How AI Solutions Reshape the Financial Sector

We live in the era of rapid technological progress, with the virtues of that progress finding application in a variety of industries and niches, including finance. Financial institutions have always been at the forefront of technological innovation as they deal with large masses of customer data, financial analytics, economic forecasts, and financial planning.

That’s why a wide range of financial entities are investing in AI now, with the cost savings expected to reach $447 million by the end of 2023 and over 80% of banks recognizing the transformative value of AI integration. PWC experts estimate the AI contribution to the global economy to exceed $15 trillion by 2030, and the trend is evidently accelerating, with the interest in AI integrations increasing by 50%+ over 2023.

Thus, with the rising trend of digitization, financial companies have already embraced the computational speed and error-free technologies modern innovation offers.

One of the best and most promising innovations in FinTech is artificial intelligence (AI). The focus of AI is to achieve better efficiency with lower costs by performing human tasks and simulating human behavior at a much higher speed.

In other words, AI lets computers perform human tasks in terms of client demand forecasting, personalized customer service, and advice, as well as sensitive, accurate decision-making based on large masses of unstructured data. It’s done much quicker than people, and usual computers can do, with the AI potential increasing day by day as machines learn and hone their intelligence and skills.

The primary benefit of AI for financial services is the impressive computational speed and the analytical potential it offers, allowing quicker and more sensitive decisions based on accurate analytical prognoses.

In other words, the key target of AI implementation is efficiency increase coupled with more client-oriented customization achieved with the help of advanced algorithms, big data analytics, and in-depth data analysis.

What is fintech?

In a nutshell, one can characterize Fintech as technology-oriented financial organizations applying the latest innovative technologies for the advancement and optimization of financial service provision. Due to the emergence of Fintech companies only around a decade ago, the challenges and barriers people used to experience on the way to accessing financial services are gone.

The use of cutting-edge technologies enables high-quality services at lower rates and well-organized and streamlined workflows with a small workforce. As Fintech companies are created initially with a focus on cutting-edge technology and innovation, nothing is surprising about their active interest in AI. Among the most famous Fintech startups investing in AI development are Aire, ZestFinance, and EyeQuant.

Read also: How blockchain is being used in the food industry

How is artificial intelligence used in finance?

There are many ways to adopt AI in finance and take advantage of its features and capabilities. Here are some core benefits financial organizations derive from AI integration in their operations today.

Crowdfunding

Previously, lenders had to go to a bank and file a heap of documents asking for a business loan, getting which was (and is) extremely troublesome. But with the emergence of Fintech, crowdfunding platforms like Patreon or GoFundMe merged to unite borrowers and investors in a space alternative to traditional banking. Now, if your idea is cool, you can attract investors directly, getting money from different sources to jumpstart your businesses without financial blocks.

Intelligent assistants

AI assistants have entered the financial industry and enjoy great popularity among users. For instance, such advisors are helpful in investment decision-making, analyzing large-scale stock and bind data, and giving users suggestions for buying or selling assets depending on their level of risk, financial goals, and the composition of their portfolio.

Blockchain and cryptocurrency

Probably the most famous application of artificial intelligence in finance, blockchain and cryptocurrency now rule the world of decentralized, alternative banking. Global crypto-exchanges like Coinbase allow users to exchange crypto-assets for money and vice versa, while new smart blockchain services (e.g., BlockVerify) help users avoid fraud by securing the intactness of blockchain data and transactions.

Insurance management

Insurtech is a new trend branching out of Fintech today. AI-enabled insurance services are much more customized and sensitive in terms of pricing and coverage, giving more people access to affordable insurance and bridging the gap between providers and users. Some bright examples of promising Insurtech startups include Oscar Health and Credit Karma, both raising considerable funds from the onset and exhibiting healthy growth within the past years.

Algorithmic Trading

Stock and crypto markets are not perfect, and the basis of algorithmic trading is to detect and exploit these markets’ inefficiencies to gain profit. AI algorithms are trained on historical trading data and provide automated trading decisions executed by computer programs. This way, stock trading occurs in a fully autonomous regime without human participation, with the trader only able to set the initial configurations for the AI model.  

