AI in banking: A new rise for banks

Author —Rajiv C. J8 mins read06 Jun 2024

Artificial intelligence (AI) is gaining immense popularity, impacting every industry as well as our daily lives. The banking sector is no different. Banks deploy AI technologies like machine learning (ML), computer vision, and natural language processing (NLP) to transform how they work and to stay relevant. But how is AI used in banking?

Banks use AI-based systems to cut down costs by increasing their productivity and making decisions that are tedious for humans. Additionally, artificial intelligence in banking systems assists financial institutions in risk management and adhering to regulatory compliances while aiding them to boost security by detecting fraudulent actions. Why must banks become AI first?

Why must banks take the AI-first approach?

Banks continue their technologies and innovations adoption over the ages to redefine their customer engagement. Banks introduced ATMs in the 1960s and electronic card-based systems in the 1970s. At the start of the 2000s, we saw the rise of 24×7 online banking, followed by smartphone-based “banking on the go” in the last decade. How is AI changing the banking industry?

AI, accompanied by the availability of vast amounts of data and reduced storage costs, presents a significant opportunity for banks to harness this transformative technology. AI and its underlying technologies lead to greater automation and lesser human effort at banks, leading to fewer errors and cost savings. Improving the bank’s decision-making capabilities increases speed, efficiency, and accuracy. According to McKinsey, the potential of analytics and AI  for banking could reach up to $1 trillion.

AI enables banks to offer extreme personalization of services and customer support, thus boosting revenues. Moreover, banks can reduce their error rates and help with resource utilization. Financial organizations can now unravel new and previously unrealized opportunities. In short, AI helps banks achieve new heights in four outcomes – increased profits, deep personalization, swift innovation opportunities, and unparalleled omnichannel experiences. It’s high time for banks to take the AI-first approach. What are the benefits of AI in banking?

How AI transforms the banking sector?

AI-first banks are leveraging the technology to counter various competitive threats and deliver rising customer expectations. AI and allied technologies offer intelligent business propositions that blend with banking capabilities and relevant financial products and services. Here are a few applications of how AI is influencing the banking sector.

Fraud detection and cybersecurity

Banks handle millions of transactions daily online or via mobile apps, including bill payments, deposit checks, withdrawals, account transfers, and more. What is the future of AI in banking? With the high amount of transactions, the risk of cyberattacks and fraudulent dealings is high, forcing banks to increase their cybersecurity and fraud detection efforts.

Artificial intelligence is perfect for cybersecurity, helping banks improve their security, track loopholes in their system, and reduce risk rates. AI and ML can detect fraudulent movements, alerting banks and customers instantaneously. AI’s ability to monitor activities continuously enables banks to take preemptive action before affecting the internal systems.


Chatbots are indeed one of the finest practical examples of the application of AI in the banking industry. The chatbots can perform 24×7, in comparison to their human counterparts.

The bots learn the usage patterns of a particular customer and understand their needs efficiently. Banks ensure that the chatbots serve their customers around the clock. For example, the chatbot handles tasks like upgrading the security of credit cards and debt reduction on them. On understanding the customer’s behavior, the AI-powered chatbot offers personalized support and recommends financial products and services that suit them.

Smart loan lending decisions

Lending loans and managing credits are some of the most critical banking functions. AI assists financial institutions in making informed decisions regarding safe and profitable loan lending. As of now, banks have limitations in checking credit scores, credit history, and trustworthiness of an individual or a company. However, the current system is error-prone, missing creditors and real-world transactions.

An AI-based system tracks and observes the patterns of a customer and their credit history to decide their trustworthiness. The system generates a credit score and can warn banks about specific customer behaviors, helping them make better loan and credit decisions.

Tracing market trends

AI assists banks in evaluating the current market trends by analyzing and processing bulk volumes of information from multiple sources. Data analytics helps an organization learn the current market trends, currencies, and stocks. Additionally, AI and advanced machine learning help financial organizations assess market sentiments and investment options. The AI system suggests the best time to invest in particular stocks and warns when there is a potential risk. Artificial intelligence speeds up a bank’s decision-making according to market trends.

Regulatory compliances

The banking industry faces tight regulations from governments as a measure to ensure that people do not misuse financial institutions and that banks have acceptable risk profiles to evade large-scale defaults.

Most banks have an internal team that handles these regulations and compliances. However, the manual process is tedious and time-consuming. These compliance regulations face frequent changes, forcing the bank to update its policies and laws frequently. AI, along with natural language processing (NLP), reads the new compliance regulations to boost the bank’s decision-making. Though the role of a compliance analyst is irreplaceable, AI speeds up the process and makes it more efficient.

Better risk management

The banking and financial sector is always prone to external factors like natural calamities, political unrest, and currency fluctuations. Financial must be extra cautious during these times, particularly in decision-making.

Artificial intelligence plays a vital role during these testing times, with data-driven analytics giving financial organizations a clear insight into the future outcome and making timely decisions.

The power of AI is not limited to external factors alone. AI can understand the chances of a client not repaying the debt based on their behavioral patterns and smartphone data.

Process automation

Robotic Process Automation (RPA) is one fascinating aspect that disrupts the banking sector, bringing maximum operational efficiency and accuracy while reducing time and cost. RPA automates tedious and repetitive tasks, allowing the workforce to concentrate on more complex jobs. For example, RPA learns and automates document reviews and handles data from them much faster than a human. As for a bank, this reduces the time spent by the workforce, hence maximizing their efficiency.

Becoming an AI-first bank

Now that we have seen the benefits of AI for financial institutions, let’s see how banks adopt the technology on a broad scale and address all the challenges.

Build a strategy revolving around AI

Every deployment starts with a strategy. While pivoting the bank to an AI-first approach, it’s important to develop an enterprise-level strategy that follows the values and goals of your organization.

To begin with, conduct market research to find the gaps in your system that AI can fill. Ensure that your strategy adheres to all government regulations and policies. Refine the internal practices and information regarding handling data, people, and infrastructure. The knowledge comes in vital when adopting AI across the entire organization.

Devise a use case-driven method

Once you have a strategy, identify the best AI opportunities and align them with your process and goals. Evaluate the extent of implementing AI solutions within the existing or modified banking processes.

To identify the potential of AI and ML, the tech team must run enough feasibility tests. Explore all aspects and find their gaps before selecting feasible use cases. Once deciding the use cases, map out the talent for AI implementation. You can either have a dedicated in-house team or outsource the work to a trustworthy technology partner like PiServe.

Build and deploy

With a solid plan, the bank can now focus on with developing and deploying AI. First, build full-fledged prototypes to understand the technology well by compiling relevant data and feeding it into the system. The data must be accurate as the system analyzes and trains upon it.

Once the bank completes the step, interpret the results and understand how it performs in the real world. Then, the last step is deploying the trained model.

Monitor continuously

The role of artificial intelligence in banking is not complete with its implementation. AI-first banks require constant monitoring and calibrations, creating a design cycle to observe and evaluate the AI model. The monitoring helps banks find and neutralize cyber threats and get sharper in executing tasks.


Artificial intelligence is an inevitable part of modern-day banks. It’s high time for financial institutions to pivot to being AI-first. Having doubts about the benefits of AI or how to become an AI-first bank? Let’s discuss taking a step further.

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