Fintech brings technology-enabled innovation to the financial sector, and artificial intelligence stands at the forefront of this digital revolution. This technology has demonstrated the ability to enhance various aspects of financial services, offering unprecedented opportunities for efficiency, security, and personalization.

This article will explore the relationship between AI and fintech, focusing on how this powerful technology reshapes the financial landscape. We’ll examine top applications of AI in finance, from automating routine procedures to improving fraud detection and providing personalized financial advice.

AI in fintech: Key statistics

The impact of AI in the financial sector is far-reaching, transforming the industry at its core. With the help of statistics, we gain valuable insights into the current state of AI adoption in fintech and glimpse the future of this symbiotic relationship. 

• The fintech industry has experienced explosive growth in recent years, with AI playing a pivotal role in driving this expansion. The global fintech market is currently worth $340.1 billion. At the same time, the market value of AI in fintech is estimated at $44.08 billion. With a compound annual growth rate (CAGR) of 2.9%, the share of artificial intelligence in financial technology is expected to reach $50 billion over the next five years. (Fortune Business Insights, Statista)
• Approximately 72% of companies utilize AI in at least one business function. Such widespread adoption is not surprising – businesses that harness AI see a lot of improvement in data processing, security management, cost reduction, and customer service. (McKinsey)
• 67% of organizations plan to increase spending on technology, especially in data and AI fields. This widespread commitment to technological investment signals a future where data-driven insights and AI-powered solutions will be fundamental to business success. (Accenture)
• AI implementation in identity verification is set to save banks $900 million in operational costs and cut 29 million hours from digital onboarding processes. The average time spent per digital onboarding check is expected to decrease by 30%, from over 11 minutes in 2023 to under 8 minutes in 2028, highlighting AI’s potential to streamline financial operations and enhance efficiency. (Juniper Research)
• AI-powered chatbots and virtual assistants can handle many customer interactions in real time. These solutions have demonstrated impressive results, helping reduce the cost of dealing with user inquiries by up to 80%. Furthermore, recent advancements have led to a 25% improvement in conversational AI accuracy, enabling these systems to better understand customer sentiments, recognize their intent, and respond to their needs. (IBM)

The benefits of AI for fintech business 

Artificial intelligence has emerged as a game-changing force in the fintech industry. It offers a wide array of benefits that improve business operations and enhance customer experience. From advanced data analysis to personalized service delivery, this powerful technology is fueling growth and transforming the future of finance.

Saving operational costs

According to McKinsey, 42% of companies in the financial industry spend between 5% and 20% of their digital budget on analytical AI. Using this technology, fintechs can eliminate a vast amount of manual work, optimize business processes, address talent shortages, and significantly reduce human errors, which ultimately helps them save time and money.

In terms of tangible results, many companies that harness AI capabilities report significant cost reductions in service operations (49%), supply chain (43%), software engineering (41%), HR (37%), and IT (37%). These areas also see meaningful revenue increases, along with marketing and sales, which generate value for 71% of organizations, product and service development (56%), risk, legal, and compliance (53%).

Powering a better customer experience

AI-powered technologies are revolutionizing customer service in the fintech industry. By leveraging these tech advancements, banking, financial services, and insurance (BFSI) businesses can provide superior customer experiences, leading to increased retention, loyalty, and positive word-of-mouth referrals. 

With the help of AI algorithms, financial institutions can connect the dots between all pieces of information about their client and aggregate it into a complete, holistic, individual customer profile. This comprehensive understanding enables companies to enhance their customer service in multiple ways:

• Delivering all interactions with customers with greater precision. 80% of customers say the experience a company provides is as important as its products and services. By providing consistent, personalized experiences across all channels, AI allows fintech companies to proactively anticipate customer needs, offer value-driven recommendations, boost engagement and ultimately improve customer satisfaction.
• Understanding customer sentiments. Modern financial technology can analyze customer sentiments and buying behavior to identify potential service gaps. Based on these findings, fintech companies can offer relevant, pre-approved products and tailored financial advice, making interactions more meaningful and targeted. Additionally, AI-powered voice and speech analysis can predict customer moods and guide agents to provide appropriate solutions.
• Creating lifelike virtual engagements. Implementing AI in fintech enables industry players to provide their clients with prompt support, quick access to relevant information, and helpful on-the-spot guidance. While the interactions are not exactly the same as what customers would expect from meeting an agent in person, AI-driven chatbots are pretty good at handling common queries and freeing up human representatives to focus on complex issues. Moreover, they offer round-the-clock support that meets the growing customer expectation for instant access to financial services on any channel – be it a website, mobile app, or messenger app.
• Suggesting next best actions. 65% of clients expect companies to adapt to their changing needs. AI-driven analytics can predict customer demand based on their financial history, transaction patterns, and life events. For example, the system can identify when a customer will likely need a mortgage, investment recommendations, or a new savings plan, enabling targeted and timely offers that resonate with the client’s current situation.
• Tracking customer cases across multiple channels. Advanced AI systems can track customer interactions across various touchpoints, including mobile apps, websites, call centers, and in-person visits. This allows for seamless handoffs between channels and representatives, ensuring customers don’t have to repeat information or restart processes when switching between platforms. 

