Impact of AI on Banking & Insurance (2 Exciting Use Cases)

Impact of AI/ML on Banking & Insurance (2 Exciting Use Cases)
Digital Transformation

Impact of AI on Banking & Insurance (2 Exciting Use Cases)

Digital transformation is taking the financial industry by storm. Artificial Intelligence and Machine Learning are the red-hot technologies transforming the sector, reeling under the pressure of managing customers and their data.

Finance is a process-heavy industry and produces a lot of data. The industry has struggled to improve its data management over the years, yet failed due to lack of systems and tools. The real challenge is managing the data with the current systems, tools, and processes.

Now, the invasion of AI and ML has enabled Finance companies to improve their end-to-end processes and data management.

And the technologies are helping greatly improve the Finance company’s performance, efficiency, and customer satisfaction. With many benefits to offer, companies are heavily dependent on these futuristic technologies.

Here are some of the challenges the industry faces daily.

  • Managing customer data
  • Adjusting strategy to improve customer satisfaction
  • Making quick and decisions
  • Enticing customers with offers and discounts
  • Engaging customers
  • Serving customers
  • Risk management
  • Detecting fraudulent activities
  • Improving customer retention rate
  • Enhancing customer satisfaction, etc.

Consider any finance-based company – Banks, Insurance, etc., that faces the above-stated challenges daily and looks for solutions to negate them.

Before diving deep into understanding the impact of AI/ML in the Finance Industry, let’s throw light on their growth across different verticals.

Growth of AI/ML Across Verticals

The global artificial intelligence market size was valued at USD 93.5 billion in 2021 and is projected to expand at a compound annual growth rate (CAGR) of 38.1% from 2022 to 2030.

Growth of AI/ML across verticals

                                                              (Source: Grand View Research)

Growth of AI/ML in Finance Industry

The global AI in banking market size was valued at $3.88 billion in 2020, and is projected to reach $64.03 billion by 2030, growing at a CAGR of 32.6% from 2021 to 2030.

                                                                 (Source: Allied Market Research)

Impact of AI on Banking

Automated workflows and faster decision-making are two critical factors that help the Banking Industry better serve customers. Ever since AI/ML is added to the Banking strategy, the results have been excellent. The industry can automate many processes while reducing human-induced errors.

Here are a couple of AI use cases in Banking.

  1. Automated Credit risk assessment and data management

Credit scores play a critical role in loan sanctioning and settlement to individuals. Failing to lend to a potential customer can impact the bank’s performance, and funding a false customer with a poor credit score can equally impact a bank’s growth.

Assessing the credit score requires many factors to consider and analyze before arriving at the final value. It is a time-consuming and sensitive process involving many parameters.

With AI, both the above challenges are addressed. Artificial Intelligence systems in the bank enable the workforce to calculate the individual credit score of every customer by weighing different parameters.

The credit report for an individual often provided by AI-based systems is 100% accurate as the technology considers real-time scenarios before arriving at a credit score.

This way, it is easy to manage the credit risk.

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Automated fraud detection in Banking

Assessing and reducing potential fraud is critical for the bank’s growth and for preventing losses. The industry faces many problems with phishing scams and other malware’s that create havoc in the systems, leading to potential losses.

On the other hand, some customers provide fake details to acquire fund from the banks, contributing to massive losses.

AI-enabled automation will improve the fraud detection process and reduce losses significantly. AI technology matches the requirements with the rules, and any deviation concerning preset rules will be monitored and informed to bank staff.

Similarly, phishing threats can be reduced by setting up AI alert systems for monitoring any abnormalities in the core of programming.

So, both challenges will be negated by AI/ML technologies.

Impact of AI on Insurance

Insurance is another industry that massively benefited from AI/ML technology investments. Especially customer claims and underwriting are processes that are improved significantly with the help of AI/ML technology-enabled systems.

Here are the two use cases of AI/ML in Insurance.

Automating underwriting process

Underwriters help the bank with detailed assessments against the risk of providing a loan to any individual. The process involves many factors and everything should be analyzed perfectly before lending the amount to any customer.

The complicated process involves much effort to explore the risk and reduce any potential loss in the future.

The introduction of AI in the process has completely changed the way it works. With the help of AI and ML automation, the underwriters can accurately assess the risk associated with any individual and ensure whether they are the right candidate for the lending.

This simplifies the task of underwriters and fast-forwards the process. It also improves the data security of the process significantly.

Automating claims process

Claim assessment and settlement is a vast process in the Insurance industry. Claim executives have to sweat it out to assess and evaluate the customer data before opting for settlement.

Any discrepancies or false data could impact the Insurance industry as they will be staring at huge losses. The industry is looking for solutions to address claim management risks and seamless claims settlement.

AI/ML is changing the entire process and enables claims executives to perform the assessment and settlement part perfectly.

Automating the process with AI and assessing the critical parameters against the claim data helps the staff to decide quickly and efficiently. In other words, the claims process will be decided based on 100% accurate data.

So, managing the claim process and analyzing data is seamless and hassle-free with AI/ML.

These are only some of the use cases of AI/ML in the field of the Banking and Insurance Industry. I have just shared a few with you.


AI/ML is a potential game changer for almost every industry, and Finance isn’t an exception. The industry is investing a lot in the above-said technologies to automate and manage complex processes better and more efficiently.

As both these technologies are known for quality and accurate outputs, any industry using them will be benefited staggeringly.

If you’re heading banking or insurance operations, you must update your existing strategy by adding AI/ML.

Are you planning to improve operations without reducing data security? Try AI/ML now.

Want to know more? We’re all ears.

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