An illustration of a bank that contains a chat bubble represents the the application of AI in Banking. The accompanying text reads, 'A New Era of BFSI Sector.'

Advanced Application of AI in Banking | A New Era of BFSI Sector

By Nivin Biswas Category Artificial Intelligence Reading time 10-15 mins Published on Nov 23, 2022

Know How AI in Banking and Finance is Leading Us to Super Secure and Worry-free Banking Experiences!

Standing in long queues for a banking service or waiting a week to do a money transfer is now like an olden days story.

You need just a few clicks to do the above actions. Visiting a bank is a very rare story now. In fact, we can expect that in the next 15 years, banks will have to cooperate with offices only, and public relations will be entirely virtual. But how's that possible?

It's nothing but the magical power of artificial intelligence (AI).

We can all agree that AI has developed to the point where we can confidently foresee its future growth and eventual success. With such development, it has significantly impacted the banking and finance sectors. In addition, AI in banking and finance has provided the BFSI sector with significant security, convincing, and management support.

It is not only providing better methods to handle data and improve customer experience but also simplifying, speeding up, and redefining traditional processes to make them more efficient. By offering higher-quality products and services, this transformation benefits banks and their customers.

A robot hand holding a bank model represents applications of AI in banking.

Why is the application of AI in the banking and finance sector mandatory?

Data has always been a valuable asset to financial service companies, but with the introduction of new technologies such as artificial intelligence, data is now more critical than ever before. To be successful, banks need to use AI to create efficient and personalized products and services instead of relying on their size alone. This shift in focus will undoubtedly lead to more successful businesses in the BFSI industry.

Top 4 Advantages of AI in banking and finance:-

Financial analytics:-

AI is a valuable tool that can provide finance companies with greater insight into their financial data. This will, in turn, help them plan more constructive and beneficial approaches for both themselves and their customers.

Faster functionality:-

Application of artificial intelligence in banking can operate considerably and more rapidly than humans because it is based on real-time data. This implies that it can conclude far faster than a human, which might take minutes or hours. In a matter of seconds, automating in accordance with the requirements can provide results and predictions. Here, the problematic choice can also be made very quickly.

Reduced expenditures:-

Using artificial intelligence in banking has several advantages, but one of the biggest is that it can potentially cut expenses. Businesses can reduce labor expenses by automating jobs and decision-making processes. AI can also increase business productivity and efficiency, resulting in further cost savings.

Managing enormous data:-

AI brings many benefits to the finance industry, one of which is its ability to handle large data sets. As a result, it's a valuable tool for financial analysis since it can handle massive amounts of data far faster than a human could.

A smartphone display shows a  mobile banking app powered by AI.

9 sorts of applications where AI has played a prominent role in advancing the banking and finance industry:-

1. Personalized Banking Solutions:-

Personalization is a powerful tool that can be used in all industries, and it is incredibly impactful in industries where the customer experience is key to success. Hence AI-powered personalization in the banking sector is in huge demand.

AI and machine learning are two examples of advanced cognitive technologies that are assisting banks in enhancing their TPRM programs. These technologies help banks identify risk more effectively, anticipate it, and adapt more quickly to rapidly changing regulatory requirements by automating the manual process.

Using a recommender system, banks can typically filter customers based on need based on moderation policies. Essentially, it recognizes users and customers who have made previous purchases and suggests the same products they are likely to buy.

Evidently, this personalized customer experience enhancement in banking aids in adopting communications and catering to a specific customer's preferences.

2. Fraud detection:-

Blockchain has been garnering a lot of attention for its potential for fraud prevention. The blocks' transparency and immutability make it so difficult for fraud to occur. ML tools appear to be the best option for successfully tracing fraud. Of course, you can use both technologies for the best results!

Banks can use unsupervised algorithms like KNN to detect fraud. Trained with historical data, they can identify unusual patterns and help humans quickly review potential fraud cases. However, manual verification is still necessary to confirm that a potential case of fraud is actually fraudulent. By lowering the number of errors that need to be examined by humans, algorithms can help save time and resources.

