How Does 2022 Look Like for Applications of AI in Financial Services?
Applications of AI in financial services are getting smarter with each day. From enhanced customer service quality to improved fraud detection systems, advantages are plenty.
The financial sector every year faces cyber threats that are unmeasurable if we take all financial companies across the globe into consideration. According to one Statista report, in 2021, the majority of cyberattacks targeted financial organizations. Cryptos, crypto mining, banks, and other financial services were targeted by these cyberattacks to steal data, money, or cripple organizations.
Artificial intelligence technology, on the other hand, uses historical data and predictive analytics tools to detect or predict such attacks. As the technology is improving and getting smarter, for hackers, it is getting tougher to attack servers of financial institutions.
To discuss the contribution of AI in financial services better, we are shortlisting a few crucial applications of financial services. We are discussing these AI in financial services applications from the perspective of lending, investments, and insurance, among others. So, if you think the blog sounds useful, stay with us until the end of this blog.
1. Customer assistance
One of the major AI use cases that are leveraged by financial services is customer assistance. AI is currently used to provide customers with a platform that can solve the majority of queries without the involvement of a human agent. These chatbots are getting advanced as AI is seeing new evolutions. From personalized text responses to enhanced voice recognition, chatbot AI in fintech is indeed one of the top advantages this sector has. These chatbots are usually integrated into websites or mobile applications in financial services to make their access super easy.
2. Trading
With AI-supported technologies such as predictive analytics, trading has become one of the crucial segments of financial services that uses AI. This AI for finance use case can observe large amounts of historical data along with other factors that influence the behavior of trading factors to predict their possible position in the near future. Now, to save time invested in manually observing the trading pattern, this AI application in finance can be a handful. However, at this moment, humans are still considered more reliable to make investment decisions.
3. Automated KYC
Know-Your-Customer (KYC) is one of the easiest tasks for applications of AI to execute. With the help of sample data or smart contracts, AI is capable of handling the KYC verification process entirely on its own. To provide faster approvals and disapprovals, financial institutions are already adapting this advantage of AI for finance. AI can also be programmed to forward rare cases to human agents. That way, the cost and burden of work can be reduced to the least amount possible.
4. Fraud identification
Financial sector is the most vulnerable to cyberattacks and fraud activities. Stealing payment details, unauthorized transactions, and more are some of the common examples of cases that have been happening for years when cybersecurity was not too advanced. But now, artificial intelligence in finance applications is capable of providing an advanced fraud detection system that can predict if fraud is about to happen.
Factors such as card usage behavior, location of transactions, frequency of transactions, merchant type, and more are taken into account to automatically observe each account for fraudulent activities. Many financial institutions are also offering services where customers can inform in advance if they are traveling outside the country or their regular transaction area so that their card usage activity is not considered fraudulent and suffers an automated block.
5. Credit scoring
Credit scores are supposedly the most important factor that financial institutions consider when processing a loan or credit card application. Now, AI and finance can join hands here to process and evaluate user applications much faster than usual. Apart from the credit scores, AI can also use customer data such as their credit usage behavior, income, active loans, and more to observe and evaluate applications accordingly.
6. Non-fungible token
NFTs are the hype in 2022 and many crypto enthusiasts have entered the segment to generate, exchange, collect, and sell NFT content. Now, AI for finance is helping NFT enthusiasts in generating content faster. Moreover, by pairing up with technologies such as blockchain, AI in fintech is also helping in keeping servers secured. One of the top examples of AI use cases in the NFT segment is Eponym. The platform is designed for inexperienced NFT content creators and offers features such as text-to-image NFT generation.
7. Finance management
Making the right financial decisions is the key to growth. Thus, tools with artificial intelligence in the finance segment can help businesses and individuals in making better financial decisions. AI can help in keeping expenditures and incomes on track while also helping users in prioritizing their expenses according to the need. With data such as expense patterns and the importance of expenses, it can be easier for users to figure out how many expenses can be reduced by them, especially when some of them are not necessary.
Conclusion
AI and finance sectors are coexisting for years now and with time, banking and financial institutions are getting more dependent on technology. The reason behind this level of trust in AI is the level of security and automation that it brings along. Now as 2022 is the year of virtualizing operations such as retail on an extended level, new developments are expected to be seen for AI in the financial sector.
In the end, we hope you found this blog helpful. As MobileAppDaily is a platform that keeps publishing updated and well-researched blogs for tech enthusiasts, do not forget to explore its page to grow your knowledge.
