The exponential growth of AI is impacting all sectors. You may have noticed the same in the case of the US financial markets. Most importantly, AI is streamlining the banking methodology. For instance, the US financial sector leveraged many new options for online retail banks. Let’s find out the details of AI for Finance.

How Does AI Work Fundamentally?

The most used algorithms of AI for finance can generate deeper insights from the data sets. They can guide customers with personalized banking and investment suggestions. They can also create scope for faster and contactless payments. As a result, the importance of manual work in the financial markets is reduced. 

Lastly, fraud protection and extensive cyber security are the other most prominent benefits of AI for finance. 

Impact Of AI: New Improvements in The Financial Markets

Experts say AI created new horizons for better risk management in the US financial sector. For instance, most US-based fintech corps enjoy better cyber security due to AI. 

At the same time, AI plays a crucial role in the regulatory compliance of banking corporations. In the meantime, AI can process large swathes of data to identify patterns and anomalies. Hence, it is an unbeatable tool of anti-money laundering. 

AI also made detection of imposters in banking easier.  For instance, Wells Fargo uses the Optical Character Recognition (OCR) technique for KYCs. This is an AI tool.

It can extract digital codes from KYC forms and match them with the information on public databases. 

In addition, generative AI has a crucial role in KYC, too. It is one of the phenomenal uses of AI for finance. 

AI brings unique tools like liveliness tests and innovative screening to find analogies and anomalies between the original photographs (during KYC) and ID card photos. 

It is a critical component of entity verification.

Significant Applications of AI For the Finance Sector

Let’s analyze the areas where AI for finance can be most effective. We will also discuss the critical roles of AI in those sectors. 

Reducing workload and improving service efficiency using Chatbots 

All chatbots are AI-powered. Chatbots use the natural language process power of generative AI. 

But How Do Chatbots Use It?

Chatbots use AI to process and send immediate replies to large volumes of customer queries simultaneously. 

Stockgeist is a reputed financial virtual assistant who answers queries regarding the financial market for people in the US. 

Such chatbots use accurate time market survey data, existing survey results, and NLP to create expert market suggestions. 

Fraud Prevention Through Quicker Detection

Traditionally, US banks use rule-oriented AML and the typical name screening system. However, experts say it may return many false positives. 

But AI can detect most malicious transaction patterns. At the same time, AI can detect anomalies in banking data to decipher potential frauds. The same technology can also uncover the unholy association of individual customers with vicious entities. 

As a result, the financial market operators can follow a more proactive approach to customer services. 

Managing Customer Relationships

Banks can use chatbots to offer 24×7 customized replies against customer queries. Previously, customer calls were redirected 2 to 3 times before someone could answer your query correctly. 

However, those days are past. 

Suppose you ask Erica, “Which deposit scheme is best for me?”. 

Erica is Bank of America’s chatbot for those who don’t know. It will analyze your past data and ask you some simple questions. Then, its algorithm will calculate and give you the best possible suggestions. 

Better customer service played a crucial role in improving customer satisfaction at US banks. For instance, BoA’s customer satisfaction grew by 5%. Chase Bank’s score also improved by the same limit. 

Predictive Analytics

ML combined with AI for finance can yield the best forecasting results. In financial markets like NYSE and NASDAQ, it may forecast stock revenues, predict stock prices, and monitor the risk of investors’ profiles. 

Apps like Stockgeist use such generative AI for predictive analysis. 

Experts say that most investors use online apps for investing. They leave trails of digital footprints and share personal data. As a result, AI can track the data. Again, it helps AI make more efficient predictive analyses. 

AI in Banks

Ai paradoxically impacts the product and service quality in the US banking sector. It leveraged advanced data handling methods. At the same time, AI also improved banks’ customer experience. 

But what are the parameters of improved customer service?

• Online banking services became simple and easy to understand 
• The service speed increased manifold
• Traditional banking services have been replaced by customer-friendly online banking

The banks can easily leverage big data now. As an outcome, data analytics has become more accessible, too. 

Want to know how data analytics helps banks? 

It helps in exploring areas responsible for cost overburn. At the same time, it also helps identify individual resource-exhausting aspects of banking operations. 

Data analytics also plays a crucial role in real-time asset management.  Despite using AI in banks, many experts are not enthusiastic about it.  They feel that BFSIs can perform better with the integration of AI. 

The emergence of online payment banks and neobanks are prime examples of efficient AI use. 

The role of AI in banks’ finances is shrinking every day. On the other hand, a plethora of new opportunities are opening before NBFCs. 

Not only online retail. However, many fintech companies are also leveraging the efficient use of AI. 

Challenges Of AI For the Finance Sector

The implementation and integration of AI can also be a critical challenge in the banking sector. 

There’s no questioning that the market of Generative AI reached USD $44.89 billion, before 2023 ended. However, most frontline US banks are banning ChatGPT use. 

Banks like Chase, CitiBank, BoA, and others are on this list.

But why?

The reason is simple. 

The banks source generative AI assistance from various third parties. And they often don’t conduct due diligence checks. So, daily implementation of these third-party AI services can be risky. 

However, there are more precise reasons for excluding ChatGPT. For instance, technology draws data from global users to improve its assessing and responding capabilities. 

At the same time, it also depends on user-generated data to develop patterns and create data privacy algorithms.

I mean that the risks are inherent to AI technology. 

The consequences may be risky if there is a data leakage from big banks.

Malicious agents can usurp the data and derive patterns that can be used to harm the bank or its customers. 

So, I feel that banks can protect customers by cutting off ties like ChatGPT. 

How Worse Can It Be?

In May 2023, a fake AI-generated image was published. The image was of an explosion near the Pentagon. It triggered fear. As a result, the value of US stock depleted overnight. 

Events like these show that AI can pose significant threats. Experts say that there are various groups ready to take advantage of the weaknesses in financial markets. 

There are many fraud financial schemes in the US markets. Most of these schemes are AI-generated. However, such schemes lead to spoofing, like voice cloning, to manipulate the market in dynamic ways. 

But many new applications of AI for finance are emerging. As a result, cyber security will further weaken. But I feel that misinformation can cause even more panic, hampering the financial market. 

The Bottom Line……

AI is drastically changing the products and services in the financial markets. For instance, chatbots, automatic trading, AI stock screening and others are the prime gifts of AI for finance.  However, we learned the lesson that AI can cause disruption in the market too. It may pose financial as well as operating risks at great lengths.

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