such chatbots can often recognize customer emotions and adjust their response based on them. If they detect that the consumer is very angry, it might make sense to connect them with a human consultant to try to fix the problem as soon as possible and avoid further frustration. The increasing proficiencies of intelligent chatbots also enable cost savings by reducing the workload of call centers.

Personalized Banking Experience

But chatbots aren’t the only personalized experience in finance. Many institutions take advantage of the vast amount of data they have to analyze consumer spending behavior and provide personalized financial advice that can help them achieve their goals. Institutions may also inform you that some recurring transfers are coming soon and that you do not have enough funds in your account. All these are just the tip of the iceberg of what modern financial companies can offer their clients.

Top 11 Use Cases of AI in the Fintech Industry! 

In today’s era of digitization, staying updated on technological advancements is a necessity for businesses to both outsmart the competition and achieve desired business growth.

 It is less than 2 mins and summarizes how can Artificial Intelligence & Machine Learning be used in Finance. 

ai use cases in finance and banking

We hope this helps you learn more and save your time. Cheers!

The top 11 use cases of artificial intelligence in the fintech industry are listed below. So, take a look!

  • The rise of AI-enabled chatbots
  • Virtual data analytics
  • Robo-advisors
  • Predictive analytics
  • Process automation
  • Credit scoring
  • AI-enabled churn prediction solutions
  • Quantitative trading
  • Fraud prevention
  • Custom banking solutions
  • Personalized finance

 Let’s discuss each use case of AI in the fintech industry in more depth!

#1. The rise of AI-enabled Chatbots

The rise of AI-enabled Chatbots

Businesses operating in the financial sector are now able to provide clients with assistance around-the-clock by incorporating custom AI solutions, such as smart chatbots and virtual assistants. These virtual assistants may respond to a wide range of questions and even aid users with different financial tasks or problem-solving. The benefits of AI chatbots continue to grow since they can also provide clients with individualized financial advice, help them keep track of their costs, and eventually help them achieve their financial goals. Best Practices and Things to look out for in a Chatbot Development Company or a Chatbot Technology Partner.

#2. Virtual Data Analytics

Virtual data analytics

For financial service providers, virtual data analytics is the ideal use case of AI in the fintech industry. In a single day, around one billion transactions may take place.

Financial institutions must perform time-consuming manual operations such as information gathering and record keeping. 

The real potential of AI-based mobile apps created for the banking sector is their ability to upload, gather, and analyze massive data sets in a matter of seconds. Banking software can monitor transaction history and detect fraud if any, thanks to AI technology.

#3. Predictive Analytics

Predictive analytics

Another competitive benefit of adopting AI tools in the finance industry is predictive analytics. Increasing sales, resource use efficiency, and operational effectiveness are all beneficial.

Businesses can efficiently collect and analyze enormous data sets using machine learning algorithms to quickly and accurately predict future financial market movements.

They can provide consumers with tailored solutions as a result. 

Predictive analytics can help CFOs to use the existing data and identify trends for more accurate planning, forecasting, and decision-making.

#4. Robo-advisor


Robo-advisory software presupposes a digital platform where users can avail themselves of automated and algorithm-driven financial planning services with little or no human intervention.

Robo-advisors are not only less-priced alternatives to conventional financial advisors, but they may also help a huge number of consumers receive financial counseling and make better financial decisions.

Additionally, data-driven AI-powered Robo-advisors can offer investors advice on estate planning, retirement, and other topics, which can make the account opening process more engaging.

build your fintech application with ai

#5. Process Automation


Automating tedious manual operations has been one of the main advantages of technological development. We can improve our capacity to automate even the most difficult tasks with the aid of AI. For instance, judgments regarding loan approval can be made by the algorithms working in the background without the need for human involvement. 

The traditionally time-consuming process of signature matching for check approval has been fully automated. As a result, the process is quicker and less likely to make mistakes, lowering operational costs. The capability to use artificial intelligence to automate KYC compliance and anti-money laundering procedures is yet another use of AI in the fintech industry. 

