Computers and TechnologyTechnology

Artificial Intelligence in Banking: The Role of AI in Banking and Finance Sector

“Predicting the future isn’t magic, it’s Artificial Intelligence”. 

                                                                                                       —Dave Waters

Do you know Artificial Intelligence (AI) is the next big thing in the banking and finance sector? Yes, you heard it right! The banking and finance sector is all geared to embrace Artificial Intelligence. 

The past year’s achievements of Artificial Intelligence (AI) in the banking and financial sector will amaze you, and it’s just the beginning. Check out the technology’s astounding market growth below.

AI in Finance


Even in 2020, AI alone had generated $7.91 billion in the finance sector. This staggering count will reach $130 billion in 2026 at a (CAGR Compound Annual Growth Rate) 23.17%.

Indeed, the role of AI has gained immense importance with expanding horizon of banks.  Now, this sector is looking for new ways to make itself stand out amidst the tough competition. And, these technologies are competent enough to give 

So, any guesses what exactly happens when AI blends with finance? How do they complement each other? What’s new AI has in its store for the finance sector in 2022? Let us dig deeper into these questions to understand this symbiotic relationship with some case studies.

The Role of Artificial Intelligence (AI) In The Banking & Finance Sector

The finance sector has become a key player in AI adoption. Below, I’ve listed the essential roles technology plays in this industry. Have a look.

Role of AI In The Banking & Finance Sector

1. Risk Management That Mitigates The Impact of Non-performing Assets:

AI can learn from past data and predict future risks of defaulting loans. This way, it helps financial institutions mitigate the risk of non-performing assets with enhanced accuracy. 

Moreover, banks can predict a customer’s behaviour based on their financial profile and the services they already use. By doing so, banks can productively manage customers’ accounts and ensure that there are no unpaid loans or delinquent credit cards.

Cognitive Computing in Finance

For example, Deutsche Bank has used cognitive computing software to assess its risk exposure and categorize many of its loans into “standard” or “watch” categories. This has helped the bank have an unrivalled 360-degree view of its significant exposures and reduced its dependency on complex and time-consuming manual checks.

2. Fraud Detection To Save Organizations from Huge Financial Loss:

Artificial intelligence In Banking sector


Artificial Intelligence and Machine Learning have enabled banks to develop robust fraud detection mechanisms that save huge losses. Now financial organizations can identify anomalies in real-time and take necessary countermeasures before the fraudsters make away with the money.

Matercard Decision Intelligence


For example, MasterCard’s Artificial Intelligence (AI) tool called Decision Intelligence helps the company fight against fraud by constantly learning from the organization’s transaction data. It can quickly differentiate between “good” and “bad” transactions, including those that are potentially fraudulent.

3. Customer Service for Interactive Customer Experience in Real-time

AI Chatbot Statistics

AI personalizes customer service in real-time with advanced data-driven insights. It automates routine questions, eliminates service agents’ repetitive tasks, and reduces the overall effort required for customer care.

Banks use AI chatbots to answer simple customer queries or questions autonomously while also interfacing with their customers more naturally through voice assistants like Amazon Alexa or Google Home.

For example, in Japan, SBI Sumishin Net Bank uses AI-powered chatbots to answer customer queries in natural language. They also integrate an AI feature called Emotional Analysis into their chatbots that helps detect customers’ emotions and provides them with the correct information accordingly.

4. Marketing  to Personalize Messages for Acquiring New Customers

AI provides relevant banking offers or messages that appeal to the customers’ interests. It generates personalized messages by analyzing the activity on various accounts, data of an individual’s social media feeds, etc. And then accordingly deliver the relevant offers via email or text message.

It makes banks ahead of their competitors as they can now interact with customers to upsell or cross-sell using advanced marketing techniques during the customer journey. 

AI in Banking and Finance


For example- Wells Fargo & Company sent personalized messages with AI to customers who were most likely to upgrade their credit card accounts. It brought about $80 million new clients and earned $30 million by sending timely offers through email or text message.

5. Supervise & Optimize the Performance of the Organization to Improve Results

Using AI, firms can calculate and optimize a range of critical business metrics to improve their performance. It ensures that the organization is better equipped to achieve its daily goals, delivering improved results for all stakeholders.

In finance, AI-based technologies make it easier to keep track of a company’s overall health by closely monitoring expenses, inventory, personnel, and any other business-related metrics.

For example, US-based financial services firm USAA now uses cognitive computing software from IBM to help monitor, predict, and prevent a wide range of customer issues across its banking, insurance, and investment arms.