Risk Assessment and Management

An actional risk management plan is key to a financial organization’s survival. It helps identify high-risk loan and mortgage applicants, credit card fraud, identity theft, and other risks typical for the financial sector. AI-powered risk management practices are more efficient and productive, as well as cost-saving for businesses of all sizes, as they perform analysis of big data in real-time and can minimize the company’s financial losses.

Credit Scoring

AI offers a promising alternative to the traditional balance scorecard approach to credit scoring in financial organizations. This method is too limited in terms of the anticipated creditworthiness of applicants and makes decisions only based on the credit history and track record, which many people don’t have yet. AI models embrace a much wider diversity of data sources and also include non-traditional data in credit scoring analysis to give a more intelligent and nuanced view of the applicant’s creditworthiness. 

Portfolio Management

AI applications are also gaining popularity in the field of smart portfolio assessment and risk management. The AI-powered analysis is performed using a set of indicators, based on which the AI model can issue accurate predictive modeling of the asset portfolio’s profitability and recommend adjustments to it. 

Fraud Detection and Prevention

AI risk management technologies can detect and address real-time anomalies in financial operations, such as suspicious banking transactions, abnormal app usage, the use of non-standard payment methods, and other unusual financial actions. This way, a financial institution can block fraudulent activities quickly and prevent fraud and financial loss with a higher degree of accuracy than a retrospective manual check would allow.

Personalized Financial Advice

AI is also actively integrated in the personal finance sphere, where smart apps help users track their spending, cash flows, and bills. Based on the historical analysis of the user’s financial behavior, the apps can recommend more cost-efficient budgeting decisions and give investment recommendations compliant with the user’s individual risk tolerance. One of such apps is Wally, an AI-powered personal finance assistant that tracks expenditures, advises on budgeting, and gives a 360-degree view of the user’s spending habits by categorizing data from all accounts. 

Regulatory Compliance

As AI systems are able to identify anomalies and discrepancies in financial transactions in real time, they can become a powerful tool for any organization’s compliance tracking process. The detected non-compliant transactions are quickly blocked, preventing the company from perpetuating fraud and illicit activities. This way, AI can help financial entities preserve their reputation, ensure regulatory compliance, avoid fines, and protect user funds from theft. 

Market Analysis and Prediction

The major strategic advantage of AI systems is their ability to identify emerging trends and give accurate predictions of financial market shifts. This predictive modeling feature is widely used in businesses of all scales and sizes, allowing them to adjust product offerings, marketing strategies, and activities to embrace innovative market opportunities and beat the competition. 

High-Frequency Trading

The principle of high-frequency trading (HFT) is to identify and use the tiny mispricing anomalies in the stock market. The situation in which mispricing occurs can be very short, with the market equilibrium restored in a matter of seconds. That’s why manual HFT is impossible; it can only be performed by adequately trained AI algorithms that exploit the mispricing related to statistical arbitrage, market marking, and news.   

Anomaly Detection

A financial entity’s reliability and reputation heavily depend on its ability to detect and address anomalies – abnormal data patterns. The anomalies are not only bad and don’t always suggest fraud; in some cases, anomaly analysis can help retail businesses identify positive buying behaviors and personalize their services for higher revenues. However, the mainstream use of anomaly detection AI is still directed at identification and mitigation of potential threats, such as fraud, malicious spyware, and scams.

Customer Insights and Behavior Analysis

The use of AI-powered customer behavior analytics is a new page in the world of personalized marketing. Companies utilizing such AI algorithms get unique insights into their customers’ preferences, shopping patterns, and tastes. This data enables greater service customization and increases the company’s revenues by offering customers their preferred goods at optimal prices, which increases the likelihood of a completed purchase. 

AI Use Cases in Finance 

Which front-line financial institutions have already implemented AI in their operations? Which AI companies exemplify the next-level financial service? Here are a couple of examples that may illustrate the pace of AI adoption in the financial sector.

• J.P. Morgan Chase is considered the present-day leader in AI research. The mission of its AI program is to explore and advance state-of-the-art AI/ML capabilities in the financial sector. The bank was also one of the first to release an AI-powered virtual assistant for assisting corporate cross-border transactions of any scale. 
• Capital One has the largest number of AI patents. The company filed 55 AI-related patents in Q3 2022 and 47 patents in Q2 2022, with the numbers growing higher in 2023. 
• Wells Fargo is known for a considerable volume of AI investments. The bank also uses AI technology as the backbone of its virtual assistance, ensuring personalized interactions and improved customer experience. 
• Zest AI is a US-based underwriting platform that provides AI-powered credit scoring services for companies dealing with applicants with scarce to no credit history. 

