AI Solutions for Banking: Examples, Benefits, and More

AI solutions are revolutionizing banking use cases like KYB and KYC verification, business loans, and mortgage underwriting. They’re helping companies enhance operational efficiency, boost customer satisfaction, and reduce costs.

Here are some real-world AI applications that can show you how to streamline your business operations.

Key Takeaways

• AI solutions can streamline both simple and complex processes.
• AI can help with KYC and KYB in banking.
• Artificial intelligence helps companies gain a competitive advantage.
• Some of the largest banks rely on artificial intelligence to streamline processes, reduce costs, improve security, and improve customer experience, which leads to business growth.

8 Benefits of AI in Finance

1. Enhanced Security

Companies can use AI to identify and mitigate potential threats, as well as guard sensitive data and financial assets. 

• For example, AI solutions can help analyze transactions in real time and detect anomalies that could indicate possible fraud.
• They can also boost biometric security measures, such as facial recognition or fingerprint scanning, to reduce the risk of impersonation or identity theft.
• Finally, they can also initiate protocols when a threat is found to minimize damage and maintain the integrity of customer data and trust in financial institutions.

An added benefit is that AI algorithms continuously learn based on new data, improving accuracy and ability to predict and prevent fraud over time. 

2. Operational Efficiency

Banks can achieve faster operations by automating routine and complex tasks. The result is reduced time required to complete these tasks, reduced human error, and more reliable outcomes.

AI solutions can also analyze large datasets. This helps uncover insights quicker than traditional methods. This can help improve resource efficiency by predicting cash flow scenarios and optimizing asset allocation.

3. Real-Time Data Processing

AI solutions help financial institutions make informed decisions, identify patterns and trends, and find anomalies that human analysts might miss.

As a result, financial institutions can make changes, react promptly, and adjust trading strategies to manage risks effectively. AI solutions also improve customer experiences as they can analyze and process past transactions and interactions. Using this information, banks can tailor personalized products and services for their customers. 

The final benefit for customers is proactive, quicker support than with traditional methods.

4. Loan Underwriting

AI solutions enable more accurate and efficient credit risk assessment. AI solutions can analyze many more data points than human analysts and non-AI software – including credit history, income statements, social media profiles, and even behavioral data.

• This gives financial institutions a more comprehensive view of the borrower’s creditworthiness, allowing them to make more informed decisions. 
• Since AI solutions work quickly, analysis of required documents for loan underwriting is faster than ever. 
• Financial institutions also find it much easier to keep up with borrower’s data changes that can affect the risk factor.

With a focus on data and algorithms, bias is reduced in the underwriting process. 

5. Automated Customer Service and Customer Retention

AI solutions can provide customer support 24/7, which results in much quicker and round-the-clock assistance. Reducing the burden on human customer service representatives, AI can help automate routine inquiries. This includes balance inquiries and account updates to free up human agents, and more.

AI solutions can understand and respond to customers in real time with personalized and contextually relevant responses. While AI solutions deliver tailored support, customer agents have more free time to attend to complex inquiries.

Analysis of customer behavior data and behavior can identify at-risk customers and provide them with personalized offers and incentives, which leads to better customer retention. The combination of these two benefits helps financial institutions improve customer relationships and maximize lifetime value.

6. Accurate Credit Scoring and Market Predictions

AI solutions actively learn from new data and evolving trends, which ensures that credit scoring models adapt and improve over time. Credit scoring remains accurate even when economic conditions or borrower’s behavior change.

Analysis of data can go far to the point where AI solutions analyze sources like financial data, news articles, and social media sentiment. This helps make market predictions, identify trends, and spot anomalies much more accurately.

A combination of these benefits leads to better risk management, well-informed decision-making process, and better financial outcomes for both individuals and institutions.

7. Portfolio Management and Trade Optimization

AI can analyze information like historical performance, market trends, economic indicators, and individual investment goals and performance. With such information, financial institutions can make investment decisions much more easily.

Portfolio allocations can adjust in real-time according to changing market conditions, investor objectives, and risk tolerance levels. This brings automated flexibility, which helps optimize returns while minimizing risks.

AI solutions can also make optimal investments, which help minimize transaction costs that make an impact with reduced execution risk. In addition, AI solutions can analyze liquidity, order book dynamics, and trade execution algorithms with the help of AI.

8. Cost Saving

Cost saving comes from improving operational efficiency and improving profitability. By reducing manual labor in repetitive tasks and replacing it with automated processes, labor cost is reduced while accuracy and efficiency are improved.

Lowering the risk of fraud also leads to cost savings since financial losses due to fraudulent activities are minimized. Predictive analytics also help predict future demand, which allows banking institutions to adjust banking services accordingly. This leads to improved profitability, better competitive advantage, and improved sustainability.

15 AI in Banking Examples

1. JPMorgan Chase & Co. - Automated Loan Underwriting

JPMorgan Chase & Co. uses AI to streamline various business loan underwriting processes, such as analyzing borrower’s data, credit risk indicators, and financial metrics. 

This helps the company accelerate lending decisions and ensures quicker capital for businesses without compromising accuracy. Besides improving its operational efficiency, this financial institution also enhances the accuracy of credit risk assessment, which leads to more informed decisions.