#6. Credit Scoring


Whether it is for consumers or lenders, credit is one of the key components of financial services.

Finance institutions can use AI solutions to more accurately assess a consumer’s creditworthiness utilizing a range of indicators than they could with a traditional tool. Banks and credit lenders can make wiser judgments and streamline their decision-making process as a result. 

If you are planning to take a loan for the first time and are worried that you will not have the desirable credit score, then AI financial analysis is there to help you out.

#7. AI-enabled Churn Prediction Solutions


Another powerful use case of AI in the finance industry is the forecasting of prospective preferences changes among loyal clients when it comes to their financial service providers.

Financial firms will have a significant advantage if they can anticipate such a change since it will allow them to keep devoted customers from switching to a rival for financial services.

Artificial intelligence-powered churn prediction solutions entail analyzing customer behavior together with other data and turning it into estimates that can help organizations keep their most devoted customers.

#8. Quantitative Trading


Quantitative trading is the art and science of detecting recurring patterns in historical data and using the identification of said patterns on future data for predictions.

Using big data sets to identify patterns that can be used to make strategic bets is known as quantitative trading.

Financial organizations may evaluate any number of complicated data sets more quickly and effectively with the use of custom AI solutions.

By automating the deals, this algorithmic trading method can assist the service provider to save precious time and resources.

#9. Fraud Prevention


The finance industry urgently requires good cybersecurity and fraud detection measures.

By evaluating thousands of data points in real-time and reporting suspect transactions or fraudulent claims before they cause any form of loss for the business, AI systems can assist to increase the security of digital transactions and online activities. 

Banks are finding that AI for fraud detection is fast, effective, and efficient. 

Banks have a secret weapon at their disposal that can be used to help identify and prevent fraud: artificial intelligence (AI).


#10. Custom Banking Solutions


Consumers who are familiar with technology go beyond traditional banking methods; they desire to keep track of their finances while on the road and make modifications to their spending immediately.

Chatbots powered by AI and natural language processing could be useful for facilitating this trend by providing individualized financial advice and immediate assistance.

Financial institutions can also offer their clients improved wealth management information in this way.

#11. Personalized Finance

Personalized Finance

Using AI-powered chatbots, machine learning-based fraud detection solutions, or personal finance apps based on predictive analysis, AI is already transforming the banking sector.

In personal finance apps, artificial intelligence (AI) examines a user’s purchasing and spending patterns to determine their spending habits and offers suggestions for improving their spending behavior. 

When clients download banking apps, AI algorithms collect and analyze consumer information. Based on the data, relevant pre-approved products and personalized financial advice can be recommended.

Future of AI “Artificial Intelligence” in the Finance Industry 

With AI becoming more pervasive across industries, it’s no surprise that it’s taking off in the world of finance, especially as COVID-19 has changed human interaction. By simplifying and consolidating tasks and analyzing data and information much faster than humans, AI has had a profound impact. Experts predict that it will save the banking industry around $1 trillion by 2030.

Artificial intelligence technologies are becoming more integral to the world we live in, and banks must implement these technologies at scale to remain relevant, according to McKinsey & Company. Success requires a holistic transformation that spans multiple layers of the organization.

It is also important to note that millennials and Gen Zers are becoming the largest consumer group for banks in the United States, which means that financial institutions are looking to increase their IT and AI budgets to comply with digital standards. higher, as younger consumers often prefer digital banking. In fact, 78% of millennials say they wouldn’t go to a bank if there was an alternative.


Artificial technology, which has modernized the financial industry over the past ten years, has caused a number of significant changes in the finance industry. 

Artificial intelligence in the fintech industry has the ability to supplement human intellect to improve financial decision-making and can also enhance internal organizational procedures. When it comes to the Customer Experience, this is so crucial. 

Adopting smart Artificial Intelligence solutions is demonstrating encouraging outcomes in terms of increased production and efficiency, and they also hold enormous promise for the future.


Financial services providers gain a competitive edge in the market and are better equipped to address all of their customers’ needs by integrating AI into various organizational processes.