Innovative mobile payments

As smartphone users are becoming the world’s largest segment of Internet users, Fintech responds to their needs for payments and other financial services on the go. With a mobile phone in their hands, users can now perform all kinds of operations ranging from paying for goods and services to exchanging money, paying taxes, and even managing their employees’ payroll.

Smart financial advisors

AI systems’ ability to handle big data and analyze it smartly has found an application in Robo-advising and investment management. Robots of Betterment and Ellevest help clients make investment decisions based on large-scale stock dynamics data. At the same time, innovative stock-trading apps remove the need for an official stock exchange as an intermediary charging a commission.

Pros of AI application in fintech

The pros of applying artificial intelligence in Fintech in your business are numerous. Here are some of them you should consider if you plan to opt for the AI solutions.

• Large-scale cost cuts. Apart from the traditional financial institutions that have long bureaucratic pathways for all decisions, Fintechs are experiencing unprecedented growth because of quick and smart decision-making based on big data use, AI, and predictive intelligence. These gains are accompanied by better data and asset security, guaranteeing cost savings, and quality improvements at all levels.
• Advanced fraud detection. Unlike the old-school rule-based system of fraud detection, AI helps to make smarter decisions based on fuzzy, unstructured data in real-time. As a result, fraud detection gets more accurate, with higher predictive power in ambiguous user behavior cases, enabling Fintech organizations to secure their users’ funds and keep their sensitive assets intact.
• Efficient customer service. The progress of AI has made human-like chatbots a reality. Today, some users can’t even distinguish whether they are chatting with a bot or with a human being as AI responses and problem-solving capacity are impressive. Today, AI-enabled chatbots are used by MasterCard, Lemonade, PayPal, American Express, and many other progressive organizations. Other examples are the KAI chatbots of Kasisto and the Co-Pilot chatbot by Digital Genius. The best about chatbots is that they continuously apply natural language processing and machine learning to understand people better, thus improving the quality of their service day by day.
• Smart decision-making. Managers of FinTech organizations receive better data for making decisions as a result of in-depth data analytics with AI. As a result, they can make more sensitive and accurate decisions, progressing towards data-driven management. With AI, financial institutions become more accurate and personalized in lending, payment, and insurance services, able to access broader pools of clients because of more intelligent credit scoring and borrower analysis approaches.
• Accurate predictive analysis. The best AI can now offer to Fintech is smart management of no-file and thin-file segments of the population, including people with no credit history (or a scarce one). Previously, such potential lenders were scored low in the banking systems following the traditional scorecard method. At the same time, AI helps examine the employment potential and wealth growth prospects of people who currently have little chances to qualify for a loan.
• New career opportunities. AI is a distinct knowledge-intensive sphere in which skilled and well-trained AI operators are in ever-growing demand. According to the latest McKinsey report, AI is expected to create 133 million new jobs in the financial sector by 2030. Therefore, the quick expansion of AI in all business spheres promises exciting, highly paid career opportunities for AI and Data Science experts.

Cons of AI application in fintech

However, together with praising the advantages and innovations AI brings to the field of finance, one should stay cautious about the limits of this solution. There are some perils of artificial intelligence that optimists tend to overlook.

They may pose some gains of Ai introduction into question and even compromise the efficiency and quality improvements a financial organization can achieve. Let’s take a closer look at the aspects of AI requiring caution and extra consideration.

• High implementation cost. Even though AI implementation is economically viable and promises significant cost cuts, its integration is still costly at first. Thus, some financial firms find themselves unable to embrace the innovative technology, remaining at the brink of progress while their competitors advance and take their market share.
• Limits to human intelligence replication. AI systems are praised for imitating human intelligence and performing tasks in a human-like fashion, though at a much higher speed. Yet, every businessperson implementing AI solutions should keep in mind that no AI system will ever get close to human performance as machines are doomed to remain machines. So, it’s better to remain sober and realistic about the limits of AI potential.
• Limited improvement in performance. No matter how long the artificial intelligence systems learn, they still cannot surpass the human learning potential, with their capacity limited to the amount of historical data they receive.
• Poor creativity. AI systems are totally devoid of the uniquely human trait – creativity. Thus, they can perform some tasks tediously, accurately, and quickly, but they are unable to make unusual, extraordinary decisions, which is sometimes imperative.
• The threat of unemployment. A common fear surrounding the massive introduction of AI is the threat of unemployment it brings. Overall, it is valid for low-skilled labor worldwide, as automation allows replacing manual operations. Machines do not sleep and eat; the employer doesn’t need to pay taxes for them or cover their vacations. Thus, in the long run, AI automation is indeed a business measure for cutting costs on otherwise costly manual labor.