Protecting against security breaches

According to the IBM report, the financial sector is one of the prime targets for cybercriminals. In 2023, the industry’s average cost of a data breach amounted to $5.9 million.

However, the study also revealed some promising findings regarding the use of AI in cybersecurity. Organizations with extensive use of this technology in their security protocols experienced data breach costs that were, on average, $1.76 million lower compared to no use. These organizations were also able to detect and contain breaches an average of 108 days faster than those not utilizing AI capabilities.

AI systems continuously monitor network activity, swiftly detecting and flagging anomalies in user actions, such as unusual login patterns or suspicious data requests. This proactive approach helps prevent data breaches and leaks, safeguarding sensitive financial information and maintaining customer trust. 

AI also enables biometric authentication techniques such as speech, facial, and fingerprint recognition, which provide a safer alternative to traditional passwords, protecting clients’ financial data and money. Additionally, AI combined with IoT technology enhances physical security in financial institutions, employing advanced surveillance, automated security protocols, and behavior analysis to protect offices and branches from theft and armed robberies.

Unlocking data-driven insights

AI has revolutionized data analysis in fintech, offering unprecedented capabilities in processing and interpreting vast financial information. Research shows that many BFSI executives said their organizations reap massive benefits from their existing data and analytics projects.

• Enhancing productivity. Data analytics help to create more effective processes and optimized operations. For example, banking institutions can use it to provide faster and more accurate responses to regulatory requests. Advanced analytics can also improve cash management in ATMs and streamline routing, resulting in significant cost savings.
• Boosting sales. AI conducts in-depth analyses of customer data, making predictions about consumer preferences and product development. For instance, a leading Asian bank used advanced analytics to explore several vast datasets, including customer demographics, credit-card statements, point-of-sale data, online and mobile transfers and payments. This helped to uncover over 15K micro segments in their client base. This revelation prompted the bank to create the next-product-to-buy model, which led to a threefold increase in sales likelihood.
• Optimizing revenue streams. AI-powered analytics can uncover hidden patterns in pricing and discounting practices. A US bank used machine learning to analyze discounts offered by private bankers, revealing unnecessary concessions. By correcting these patterns, the bank increased revenues by 8%, demonstrating the potential for AI to directly impact the bottom line.
• Enabling digital transformation. AI accelerates the shift to digitization by providing insights that support the development of new products and services. This technology also supports the automation of back-office processes, improving operational efficiency and reducing costs associated with clerical tasks.
• Improving decision-making. AI-powered analytics enhances decision-making at all levels of the organization by providing real-time, data-driven insights. It allows executives to make more informed strategic decisions based on comprehensive market and customer data. At the operational level, it enables faster and more accurate decisions in areas such as risk assessment, product pricing, and resource allocation. 

Top applications of AI in fintech

Artificial intelligence has several practical applications throughout the financial world. Most use cases of AI in fintech are related to data and AI’s ability to analyze that data efficiently and cost-effectively. Financial institutions often manage large pools of information but don’t do much with it because it takes a lot of time and resources to go through all data points and turn them into valuable insights. This is where AI comes in. It can effectively analyze large amounts of data, enabling faster decision-making and leading to improved operational efficiency, enhanced risk management, and customized user experiences.

Fraud detection

Fraud detection is one of the most significant concerns for financial institutions. It’s challenging to combat this many-headed hydra that can take various forms – identity theft, account takeover, skimming, fund transfer scams, and direct theft of funds. Given the vast number of daily financial transactions, it’s practically impossible to manually analyze every operation to spot anomalies.

To effectively tackle these issues, fintech companies are implementing AI-based systems that can analyze numerous data points, watch for suspicious activity, and flag transactions that deviate from the norm. There are several applications of AI that are making the most significant impact in the fight against financial fraud:

• Guard duty. AI is good at monitoring fraudulent transactions, filtering spam messages, blocking harmful content, and identifying malware. It can also recognize patterns of social engineering and alert users of potential threats. 
• AI vs AI battle. While AI enhances fintech solutions and systems, fraudsters also leverage this technology. Recent advancements in this field, such as deep fakes, voice cloning, and tailored emails created with GenAI, have increased the volume and sophistication of fraud and scams. However, financial institutions can beat the criminals at their own game by using artificial intelligence technologies to identify synthetic content like fake images and voice IDs and distinguish between trustworthy and untrustworthy content.
• Freeing up investigative resources. AI employs sophisticated modeling techniques, combining various data types to provide comprehensive views of high-risk behaviors. This improves operational efficiency by automating repetitive tasks and enabling fraud analysts to deal with complex cyber threats.

Data processing

The volume, variety, and complexity of data flowing into the financial sector have changed significantly in recent years. As companies become increasingly data-driven, the cost of errors caused by poor data is too high to ignore. Hence, 50% of companies plan to implement modern data quality solutions to support their digital initiatives.