3. Forecasting to Increase Bank Incomes:-

There are a number of forecasting techniques to boost bank income consistently, including-

  • Simple Moving Average (SMA)

  • Exponential Smoothing (SES)

  • Autoregressive Integration Moving Average (ARIMA)

  • Neural Network (NN)

  • Predictive algorithms

But the banks preferably used predictive modeling for a long time to evaluate risk in loan underwriting and fraud detection. However, what predictive modeling can accomplish for banks is only getting started. There are lots of room to explore and invent.

A man whose face is being scanned using artificial face recognition technology.

4. Artificial face recognition:-

While discussing the benefits of AI in banking and finance, not mentioning the AI face recognization tech might be an injustice to the readers. The technology behind this computerized vision-based payment system is actually quite simple. In short, the payment terminal scans your face and sends the template to an interpreting device. This device then compares your face's template with the verified template on file from your bank. From the customer's standpoint, this new system provides a great deal of convenience since the transaction no longer requires a mobile phone or credit card. Despite these benefits, the technology remains controversial due to the potential field for mass surveillance it creates.

Here are some uses cases for artificial face recognition:-

  • A person's identity can be verified with ease using an ATM camera to analyze an image from the database.

  • We can quickly suggest a simple biometric method that is useful for opening accounts, locking wallets, and other financial transactions.

  • In particular, technological advancement aids in the KYC (Know Your Customer) process to provide for all requirements and customer interaction.

5. RPA and NLP-based Backoffice Automation Technologies:-

RPA enables banks to streamline collections, issue invoices, and schedule payments. Bots are primarily used to gather data and information from documents using the NLP process. This makes it simpler for banks to review applications and make decisions.

6. Enhanced customer services:-

Virtual assistants can have a positive impact on customer experience through personalization. After verifying the subject's identity using cutting-edge techniques like facial recognition, voice recognition, and biometrics, VA typically analyses the account information using demographic data and behavioral data to advance the process solution.

7. Risk management analysis:-

Banks play a significant role in the provision of credit, yet providing loans has long been seen as risky. In the past, banks heavily relied on historical credit information to assess an applicant's loan risk. However, thanks to AI-assisted underwriting, banks can now have a more comprehensive understanding of potential borrowers, enabling them to make well-informed decisions when approving loans.

A green circuit board with the face of an AI bot.

8. Anti-money laundering shrieks:-

Money laundering is basically initiated in 3 steps:-

It is divided into three stages, which are as follows:

A. Placement :- This is the first step in which money is obtained from an illegitimate source and transferred to a financial institution for the first time.

B. Layering :- This is the second step in which the money launderer divides their money into multiple bank accounts in order to confuse the baking analysis and ML algorithm, which essentially assists them in concealing the actual source of laundering.

C. Integration :- Last and final step is where the money is layer and settled into the money launder account.

The integration of AI with the financial industry has been performed to minimize such actions.

The innovative adaption of AI in banking and finance can also save national wealth and can control the uncertain fluctuations in international capital flow. Money laundering, sometimes known as the practice of making dirty money clean, has become more prevalent over time. The technology used to spot suspicious activities connected to money laundering is always developing and improving. Governmental and major financial institutions can use machine learning (ML) and deep learning to monitor for possibly fraudulent behavior. Additionally, these innovations aid in raising the standard of alert.

9. OCR strategies:-

In the banking industry, OCR engines can be used to identify letters and characters and digitally recreate them. This lessens the potential for human error and enables the bank to digitize documents, process bills, and place purchase orders. OCR can be used by banks to precisely digitize the personal data on bank cards used at ATMs so that a security system can validate it, manage bank statements, detect novel text arrangements, and scan paper applications and bank statements. This is a revolutionary usage of AI in banking and finance.

What's next?

So, It's clear that AI has significantly impacted the banking and finance sector. Reducing human effort and making things more accessible it's helped the banking sector grow immensely. And it appears that this expansion will continue as more businesses embrace technology to develop cutting-edge goods and services.

So, will Artificial intelligence in the banking sector replace human employees completely?

Well, it's not like that. The entire BFSI sector will be in need of employees with advanced AI knowledge. Traditional banking and finance degrees, as well as knowledge, are going to obsolete soon. BFSI domain knowledge with artificial intelligence skills is going to be the new fuel of the BFSI sector. Hence, to stay future-proof and away from career insecurities, you must upgrade yourself through advanced artificial intelligence industrial training. But yes, keep in mind a simple AI course can't help you. Opt for an industrial artificial intelligence course with a BFSI domain specialization.