Machine learning, natural language processing, computer vision, generative AI and other technologies has always played a key role in the finance sector. The ability of such models is used in fintech to analyse large volumes of data, automate procedures, and improve decision-making tasks.

The advent of large language models (LLMs) has significantly impacted many sectors, but we can imagine the potential disruption of this technology when it comes to the financial sector.

The exploitation of the vast amount of data that financial institutions possess can generate great opportunities both for already existing financial services companies with access to proprietary data and new startups that despite having less access to historical data, can leverage public available data and count on more flexibility and capability to winnovate.

AI's Place in Fintech

Artificial intelligence is predicted to have a significant impact on the fintech industry for a number of reasons. These include the technology's ability to improve user experience by serving as a mediator between the customer and the service provider, thereby improving both the overall experience and the services offered by fintech firms. Additionally, technology has the potential to automate human-related tasks that currently slow down processes and to improve security measures overall by identifying and preventing fraudulent activities.

Financial institutions are being able to redefine the way clients interact with financial products and services by offering more individualized, efficient, and secure services because of this technological revolution. AI's contribution to fintech is anticipated to grow and transform the industry as it continues to advance.

Let's quickly outline the key effects that AI can have:

Improving the Customer Experience with AI

Chatbots and virtual assistants driven by AI are revolutionizing customer service in several industries. These technologies drastically cut wait times and raised customer satisfaction by responding to concerns quickly and personally. AI-powered platforms, for instance, can evaluate client data to provide customized product and financial suggestions. Customers become more dependable and devoted to the financial institution as a result, in addition to being able to make better financial decisions. This could make it feasible to reply to and understand a wide range of consumer demands, from checking account balances to giving advice on financial planning, and handle customer inquiries as they arise. Customers will thus benefit from a smooth and effective service experience, which will increase their happiness and loyalty.

The critical areas where they can significantly contribute to enhancing the customer experience are:

AI in Customer Support:

AI-powered chatbots and virtual assistants for customer service answer questions, fix problems, and give financial advice; they increase consumer engagement and provide round-the-clock assistance. Numerous businesses are using chatbots to enhance website navigation, and numerous chat providers have begun to offer GPT-based chatbot experiences customized for particular websites. Think about a 24/7 financial expert that can give you insights on everything that is related to your current financial position.

AI in Tailored Financial Services

AI customizes financial services and products to meet the needs of each unique customer. AI helps consumers manage their money more skillfully by offering tailored advice based on analysis of spending patterns and financial objectives. This is a step forward from a new version of the robo advisor that could explain and modify existing financial instruments for particular purposes in addition to rebalancing and adapting an investment. In this case AI can also give recommendations on how to increase your credit score, give you advice on any specific products such as loans, insurance or investment opportunities.

Automate Routines

Routine processes like data entry, transaction processing, and compliance checks can be readily automated by AI, greatly speeding up the workload for human workers. With the ability of models to take real actions and connect to different applications through LLM agents or Large Action Models (LAM), Financial institutions are able to service more consumers effectively as a result of this automation, which results in faster, more accurate operations. Artificial intelligence frees up human workers to concentrate on more intricate and strategic work by managing routine jobs.

As an illustration, consider how a financial business might employ AI to automate the loan application process. By examining a variety of data points, including credit histories, income levels, and spending behavior, the AI system quickly determines an applicant's creditworthiness. This thorough research ensures a quick and accurate decision-making process by cutting the time it takes to accept or reject an application from days to minutes. As a result, this speeds up the loan approval process and lowers the possibility of human error, which increases customer satisfaction and lowers operating expenses.

Other noteworthy instances of how routine automation may affect the financial industry include:

AI in Automation of Data

Large-scale financial data gathering, processing, and analysis can be automated with AI. Financial institutions are able to make well-informed judgements and strategic plans thanks to this automation, which makes real-time insights and analytics possible. AI technologies, for instance, are able to track transaction data, market trends, and consumer behaviour patterns continuously. Financial organisations can use these data to find growth prospects, spot niches in the market, and improve the services they provide. Real-time data analysis also helps with risk management by foreseeing any problems before they become serious ones, which improves the stability of the organisation.

AI in Evaluation of Credit Risk

Accurate creditworthiness assessments can be made by looking at transaction patterns, credit histories, and other pertinent information. Conventional credit scoring techniques can be biased and frequently rely on small numbers of data points. In comparison, AI provides a more effective assessment by utilizing complex models and a wider range of data sources. This sophisticated analysis makes it possible to make more accurate lending decisions, which lowers the default risk and guarantees that loans are only given to people who are actually creditworthy. Financial institutions can increase their competitive edge by providing consumers with better rates and terms by increasing the accuracy of their credit assessments. 

AI in Trading Algorithms

Large volumes of market data are analyzed by AI algorithms to execute trades at the best times, maximizing profits and lowering risks. AI systems analyze real-time data from various sources, such as news articles, stock prices, and economic indicators, in algorithmic trading.