The emergence of AI and ML tools enables fintech companies to streamline the data quality management process. These technologies automate quality checks, identify patterns and anomalies, and prioritize remediation efforts in real time. They continuously analyze data as it enters the systems, allowing for immediate detection and fixing quality issues before they can cause any problems downstream. 

Automated financial reporting

Financial institutions collect large volumes of information from various sources, which is then analyzed and turned into comprehensive reports. AI models enable the generation of up-to-date financial documents on demand, supporting timely decision-making. They also enable financial institutions to produce accurate reports much faster and identify market trends and patterns that might otherwise be overlooked.

Automated financial reporting minimizes errors associated with manual data entry and calculations. This helps to save time and frees up financial professionals to focus on more strategic tasks. In addition, AI-generated statements can be tailored to the specific needs of different stakeholders, focusing on financial metrics and insights relevant to them.

Loan underwriting

AI models can run on information gathered from diverse data points to conduct credit underwriting and make lending decisions. Typically, personal loan applications are under review for seven days or more since the process requires the involvement of several specialists. AI has significantly enhanced credit-approval turnaround time, particularly by automating document scanning and manual inputs involved in gathering relevant data. Today, most applications only take a few minutes to complete, and the loan approval process generally takes up to two days.

AI algorithms can analyze a borrower’s income level, past credit history, credit score, and some non-traditional sources like social media posts and utility payments to provide a more comprehensive and accurate assessment of their creditworthiness. This technology enables financial institutions to make faster, more informed lending decisions, significantly reducing the time and cost associated with loan processing. Industry data from Deloitte suggests potential cost savings of up to $31 billion in underwriting and collection system expenses by 2030.

Robotic process automation

Robotic process automation (RPA) is a technology that leverages capabilities of software robots and AI agents to automate high-volume, repetitive tasks that previously required human intervention. RPA streamlines various financial processes, including customer onboarding, security checks, loan applications, and routine customer queries. 

This technology reduces the risk of human errors in critical financial operations and accelerates processing times, relieving teams’ excessive workload. Thus, staff can be allocated to other priority tasks that do require human input, leading to increased productivity and ROI. This shift in work also has a positive effect on employee satisfaction.

Algorithmic trading

Algorithmic trading is a method of executing trades in financial markets by enabling high-speed, data-driven decisions that outpace human capabilities. It uses advanced machine learning algorithms to analyze market data, identify patterns, optimize investment strategies, and build portfolios.

Algorithmic trading systems can simultaneously process multiple market variables, like price movements, trading volumes, and news sentiment, to make split-second decisions across various asset classes. These systems continuously learn and adapt to changing conditions, improving their performance over time and potentially generating higher returns.

FAQ

What is the future of AI in fintech?

AI is poised to transform the fintech industry by enabling personalized financial advice, intelligent automation, and predictive analytics to enhance decision-making.

What are the best practices for integrating AI into existing fintech platforms?

Some of the best practices for integrating AI into fintech platforms include ensuring data quality, adopting a phased approach, fostering collaboration between AI experts and domain specialists, and continuously monitoring and refining AI models.

What are the challenges of implementing AI in fintech?

One of the  main  things fintech  firms  often  struggle with  is ensuring high-quality and availability of data which is a critical prerequisite for successful AI integration. Another  challenge financial  institutions  face  is navigating the complex regulatory landscape and adhering to data privacy, security, and ethical guidelines. On top  of  that they  need  to  find and retain talent with the right blend of technical and domain-specific skills to integrate AI into their platforms.

What are the key differences between AI applications in fintech and traditional finance?

AI applications in fintech are typically more agile and innovative, designed to disrupt established financial services, whereas traditional finance often aims to enhance existing processes. Fintech firms generally prioritizes user experience and rapid deployment, while traditional financial institutions emphasize stability and long-term reliability.

Conclusion

Artificial intelligence has emerged as a transformative force in fintech, revolutionizing operations, enhancing customer experiences, and driving innovation. From automated data processing and advanced credit scoring to algorithmic trading and personalized financial services, AI is reshaping every aspect of the financial services. 

As the financial landscape continues to evolve, embracing AI is a necessary step for companies looking to remain competitive and relevant. However, implementing AI solutions effectively requires expertise, experience, and a deep understanding of both the technology and the financial sector.

This is where Neontri can definitely provide assistance. Our team brings a wealth of experience in developing and implementing AI solutions specifically tailored for the fintech industry. By working with Neontri, companies can:

• Access cutting-edge AI technologies and expertise without the need to build these capabilities in-house.
• Ensure compliance with regulatory requirements while leveraging the full potential of AI.
• Implement secure AI solutions that seamlessly integrate with existing systems.
• Benefit from ongoing support and continuous improvement of AI implementations.

Reach out to us to learn more about the transformative capabilities of AI in fintech.

Next Steps in Learning

AI-Powered  Bank Transaction Categorization
Autonomous AI Agents