6. Strategic Decision Making for Foresight in the Future

AI can extract valuable insights from massive amounts of data to provide companies with a better prospect perspective. By leveraging advanced technologies, organizations can effectively make better decisions to achieve future goals.

In the financial sector, AI-powered tools are being used for market forecasting and risk analysis by getting insights from recent trends in economic cycles.

For example, Nordea Bank uses the IBM Watson Analytics platform to set up early warning indicators that help them predict market movements based on the analysis of unstructured data.

7. Process Automation to Reduce Manual Intervention

Artificial Intelligence in banking and finance

AI enables an organization to automate critical but time-consuming business activities, which helps free up valuable resources. It improves flexibility and drives better results by either automating a particular task or freeing human workers from performing menial tasks to focus on more important goals.

The technology also aids in reducing bottlenecks in the workflow as it processes the transactions or customer requests more quickly without requiring human intervention.

For instance, the State Bank of India (SBI) uses AI-based automation technologies like RPA to automate routine customer service requests. It helps reduce dependency on human workers, drives efficiency in operations, and reduces turnaround time for these transactions.

8. Cyber Security to Prevent Hackers from Stealing Critical Information

Hackers are always looking for new ways to break into networks and steal data. With the advent of IoT, hackers now have their hands on a range of new tools, making it easier for them to gain access to critical information.

Organizations can use AI technology in conjunction with cyber security solutions to proactively discover vulnerabilities before hackers exploit them.

For example, the AI-powered Cylance platform uses predictive analytics to monitor network activity and identify anomalies in user behaviour. Once any malicious patterns get recognized, the system instantly alerts IT admins to take necessary action before it’s too late.

9. Automated Decision-Making To Get Faster Results On Real-time Data

Artificial Intelligence in Banking sector

AI-based systems can make much quicker decisions based on real-time data inputs. It boosts overall performance and helps companies drive better results with minimal delays.

Machines can bring much more agility to an organization’s operations by making automated decisions quickly. It allows organizations to readjust their plans according to changes in the market and get faster results.

For example, AXA uses Iris’s AI platform to automate manual data processing and offer real-time insurance quotes. The tool provides drivers instant quotes for new policies or additional coverage based on the car model and other risk factors like location and time of day.

10. Predictive Intelligence To Turn Vast Data Into Actionable Insights

AI-powered predictive intelligence tools can turn vast data sets into actionable insights. These technologies use learning algorithms to process large volumes of structured and unstructured information from across the organization’s entire value chain.

The insights generated by these systems help organizations make better decisions, increase operational efficiencies, boost performance, and gain a competitive edge over rivals.

For example, SAS Analytics uses a cognitive solution to ingest and learn from data and recognize trends to aid future predictions. It helps businesses identify critical issues that could impact their operations and enables them to take timely action.

11. Video Analysis & Personalized Customer Experience To Spur Growth

The rise of digital video consumption has opened up new opportunities for businesses in the media and entertainment industry. AI-based tools are helping companies generate insights from huge volumes of customer-generated videos to drive engagement.

Banks and financial institutions can use video analysis tools like PivotView to understand their customers and identify which products and services they prefer.

A bank can generate essential insights from recorded customer service calls with this solution. These insights built a list of recommendations that helped the company better engage with its clients and increase customer satisfaction.

12. Higher Profit Margins with Smart Data Analysis

AI-powered tools can enhance an organization’s profitability by helping it gain more accurate insights from its data. Predictive and prescriptive analytics tools can help companies better forecast industry trends and make more intelligent business decisions based on real-time data inputs.

By enabling organizations to make smart decisions, AI solutions can boost overall performance and improve their bottom lines.

For example- SBI Remit, a division of Japanese financial services giant SBI Group, launched Finatext in 2016. The solution uses AI and machine learning algorithms to automate traditional remittance transactions data analysis. It helps the company gain better visibility into its customer behaviour, improve profitability by reducing operational costs, and make smarter business decisions.

Read Also: Usage of Artificial Intelligence in the Digitalized Retail Industry

Closing Thoughts

After reading this blog, it’s pretty evident that Artificial Intelligence significantly impacts the banking and finance industry. It transforms the customer experience, improves operational efficiencies, and enables organizations to drive better business results.

Banks can gain deeper insights into customer behaviour by bringing cognitive technologies like machine learning and natural language processing to the table. The technology also eliminates operational errors and quickly identifies potential financial risks.

Therefore, it’s high time for financial institutions to take proactive measures and embrace AI to gain a competitive edge. Also, if needed assistance, consult the banking and finance software development company in India. They’ll help you out better.

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