However, AI is not a threat to highly skilled intellectual labor as such jobs are, on the contrary, gaining prominence and enjoying higher demand. Businesses need people who will program and operate AI systems, which can be done only by highly qualified professionals with expertise in engineering.

How will AI affect finance jobs?

Many people fear that the massive introduction of AI in all aspects of financial operations threatens human workplaces by making some jobs redundant. As we discussed above, in part, it is true that AI implementation results in efficiency advances without the need to involve more human forces.

In part, it is false as AI innovations are rarely concerned with replacing humans, more often dealing with the advancement of human decision-making, speeding up financial processes, making predictions more accurate and sophisticated, etc.

Thus, finance experts should not fear remaining overboard as a result of technological progress; instead, they should hone their professional skills to integrate into the new hi-tech workforce of the future.

Emerging AI Trends in the Financial Sector 

As AI adoption is quickly accelerating in the financial sector, the global community has many emerging, innovative trends to witness in the coming years. The most significant trends to watch out for in 2024 and beyond are as follows: 

• NLP and chatbot usage. NLP tools and chatbots are increasingly used for repetitive tasks and customer service. Their use is likely to expand, with more human-level and nuanced interactions paving the way to greater AI adoption by clients.
• More accurate fraud detection. As AI learns and advances, these smart tools are expected to enable more accurate real-time anomaly detection and fraud prevention for greater financial security.
• Predictive analytics. Predictive modeling using historical data is getting more accurate with time, and future AI applications are expected to issue insightful forecasts for better risk identification, optimized lending, more precise customer targeting, and wiser investment decisions. 
• Expanding blockchain use. Blockchain technology is quickly gaining momentum because of its transparency, security, and decentralization features, and financial institutions are expected to integrate DLT at a massive scale soon. AI works in tandem with blockchain to ensure ultimate user privacy, security, and efficiency.  
• Dynamic pricing. AL and ML algorithms can inform dynamic price adjustments in line with the identified customer demand, variations in supply, time of day, and changing market conditions. This way, companies can increase sales by making their pricing sensitive to a variety of market factors. 
• Personalization. In-depth data analytics and predictive models that AI gives to financial institutions can inform greater service personalization based on the inside-out understanding of the customer. 
• Generative AI. Modern AI apps have gone far beyond analyzing data; now, they can create new data using generative AI capabilities. This AI type can fuel synthetic training data generation, enhance the automation of standard financial operations, and expand the variety of data types that AI models can understand (including text, images, video, and audio content). 
• Real-time AI model training. Real-time Machine Learning technology is one of the latest AI advancements with a huge potential for financial organizations. It uses real-time data for accurate forecasting and quick adaptation to changing market conditions. The forthcoming applications of Real-Time ML involve online model training on incoming data, which can be a revolutionary change in the financial sector.

Jumpstart your AI-enhanced fintech business with Datrics

With AI firmly gaining ground in the Fintech industry, companies worldwide are concerned about embracing the potential of this new technology and advancing their financial startups with the help of AI solutions.

We at 4IRE are ready to provide consulting and AI solution development services to help your business embrace the potential of AI-backed insights for business growth. 4IRE has been a long-term partner with Datrics – an intelligent data science platform with fully customized AI solutions. Datrics can help you maximize the value of AI for your business startup in terms of customized, individually tailored fintech-related AI integration. It enables the quick and hassle-free implementation of AI in your business operations the way you see it.

As an intelligent data science platform with fully customized AI solutions, Datrics enables the quick and hassle-free implementation of AI in your business operations the way you see it.

Even if you have never worked with AI and have zero technical expertise, you’ll be able to create a suitable AI application for your business needs without wasting time on lengthy and costly software development.