Financial organizations can benefit from improved investment outcomes by taking advantage of market opportunities more effectively thanks to this high-frequency decision making processes. Furthermore, trading systems powered by AI are able to adjust to shifting market conditions, guaranteeing consistent success even in foggy circumstances. Algorithmic trading has long piqued the interest of independent traders and trading firms. Naturally, the development of LLMs has renewed interest in the use of these models, particularly given the potential to create autonomous trading agents driven by LLM and the capacity to more effectively interpret textual data, such as news.

Enhancing Safety Procedures

By identifying and stopping fraudulent activity, AI improves security. Machine learning algorithms examine transaction patterns to spot irregularities that can point to fraud, allowing for quick risk mitigation. Furthermore, AI-powered identity verification procedures guarantee that only people with permission can access private data, shielding clients and financial institutions from security breaches.

As an illustration, consider the Italian government's recent agreement to implement machine learning models to aid in tax collection and the identification of tax evaders.

The main example of this application is fraud identification

Artificial intelligence (AI) systems are always keeping an eye on transactions and looking for odd patterns that could point to fraud. AI assists financial institutions in preventing fraud and safeguarding consumer money by identifying these irregularities. The capability of understanding real time the possibility of fraud is a key component that financial institutions are more and more applying to safeguard their customers.

Innovative AI Fintech Startups

Let’s delve a little bit on some examples of startups that are applying AI in the fintech sector and their applications

Kasisto KAI

One well-known example of a business using AI-powered chatbots and virtual assistants to transform customer care in the financial industry is the banking startup Kasisto. KAI, their flagship product, is an AI platform made to increase customer engagement and offer 24/7 support.

The way KAI improves customer service

Around-the-Clock Support: With its round-the-clock assistance, KAI helps users with account queries, transaction information, and troubleshooting without requiring human involvement. By ensuring that clients can obtain assistance whenever needed, this raises customer satisfaction levels.

Financial Advice: KAI offers clients individualized financial advice in addition to basic support, assisting them in making financially responsible decisions. This covers investing ideas, spending insights, and budgeting advice.

Upstart

AI is being used by Upstart to evaluate credit risk and automate loan approvals. Upstart offers more comprehensive and accurate credit assessments by examining non-traditional data points including employment history and education, allowing credit access for a wider spectrum of clients. When compared to traditional models, Upstart's AI algorithms considerably increase approval rates by evaluating over 1,600 criteria to determine creditworthiness. Thus, Upstart has made it possible for its lending partners to approve 44% more applications and provide loans with 36% cheaper annual percentage rates.

Some of the main limitations of such models in the assessment of credit risk have been the biases in defining some parameters such as gender, race, religion and other factors that have historically been a source of discrimination in loan approvals. In comparison to conventional lending models, Upstart's approach authorizes 46% more Hispanic and 35% more Black borrowers. The platform also provides APRs that are 34% lower for Hispanic borrowers and 28.7% lower for Black borrowers. 

This inclusive approach has helped many underserved communities access affordable credit​ (Auto Refinance Loans Credit Union)​.

Zest AI

Zest AI evaluates large datasets to find trends and forecast creditworthiness using machine learning to improve credit underwriting. As a result, loan decisions are made with greater accuracy, and default rates are decreased. With the use of Zest AI's platform, lenders can create customised underwriting models that lower charge-offs and defaults by 30–40% and raise approval rates by 20–30% without taking on additional risk. To modernize lending procedures, Zest AI collaborates with large banks such as Citibank, First National Bank of Omaha, and Suncoast Credit Union. Additionally, by enabling lenders to approve more customers and provide more cheap credit, their technology advances financial inclusion.​

SymphonyAI

SymphonyAI uses AI to maximize regulatory compliance and identify financial crimes. By analyzing intricate datasets to spot suspicious activity and verify compliance with regulations, its machine learning platform helps financial institutions manage risk better. With the use of SymphonyAI's sophisticated analytics, organizations can find hidden patterns in financial transactions that may point to possible fraud or money laundering. Because it guarantees adherence to changing regulatory standards and improves security, this is an advantageous tool for banks looking to optimize their compliance procedures.

Benefits of AI in Fintech

After having seen some concrete examples and the main applications of AI in fintech, we can try to highlight and summarize the main benefits that this revolutions can bear:

Enhanced Effectiveness

Financial institutions can manage more transactions with more accuracy and efficiency thanks to artificial intelligence, which expedites operations, automates repetitive jobs, and simplifies processes. For instance, AI-driven automation greatly reduces the manual workload in transaction processing and compliance checks, allowing for faster and more dependable service delivery.

Lowering of Expenses

Artificial intelligence lowers operating costs and minimizes the need for human involvement by automating labor-intensive procedures, which results in significant cost savings for financial institutions. According to McKinsey & Company, process optimization and automation using AI can cut operating expenses by 15 to 20% in the next 18 months.

Improved Precision

AI systems perform reliable data analysis, minimizing the possibility of mistakes and guaranteeing precise decision-making in tasks like fraud detection and credit evaluation. AI systems, for example, reduce the likelihood of wrong approvals or denials by precisely assessing credit risk by cross-referencing hundreds of data points.

