What is AI in banking and FinTech?

The term Artificial Intelligence (AI) used in the area of banking refers to the application of different technologies, including Machine Learning, Natural Language Processing and Predictive Analytics, within organizations that conduct business in finance to allow organizations to automate decision-making processes through the use of machine learning, detect fraud on-the-fly through the use of natural language processing, provide customers with hyper-personalized experiences through the use of predictive analytics, and improve operational efficiency through the use of artificial intelligence tools while still being compliant with applicable laws and regulations.

The reason AI is becoming important to financial institutions today is because estimates indicate that by the year 2030, there will be a global market for AI-in-banking valued at USD 143.56 billion with an annual growth rate (CAGR) of 31.8%. In addition to that, it is estimated that 90% of all banks currently utilize technology for fraud detection, and that generative AI applications alone could add between USD 200 billion and USD 340 billion per year in revenue to the global banking industry.

Today’s financial institutions (i.e., banks and FinTechs) utilize AI in banking and finance across all levels of their business operations and customer interactions, including real-time transaction monitoring (e.g., credit card fraud detection), AI-based credit evaluation processes (i.e., evaluating creditworthiness), hyper-personalized mobile banking applications, and autonomous forms of AI (e.g., AI agents) that complete compliance work for banks and financial institutions.

The Emergence of Artificial Intelligence (AI) as a Critical Component in Banking

The U.S. is undergoing a digital transformation, as evidenced by the amount of time people spend on social media platforms such as Instagram and TikTok, where content creators are showing people how neobanks – like Revolut and Chime – provide services including real-time conversion of one type of currency for another and analyzing consumer spending habits (AI-powered) without end users even being aware of it.

This new technology will also be available as a competitive differentiator throughout the financial services space. FinTech organizations are rapidly accelerating their use of AI well beyond what traditional banks have done so far in their execution of Artificial Intelligence.

In reality, about half of all businesses have adopted AI into their operations, with 43% stating that it is an essential component to their business’s success. As banks and FinTech firms continue to develop and implement their AI strategies, those final few who haven’t created an AI strategy—often without support from an AI Development Company USA-will find it increasingly difficult to catch up with those firms who have.

Use Case 1: Real-Time Fraud Detection and Prevention

AI uses data to identify fraudulent transactions by scrutinizing billions of transactions in real-time and flagging anomalies with a 90–99% success rate when compared to traditional rule-based systems.

Fraud is now a global issue that doesn't just happen at the back end anymore. In 2025, businesses around the world lost an average of 7.7 percent of their annual revenues as a result of fraud. This was estimated to be a total of $534 billion. Fraud in the modern day increasingly involves the use of AI itself; criminals are leveraging AI in the form of deep fakes, synthetic identities and large-scale phishing attacks.

Banks are using advanced technologies in their fight against fraud as well. One of the largest banks in the world, JPMorgan Chase, utilizes the most powerful physical and cyber security systems to monitor transaction history, location and device disclosure through real-time means; while DBS Bank leverages their systems to make use of more than 1.8 million transactions per hour, using behavioral analytics.

The outcome? With AI-enhanced fraud detection systems implemented, participating banks have realized a 30% reduction in false-positive transactions and almost no interruptions to authentic bank customers.

Use Case 2: AI-Powered Credit Scoring/Lending

Use of machine learning models to assess a customer's creditworthiness based on alternative data (i.e. cash flow, employment history and behavioural signals), enabling lenders to make lending decisions based on faster, fairer, and more accurate methods than currently available in the market.

The current credit scoring systems are heavily dependent on historical scoring systems and therefore, millions of people do not have access to credit. The availability of AI has changed this. AI credit scoring models go beyond traditional credit scoring to obtain information regarding alternative signals including cash flow, employment volatility (i.e. changes in employment) and willingness to repay, resulting in lenders being able to better evaluate their risk when lending to thin-file and underbanked customers.

One example of a credit scoring engine created with machine learning in Southeast Asia was built by analysing 600 different data types with a 97% accuracy rating and had processed 500 million loan applications at seven banks. This level of accuracy is only possible with the use of modern AI development services, which are increasingly being integrated into mobile banking applications for instant loan decisions.

Use Case 3: Hyper-Personalization In Mobile Bank Experiences

Briefly, AI will analyze your spending habits, transaction history, and behavioral patterns and provide you with real-time, personalized financial insights, recommendations for products, and contextual nudges throughout mobile apps.

Mobile is no longer just a way to bank — it’s now the main way to bank. According to McKinsey, banks that have implemented a mobile-first integrated distribution strategy have seen a 10% to 15% increase in deposit balances; AI is the primary driver behind this.

AI looks at customer wishes, habits, and overall goals to create customized experiences for customers within mobile apps in real-time (i.e., products they recommend, help budgeting). One example of this is Bank of America's virtual assistant, Erica, who completes over 1 billion interactions per year with customers, providing them with 24/7 personalized assistance.

In the UAE, USA, and Australia, banks need AI-native partners who know the technology and understand the regulations of a given region to create successful AI solutions for their respective markets. That is exactly where Hyena AI's AI In Mobile Banking Apps Development capabilities come in.

Use Case 4: AI-enabled Chatbots for Enhanced Customer Service and NLP-Driven Interaction

Websites that are powered by artificial intelligence (AI), such as chatbots and virtual assistants, can help customers get answers to common banking inquiries quickly at lower costs than traditional methods (up to a 30% decrease), leading to greater satisfaction for customers. According to Gartner, in the year 2027, chatbots will account for approximately 25% of all customer service transactions among organizations. The banking industry is already utilizing this trend.

One bank in the Middle East has implemented an AI-powered virtual assistant to identify customer engagement in order to provide services such as personalized card options, automated refunds, or simple repayment plans based on customer preference for Online Banking UAE.

In addition to answering user questions, NLP-based assistants are capable of analyzing user emotions and identifying possible frustration indicators in almost real time. The AI does not escalate issues without first determining whether a human touch would improve customer satisfaction or customer service.

Use Case 5 - Agentic AI in Compliance and Risk Management

Agentic AI systems will automatically analyse suspicious behaviour, identify instances of non-compliance and run through multi-step compliance processes, reducing the manual effort by 40% or more. Unlike traditional automation, agentic AI systems are able to plan, reason, take multiple actions at once (without having to follow explicit step-by-step instructions), provided they have clear constraints.

In the financial services industry, this means faster AML monitoring, automated KYC verification and real-time reporting on regulatory compliance. The agentic AI solution has already been successfully deployed at Wells Fargo through Google Cloud and at Metro Bank with Covecta to support their commercial lending pipeline, confirming its production capability for on-going use.

The Numbers That Matter

Why Different Approaches to Building Banking Solutions From Hyena AI

Hyena AI is an AI-focused Fintech software development company that creates fully integrated AI solutions for digital banks, digital wallets, and lending institutions, including end-to-end solutions for both traditional banks and financial technologies, in the USA, UAE, and Australia.

From building iOS and Android banking applications to designing full-service banking systems that utilize AI technology, the engineering teams at HyenaAI create production-ready AI solutions specifically designed to meet the needs of each bank, including custom fraud detection engines, smart banking applications, conversational AI chatbots, and agentic compliance solutions.

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