Loan and credit decisions are obtained faster by the bank, helping it manage its lending portfolio more efficiently. This is exactly what Andy Mattingly, CIO at FORUM Credit Union confirmed to us during our podcast, where we discussed AI-driven lending.

2. Wells Fargo - Mortgage Underwriting

Wells Fargo is one of the banks that improved the mortgage underwriting process with the help of AI technologies. By introducing AI solutions, Wells Fargo fully automates the evaluation of borrower’s creditworthiness.

On top of that, Wells Fargo’s AI solution automatically verifies income and property appraisal information, which reduces the time required for mortgage approvals without increasing the risk of error. As a result, Wells Fargo has a much more efficient mortgage underwriting process, improving customer satisfaction and providing its clients with seamless access to homeownership.

At the same time, it releases the burden of collecting and going through all necessary information by its employees. The results keep snowballing, including faster processes, better accuracy, improved customer satisfaction, reduced risk and error, and more.

This gives Wells Fargo a competitive advantage, which leads to more business.

3. HSBC - KYC Compliance

HSBC implements AI solutions for Know Your Customer (KYC) compliance, as well as Anti-Money Laundering (AML). With the help of AI solutions, HSBC can combat financial crime and stay compliant by analyzing vast amounts of customer data, transaction history, and any external risk factors.

This makes customer identity verification much easier and more secure while easily detecting and blocking out suspicious activities in real-time. With such help, HSBC helps keep its reputation, gain a competitive advantage, and maintain the trust of its customers and regulators.

4. Bank of America - Fraud Detection

Bank of America uses AI solutions to focus on fraud detection and prevention. AI helps the company analyze vast amounts of transactional data, historical trends, and user behavior data, and, consequently, identify anomalies and potential instances of fraud in real time. Real-time detection helps mitigate financial losses, protect the customers’ assets, and maintain trust in its services and products.

With such AI incorporation into the banking sector, Bank of America gains a competitive advantage with its commitment to security, integrity, and customer protection. These benefits lead to reduced costs for potential fraudulent transactions but also help gain more clients by guarding people’s money against fraud in such an efficient way.

5. DBS Bank - Virtual Assistance

DBS Bank leverages natural language processing (NLP)  for customer-facing chatbots and virtual assistants. Machine learning and NLP help DBS Bank’s virtual assistants and chatbots understand customer queries and provide personalized responses in real time.

With seamless round-the-clock support for customers’ needs, DBS Bank frees up human resources, which gets a chance to focus on more complex needs and issues. The integrated AI solutions already yield better customer satisfaction, increased operational efficiency, and drive business growth.

6. Goldman Sachs - Risk Management

Goldman Sachs uses artificial intelligence to manage risk. AI helps the company analyze financial statements, credit scores, and business performance metrics in depth. Such information lowers the risk in underwriting decisions by providing more accurate assessments of credit risk and borrower eligibility.

Being able to make better-informed decisions, Goldman Sachs identifies creditworthy businesses, mitigates default risk, and manages lending portfolios efficiently. Financial stability and success are a few related benefits.

7. Santander - Cybersecurity Measures

AI solutions can help enhance cybersecurity and protect the banking industry from emerging threats. Santander is a great example as they used AI to improve cybersecurity.

With AI solutions, Santander analyzes network traffic, user behavior, and system logs in real time. With such analysis, artificial intelligence can detect and mitigate potential threats and security breaches, unauthorized attempts, and suspicious activity. Such a proactive approach helps Santander safeguard its system, data, and customer information and assets. At the same time, they remain compliant with regulatory compliance, improving the trust in its banking services.

That’s where a balance between innovation and compliance lies, and this is also something we discussed with Lex Sokolin from Generative Ventures on our Pioneers podcast.

8. Royal Bank of Canada - Enhanced Marketing and Growth

Royal Bank of Canada leverages AI solutions to analyze customer data, transaction history, and behavioral patterns to enhance its marketing. With such analysis of data, Royal Bank of Canada can identify distinct customer segments and tailor marketing campaigns and product offerings accordingly.

Combined with a personalized approach, relevant content for each one of the customer segments, and valuable insights, Royal Bank of Canada improves customer experience, satisfaction, and loyalty. With streamlined operations, RBC deepens customer relationships, increases marketing efforts, and satisfies customers, which helps drive growth and stay competitive among financial services companies.

9. Standard Chartered Bank - Financial Data Analysis

Standard Chartered Bank relies on AI solutions that help them analyze financial data such as creditworthiness, loan eligibility, credit history, income levels, and spending patterns to evaluate credit risk accurately.

With such financial data analysis, Standard Chartered Bank easily sets appropriate interest rates and loan terms much faster and more accurately. Overall, this leads to better lending decisions and minimizes the risk of default and credit losses. AI also helps SCB with credit scoring, improving access to credit, enhancing financial inclusion, and supporting economic growth and development in the communities it serves.

10. Barclays - Portfolio Management

Barclays leverages AI technologies to optimize its investment decisions to enhance portfolio management. AI solutions help analyze market data, news sentiment, and economic indicators to identify emerging trends, assess market risk, and predict investment opportunities with greater accuracy.