Improved Decision-Making

AI gives financial organizations the ability to make data-driven, well-informed decisions that improve performance and competitiveness. It also offers real-time insights and predictive analytics. Banks can foresee market trends, evaluate risks, and create strategies that support their corporate objectives with the aid of AI-powered analytics tools.

Challenges and Risks

Of course not everything that shines is gold, there are many challenges that Artificial Intelligence needs to face, in order to be vastly adopted in the tasks we have seen.

Data Privacy Issues

Privacy problems are raised by AI systems' broad use of client data. To guarantee that consumer data is handled safely and morally, financial institutions need to put strong data protection procedures in place. In order to protect client privacy, organizations are required under the GDPR to implement measures such as data minimization, purpose limitation, and secure processing. Besides the capability to identify possible sources of biases can improve the procedures to take into account uniquely the data that are strictly needed to perform a task/decision

KYC

Procedures related to Know Your Customer (KYC) present another major danger. Since more people are using remote identification techniques, there is a greater risk of identity fraud due to deepfake technology, which allows scammers to produce lifelike fake images or videos that mimic genuine users.

By fooling verification systems, deepfakes enable fraudsters to register bank accounts and obtain financial services using fictitious identities. This carries significant hazards, such as:

In addition, this may impact a number of activities:

Fraudulent Account Creation: Applied to illegal activities such as cybercrime and money laundering.

Legal and Financial Consequences: Failure to comply with KYC rules may result in financial and legal ramifications.

Higher implementation costs for sophisticated fraud detection and verification techniques translate into higher operational costs.

Regulatory Obstacles

Fintech's quick embrace of AI presents regulatory issues. Financial institutions have to manage intricate regulatory environments and make sure that new laws and standards are followed. Regulation authorities are creating frameworks to address AI governance and accountability, such as the Financial Conduct Authority (FCA) in the UK, but also the EU AI act, a European regulation on artificial intelligence, the first comprehensive regulation on AI by a major regulator anywhere. 

Moral Aspects to Take into Account

Biases found in the training data may occasionally be reflected in the decisions made by AI. It is essential to guarantee fairness and transparency in AI algorithms in order to prevent discriminatory behaviors and foster customer trust. Financial organizations must implement ethical AI procedures in order to reduce bias and guarantee that every customer is treated fairly. This can be achieved by regularly monitoring and evaluating their AI systems.

Future trends!

AI and Blockchain

The financial industry is about to undergo a revolution thanks to the integration of blockchain technology with artificial intelligence, which will greatly improve transaction security and transparency. An extra degree of protection can be added by using AI algorithms to examine blockchain data in real-time and spot trends and anomalies that can point to fraudulent activity. AI, for instance, may keep an eye on transaction histories and identify questionable activities that stray from expected patterns. AI can also lessen the possibility of errors and manipulation by validating and verifying transactions, which helps to maintain the integrity of financial records. Industries like banking, insurance, and supply chain management that demand high standards of data security and integrity will find this integration especially intriguing.

The Rise of Robo-Advisors

AI-driven robo-advisors are democratizing access to individualized financial advice by becoming a commonplace tool for investment management. These automated systems use artificial intelligence to assess each user's risk tolerance, financial objectives, and market circumstances in order to build and maintain customized investment portfolios. The allure of robo-advisors is found in their capacity to offer impartial, data-driven, and reasonably priced investing advice. Compared to traditional human advisors, robo-advisors can provide more accurate and timely suggestions by utilizing algorithms that are constantly learning and adapting. They therefore appeal especially to younger, tech-savvy investors who favor digital solutions and less prices. According to some projections, the global robo-advisory business might reach $2.3 trillion in assets under management by 2028 in the US, indicating a high growth trajectory.

Better Predictive Analytics

The financial sector is poised for further transformation thanks to AI's capacity to evaluate enormous datasets and forecast future trends. Financial organizations can obtain a more profound understanding of market dynamics, client behavior, and potential hazards through the use of enhanced predictive analytics. Financial institutions may more accurately predict market movements, evaluate credit risk, and identify fraud by utilizing machine learning algorithms. Predictive analytics, for example, can assist banks in determining which clients are most likely to experience loan default, allowing them to take preemptive steps to reduce risk. AI-driven insights can also help with product creation, customer retention, and marketing strategy optimization. Financial institutions will eventually be able to make more strategic and informed decisions because of the ongoing development of AI technology, which should boost productivity and profitability.

Conclusion

The fintech sector is undergoing a change thanks to artificial intelligence, which is fostering innovation, efficiency, and improved client experiences. Fintech has benefited greatly from artificial intelligence, even in spite of obstacles like data protection and regulatory compliance. Financial organizations will have new opportunities as AI technologies advance, increasing the accessibility, security, and personalisation of financial services. Fintech companies must embrace AI as a necessary progression to be competitive in the quickly evolving financial sector, not just as a trend. As every major revolution, there is not such a thing as not accepting it, the traditional banking sector which has struggled to maintain a correct pace, would need to follow and embrace this new technology.