AI in Fintech: Top 5 Use Cases | Dexola

In mere years, artificial intelligence has become a driving force in global technology. Even in a traditional-ish citadel of finance, AI reigns supreme, having attracted over $12.1 billion in FinTech investment in 2023. No wonder, since AI in FinTech allows optimization of many work areas, from cybersecurity to improving user experience. 

In particular, AI in FinTech helps to:

• Automate processes
• Process large volumes of data
• Improve protection against cyber threats
• Improve customer experience, create customized offers, and extend them
• Optimize credit scoring
• Identify anomalies (particularly in applications and web services), etc.

Let’s take a look at the general picture of AI in FinTech and the top use cases, from the cybersecurity watchdogs to personalized concierges that are supposed to help each of us get rich. 

AI in FinTech in numbers

AI in FinTech is not a trend anymore; it is fundamentally transforming the financial industry and our financial habits. AI brings unique benefits of efficiency, cost reduction, high-quality innovative services, comprehensive access, and inclusion in the financial system. 

A few statistics prove the adoption rates of AI in FinTech:

• The market size of artificial intelligence in FinTech was estimated at $42.83 billion in 2023, which grew to $44.08 billion in 2024.
• Top areas of AI in FinTech are security (around 13%), market research & data analytics (almost 15%), lending automation (17%), customer credit checks (13%), and claims assessment automation (almost 20%).
• Over a third (36%) of consumers are willing to manage their finances using Gen AI. And for those under 50, this number leaps to a striking 52%.
• 13% of organizations employ AI and ML to detect and deter fraud, with another 25% planning to do so in the next year or two, representing roughly 200% growth.
• In fiscal year 2022, the U.S. government spent $3.3 billion on AI contracts.

Summing it up, AI in FinTech is set to contribute to the digital transformation of finance and shape global socio-economic development.

Use Case #1: AI-powered chatbots

Banks are always trying to improve the user experience. That’s how they came up once with online banking, which is more convenient than a brick-and-mortar bank. 

Still, not all users are familiar with using websites or software to find the necessary information, so it’s challenging for banks to simplify the user interaction experience. AI helps with it. 

Bank of America is using artificial intelligence to provide smarter service to its 25 million mobile banking customers. It has launched Erica, a financial services virtual assistant that provides intelligent services through voice interaction. Imagine voice-enabled search of account transactions, useful bank information, analysis of spending habits, and guidance on financial decision-making.

BofA clients interact with Erica 56 million times per month. They most often use Erica to:

• Monitor and manage their subscriptions, such as food services and gym memberships – 3.6 million times per month
• Understand spending habits – 2.1 million times per month
• Stay informed of merchant refunds – 863,000 times per month
• Stay on top of upcoming bills – 332,000 times per month
• Check their FICO score – 267,000 times per month

Erica combines artificial intelligence, predictive analytics, and natural language processing under the hood of a virtual financial assistant. It also links with Bank of America’s financial education platform, Better Money Habits, to customize financial advice based on users’ spending habits.

Erica’s impressive capabilities stem from its use of Natural Language Processing (NLP), which enables it to conduct human-like conversations and create interactions with users. Predictive analytics enhance this, allowing Erica to deliver personalized financial advice by examining users’ transaction histories and financial behaviors.

BofA trained Erica using 10 million hours of customer interactions, ensuring it could address the most common customer inquiries. This vast dataset, combined with numerous other ML inputs, allowed the bank to precisely program the queries Erica needed to handle.

To top it all off, Erica operates within a robust security framework, ensuring that sensitive financial information is protected while facilitating secure interactions.

Use Case #2: Fraud prevention as a service (FaaS)

The average U.S. FinTech company loses $51 million annually to fraud. According to PwC research, 51% of businesses have encountered financial fraud within the last 2 years. Banks are constantly threatened by malicious individuals who try to steal money or customers’ personal information. To protect against fraud, banks need modern, precise tools.

AI helps banks by using behavioral analytics, biometrics, geolocation, and other technologies to verify customers’ identities and confirm the legitimacy of transactions. It also detects anomalous or suspicious transactions and blocks them or asks for additional verification.

Feedzai is an example of a solution that applies AI to protect the banking sector from fraud. It is a platform that processes huge amounts of data from various sources and accurately identifies patterns of fraudulent behavior. Feedzai has partnered with renowned banks such as Citi, HSBC, and Standard Chartered.

Feedzai operates on the principle of RiskOps, a methodology that integrates risk management into everyday operations with a focus on fairness and customer-centricity. RiskOps empowers financial institutions to detect suspicious activities, identify fraudsters, and combat fraudulent activities.

Feedzai’s platform uses ML to rapidly process events and transactions, delivering results through a human-readable semantic layer. It processes and integrates multiple data streams and insights from various sources to create detailed customer profiles, facilitating the identification of fraudulent activities and potential scam victims.