With such a data-driven approach, Barclays makes better decisions, optimizes portfolio allocation, and maximizes returns for its clients. Barclays also stays ahead of market trends, reduces risk, and delivers incredible returns for its investors.

11. ING Group - Customer Preferences

ING Group uniquely uses artificial intelligence solutions to analyze customer feedback and sentiments across various channels, to understand its customers better.

Machine learning helps analyze unstructured data from channels like social media, customer reviews, and surveys. This helps ING Group extract valuable insights into customer preferences, behaviors, and perceptions.

Based on this information, ING Group has a better understanding of customer needs and emerging trends to enhance its products and services accordingly. As a result, ING Group improves customer satisfaction, drives product innovation, and maintains a competitive advantage in the banking sector.

12. Vast Bank - Loan Underwriting

Vast Bank relies on AI to streamline loan underwriting processes which are aimed at small business loans and mortgages. With the help of AI solutions, Vast Bank analyzes borrower data, credit history, and financial statements at a much faster rate.

With such a streamlined process, Vast Bank’s loan and mortgage processes are much more efficient and faster. Accuracy is also improved, which allows this bank to provide accessible financing options faster with great risk management practices in place.

As Vast Bank’s CIO, Stephen Taylor mentioned in our podcast, breaking down complex workflows into discrete, well-defined tasks helps AI systems to focus on specific sub-tasks rather than attempting to handle the entire process in one go.

This approach helped Vast Bank efficiently manage loan underwriting.

13. CitiBank - KYC Verification

CitiBank utilizes AI to provide KYC solutions to enhance compliance processes and customer onboarding experiences. By streamlining the verification of customer identities, CitiBank has a way to assess the associated potential risks much more efficiently.

CitiBank benefits from the analysis of personal information, customer data, transaction history, and external risk factors by streamlining the process, which automatically verifies identities, detects suspicious activities, and ensures compliance with regulatory requirements.

14. Morgan Stanley - Investment Strategies

Morgan Stanley uses artificial intelligence for investment research and portfolio management. The AI’s analysis of market data, company fundamentals, and macroeconomic trends helps this company generate actionable insights to optimize investment decisions and strategies for its clients.

Harnessing information from various sources, understanding unstructured data, and extracting valuable insights streamlines the process, which would usually take much longer for people to do.

As a result, Morgan Stanley dynamically adjusts portfolio allocations based on changing market conditions, trends, client preferences, and risk tolerance levels. Morgan Stanley’s clients achieve their investing objectives much more efficiently while minimizing the risk and maximizing returns.

15. BBVA - Trading

BBVA, also known as the digital bank of the 21st century, relies on AI technology for trading systems that help execute trades at optimal prices to minimize market risks. Using AI solutions that analyze market data, order flow, and liquidity conditions in real-time, BBVA can make split-second trading decisions that bring less risk than traditional methods.

Artificial intelligence helps identify optimal trading opportunities to execute transactions at the most favorable prices, which ensures BBVA delivers superior value to its customers and desired investment performance.

What Is the Future of Generative AI in Banking?

The future of generative AI in banking looks transformative and promising. Generative AI in banking is already making the most significant impacts in the following areas:

• Customer service enhancement
• Risk management
• Fraud detection
• Automated financial advice
• Process automation
• Product development
• Regulatory compliance

AI solutions can automate routine tasks, reduce operational costs, and help companies innovate products and services that fit their customers much better. As artificial intelligence develops further, the banking industry will benefit even more from a variety of new ways to improve and automate processes.

At the same time, artificial intelligence will much more easily address challenges like data privacy, bias, and regulatory compliance.

Our founder, Ankur Patel, discussed how AI impacts banking and other industries, so we highly recommend checking out his 8 key answers on this matter.

What Are the Risks of Generative AI in Banking?

Some of the notable risks of AI use in banking include data privacy concerns, bias in the AI algorithm, operational risks, and regulatory compliance.

With AI solutions needing access to sensitive data, such large-scale processing of sensitive information heightens the risk of data breaches. There might also be bias in the algorithm, leading to unfair practices in loan approvals and risk assessments.

Operational risks like system failures can compromise financial decisions. At the same time, there’s a potential risk that AI solutions might not always be aligned with regulatory compliance in banking.

Luckily, AI solutions adhere to high standards that foster a safe financial environment, ensuring compliance and reduction of potential risks.

Improve Your Banking Processes With Us

Would you like to experience these benefits of AI solutions in your company - please schedule a 30-minute call with our experts. We can discuss your needs, and best use cases, and show you how our AI solutions work live.

FAQs

Is the AI future of banking?

Yes, AI is the future of banking, with current applications helping some of the largest companies gain a competitive edge.

Can AI help mobile banking?

Yes, AI helps mobile banking by personalizing customer service, automating transactions, and improving the verification and security of those transactions.

What are the benefits of AI chatbots in banking?

The biggest chatbot’s benefits in banking include 24/7 availability, cost savings, instant personalized responses, and the ability to handle high-volume requests.