By Genify on 27/04/2023, 00:30:00

The Role of Artificial Intelligence in Digital Banking Fintech

Artificial intelligence (AI) is at the core of digital banking fintech due to its various roles. AI in all its forms is leading innovation and advancement in the digital banking industry, elevating fintech services and achieving the best possible customer experience. This article will explore the role of artificial intelligence in digital banking fintech – more specifically, the role of machine learning. Continue reading for further insights. 

Digital Banking Fintech Definition

Digital banking fintech services have become in high demand over the past few years as they offer customers much convenience, flexibility, and online access to various banking services. To clarify, digital banking fintech implements digital technologies to facilitate multiple banking and financial services and activities. 

On the other hand, fintech, which is short for financial technology, describes technology-based innovations in the financial services industry. In other words, digital banking fintech is a subset of digital technology dedicated to providing banking services and activities, and some of them are: 

  • Online banking.
  • Mobile banking.
  • Digital payments.
  • Peer-to-peer (P2P) lending.
  • Investment management. 

Such financial services are provided by startups or established financial institutions that are using technology to improve their services and compete with traditional banking. 

Definition of Artificial Intelligence (AI) 

AI, or artificial intelligence, is a computer science field that enables machines to analyze data, make predictions, and take actions based on said analysis and algorithms. To put it differently, AI gives machines the abilities characteristic of human intelligence, such as learning, problem-solving, decision-making, and natural language processing.

These abilities are optimized in various fields, including digital banking fintech services. It is worth noting that AI algorithms are designed to learn from data and improve their performance over time, allowing machines to adapt to changes, making more accurate analyses, predictions, and decisions. 

That said, several branches of AI, including natural language processing and machine learning, have a significant role and various applications in digital banking fintech. Below are some of them. 

The Role of AI in Digital Banking Fintech

The following points showcase the role of artificial intelligence and machine learning in digital banking fintech services: 


Machine learning technologies analyze vast amounts of customers’ data regarding their financial behaviors, preferences, and needs, which is the basis of personalization in digital banking fintech. To rephrase, machine learning analyzes data to enable banks and financial institutions to provide their customers with personalized services, recommendations, and products to fulfill their needs. These products include credit cards, loans, and saving accounts. 

On the other hand, chatbots and virtual assistants provide personalized financial advice by implementing algorithms’ analysis results. This way, they give customers the guidance they need to achieve their financial goals while attending to their investment preferences and risk tolerance.

Fraud Detection

Generally speaking, AI and machine learning algorithms learn the typical behavior patterns for each customer, enabling them to identify deviation from the usual patterns, which can indicate potential fraud. This AI application is known as anomaly detection. It is at the core of fraud detection since machine learning algorithms analyze data in real-time, detecting suspicious behavior and activity on the spot. 

Another AI application in fraud detection in digital banking fintech is predictive analytics. These analyses are based on historical data, and they predict future fraud trends, identifying areas of high risk. Moreover, AI is used in behavioral biometrics, which refers to analyzing each customer’s behavioral patterns, like typing speed, to create a biometric profile that can be used to detect unauthorized access to an account.

Risk Management

Risk management is one of the most critical applications of AI and machine learning in digital banking fintech. These intelligent technologies have a core role in minimizing risks for banks and financial institutions, assisting them in making informed decisions, reducing risk exposure and improving risk management strategies. 

AI is implemented in various ways to achieve risk management, including credit risk assessment, dedicated to analyzing customer data, such as credit scores, payment history, and debt-to-income ratios, using algorithms to assess the credit risk of potential borrowers. The same algorithms are used to analyze market trends and identify potential risks like interest rate changes and exchange rate fluctuations, which may impact a financial institution’s portfolio. 


AI and machine learning algorithms automate repetitive tasks and streamline processes, improving efficiency, reducing costs, and leading to a more personalized and seamless customer experience. These algorithms are used in several ways to ensure automation in digital banking fintech, including chatbots and virtual assistants, as they provide automated and customized assistance 24/7. 

Additionally, these algorithms enable customer profiling and segmentation by analyzing customer data and creating profiles and segments. This is mainly used in personalizing product recommendations. 


Artificial intelligence and machine learning applications vary, yet they work together to facilitate digital banking fintech services. In other words, they are the power force motivating the advancement of digital banking and making it great competition for traditional banking. 

Finally, Genify provides various APIs related to open banking, ideal for banks and fintech companies looking to elevate their services. Explore Genify’s APIs for more details. 

Give it a Try - It's Free

  • Explore sample enriched data & insights.
  • Query the API in a sandbox environment.
  • Explore the extended API documentation.
  • Test with up to 1000 transactions for free.
  • Access API keys.