To mitigate fraud and money laundering risks, Feedzai collects data from various sources, including cross-channel, cross-product, and third-party data. This comprehensive data collection helps distinguish between legitimate and fraudulent transactions, providing a holistic view of each customer’s interactions with the bank. Detailed profiles enable the identification of customers at higher risk of falling victim to scams, even before they are targeted.

Use Case #3: RPA (Robotic Process Automation)

RPA (Robotic Process Automation) is a technology that completes repetitive work. It drives the intelligent transformation of operations in the financial industry. In FinTech and AI story this is probably the oldest use case.

Here’s how repetitive work in FinTech has changed with the introduction of AI:

Category	Process	(Manual)	Process	(AI)
Data Entry and Processing	Employees input data manually.	Hours to days	AI systems automate data entry using NLP and OCR.	Minutes to seconds
Fraud Detection	Analysts review transactions for suspicious activity manually.	Several hours to days	Machine learning algorithms analyze transaction patterns in real-time.	Real-time to minutes
Customer Service	Representatives handle queries via phone or in-person.	Immediate per query, potential long wait times	Chatbots and virtual assistants handle queries instantly.	Immediate, 24/7 availability
Loan Processing	Loan officers verify details and assess creditworthiness manually.	Days to weeks	AI systems assess creditworthiness and make decisions automatically.	Minutes to hours
Regulatory Compliance	Compliance officers review transactions and documentation manually.	Weeks for thorough audits	AI monitors transactions and documentation for compliance in real-time.	Real-time to hours

This table compares the differences between manual and AI processes in various business operations, including the time required for each method.

The Industrial and Commercial Bank of China (ICBC) started working on RPA in early 2019. It has already formed large-scale applications in various business areas. Among those the automation and intelligence construction of customer service and marketing, operation and management, risk prevention and control, etc. 

According to Gartner, around 80% of finance leaders have implemented or are planning to implement RPA.

Use Case #4: Personalized offers

Banks seek to offer their customers the most appropriate and favorable products and services to meet their financial goals, interests, and capabilities. To do this, bank workers need to know their customers well, analyze their income, expenses, payment history, and monitor market conditions. 

AI can help banks do this by using Big Data, machine learning (ML), and analytics to create personalized financial recommendations for each customer. For example, AI can suggest the best loan or deposit rate, a discount or a bonus, or another product that will benefit the financial well-being of a particular customer.

One solution that uses AI and FinTech to personalize offers in the banking sector is Personetics. It is a platform that analyzes customers’ behavior and needs in real-time and offers suitable products and financial management tips. Personetics works with major banks such as U.S. Bank, RBC, and Santander.

Personetics’ AI-based models automatically map customers’ transactional data, cleansing, categorizing, and enriching raw data. This process provides clarity for end customers and helps financial institutions reduce transaction disputes. It also offers pre-designed UX widgets and flexible customization options. 

Perconetics users also enjoy an extensive library of over 60 actionable insights and money management activity trackers in areas such as savings, budgeting, financial tracking, subscription management, and product recommendations. These tools enhance engagement, increase deposits, and drive cross-sell opportunities.

Use Case #5: Cyber defense

The most common use of AI for financial services is in cybersecurity.

Mastercard recently launched Scam Protect, a suite of specialized solutions based on advanced AI technologies to help detect and prevent fraud. The company said that by combining AI identity checks, biometrics, and open banking capabilities, Mastercard can help protect consumers from the full spectrum of fraud.

Visa, following Mastercard, announced the introduction of new mechanisms built on artificial intelligence to strengthen the fight against fraud in its portfolio of products aimed at business customers. According to Bloomberg, Visa prevented fraudulent payments totaling $40 billion in 2023, double the total for the previous 12 months.

The first planned mechanism is the Visa Account Attack Intelligence (VAAI) Score. It will extend the existing capabilities of Visa’s AI tools to identify and block fraud in digital transactions where a physical credit card is not used. It reduced the false positive rate by 85% compared to other risk models.

The second tool will be optimized for instant use in account-to-account transactions. At the same time, Visa intends to make its existing products, notably Visa Advanced Authorization and Visa Risk Manager, available for non-Visa card payments.

How to apply AI in your FinTech products

With the continuous development of AI for financial services, institutions actively embrace change and capitalize on technological development opportunities. We see that FinTech is evolving, offering more efficient, secure, and personalized services to consumers worldwide.

We at Dexola are at the forefront, staying abreast of FinTech advancements. Our team is ready to innovate and collaborate, ensuring that the future of FinTech remains not just promising, but profoundly impactful. Feel free to contact us, and we will be happy to discuss how your future innovations can become a reality.

tems and workflows, minimizing disruption. Third, analyze the overall cost of the solution and the potential return on investment, ensuring it aligns with your budget and brings reasonable business value.

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