AI in Banking: Transforming the Future of Financial Services

The potential role of artificial intelligence (AI) in banking is massive. Predictive AI already supports many standard banking practices, such as chatbots managing routine inquiries or call center agents’ dashboards. As generative AI continues to evolve, we expect lots of time-saving opportunities around rote tasks that improve the customer experience due to AI’s ability to produce natural language content, images, and coding. McKinsey estimates that banks could add $1 trillion in value annually through strategic use of AI.

To take full advantage of AI’s now-and-future potential, banks must take steps to clean up their data, analyze their existing systems, and identify process challenges that financial services software can fix. We’ll explore four ways we expect forward-thinking banks will use AI to improve both the employee and customer experience, as well as use cases, challenges, and future predictions.

What you'll learn:

• AI in banking solutions
• AI in banking: Safeguard data, privacy, security, and trust
• Discover sales opportunities faster with AI
• Leverage AI in banking to scale next-level marketing personalization
• Improve service representative training and customer satisfaction with AI
• AI in banking examples
• Challenges of AI in banking
• The future of AI in banking

The integration of AI in banking offers innovative solutions that enhance efficiency, security, and customer satisfaction. As banks strive to stay competitive, AI is becoming an indispensable tool. From streamlining operations to providing personalized customer experiences, AI is transforming various aspects of banking. Here are some key applications of AI in the banking sector:

• Automated customer service and chatbots: Enhancing customer experience with AI, integrating chatbots into banking apps, offering personalized customer support.
• Risk assessment and fraud detection with AI: Identifying risky borrowers/applications, detecting fraud, managing cybersecurity threats.
• AI-powered investment and wealth management solutions: Analyzing market data to identify trends, making portfolio recommendations to clients.
• Loan and credit analysis: Adopting an AI-based loan and credit system, analyzing behavior and patterns of customers with limited credit history.
• Process automation: Increase operational efficiency and accuracy, automate time-consuming, repetitive tasks.
• Regulatory compliance: Using AI and machine learning to read new compliance requirements for financial institutions, improve decision-making process.

These applications highlight the versatility and potential of the use of AI in banking, driving the industry toward a more intelligent and customer-centric future.

Nearly nine in 10 analytics and IT leaders are making data management a high priority in their AI strategy. Banks are laser-focused on keeping their data secure: It’s fundamental to building trust with customers. Yet nearly half of executives say they believe AI introduces security risks, while 59% of consumers say they don’t believe AI is secure. Banking regulators are concerned as well, especially when it comes to generative AI, which relies on large language models (LLM) to generate responses.

“Getting your data in order is fundamental,” says Amir Madjlessi, Managing Director and Banking Industry Advisor at Salesforce. “You need to evaluate the quality and quantity of your data and, if necessary, upgrade data collection and management processes. Without those steps, your AI won’t be able to extract relevant and accurate insights from your systems.”

Once you’ve prepped your data, deploying AI in banking requires further unique data management, with varying access rights for different functions. For example, to follow fair lending practices, banks must hide demographic information like religion or country of origin from lending officers. But that same information must be available to regulators as evidence of fair lending.

Data management is even more complex when it comes to generative AI, which relies on LLMs to learn how to properly respond to prompts. Leveraging solutions that have built-in data integrity like ethical guardrails can help banks address data challenges and meet compliance rules. Salesforce, for example, has a zero data retention policy for LLMs — we don’t share client data with external LLMs.

AI can act like a personal assistant, helping relationship managers improve their lead and opportunity scoring across all kinds of services and products — from checking bundles to secured loans. AI improves forecasting by predicting likely performance outcomes for different business lines, whether investment, commercial, or retail banking.

In a single dashboard, predictive AI can surface relevant insights to deepen existing relationships or capture new clients for the bank. Generative AI can integrate data from third parties as well as internal sources to make suggestions in the flow of work, which increases the accuracy and relevance of those recommendations.

With the power of both predictive and generative AI, the relationship manager can understand the best channel to reach the client, with a relevant and compelling offer. These functions help reduce the time relationship managers require to fully understand customer needs across the bank while improving their experience.

Creating marketing segments and subsegments used to take weeks, and results could be lackluster and generic. Generative AI is changing that, enabling marketers to create segments within the client database using natural language prompts — and the results are available in just seconds.

These marketing personalization tools help marketers quickly build the most relevant offers or promotions, then test and learn from each, to further refine segmentation. For example, marketers using Agentforce can target customers with low savings coverage by creating an offer recommending products or services that improve financial security. The marketers can then use generative AI-powered, prebuilt email templates to share that offer with the targeted customer. Over time, the messaging gets refined as the AI engine learns how customers respond to the content. The net result: Offers become super-personalized and conversion rates improve.

One bank testing Agentforce has seen engagement jump three to four times. The reason? The messaging is rooted in real-time customer behavior and actions, making the recommendations connected and authentic.

Turnover among contact center representatives is an industry-wide problem. Continuously training and onboarding an endless queue of new employees is expensive and ineffective. Using AI to improve the training experience and the day-to-day workflow enables agents to onboard faster, which can contribute to better retention rates. It also makes the service experience more pleasant for the customer.

Generative AI can help surface the precise information service representatives need to quickly resolve issues, by populating content for known answers based on the actual language the customer uses to describe a problem. This empowers them to make smart decisions, and that’s important in cases that require judgment calls — like whether it’s OK to reverse a charge for an unhappy customer.

Plus, AI provides smarter tools for spotting fraud and verifying identity, which helps agents understand their next best actions. Salesforce, for example, now has an out-of-the-box, know-your-customer (KYC) protocol for identity verification and credit scoring.

"AI gives service representatives what they need to make the right call for the client and for the organization." – Amir Madjlessi, Managing Director and Banking Industry Advisor, Salesforce

Here are some real-world examples of how Salesforce customers use or plan to use AI:

PenFed Credit Union plans on adding Einstein to internal (and eventually customer-facing) processes with generative AI. Einstein will act as a virtual assistant, suggesting chat and email responses service representatives can use to answer questions faster and reduce queues. The assistant will propose responses to a chat or member question, beginning with PenFed's internal employee support line before it expands to its members.

Ponce Bank uses Einstein AI to personalize content in real time to deliver the right message at the right time. This allows them to create smarter, deeper relationships with customers and prospects, and also engage their audience of underbanked and underserved communities more efficiently.

The Corporate and Commercial Banking team at Santander is using AI to scale their Santander Navigator platform, which has attracted a significant number of new subscribers. AI is used to visualize international trade trends and user insights in real-time through CRM Analytics, and to generate personalized recommendations based on customer data. This innovative use of AI not only helps Santander expand the platform but also sets a new standard for the financial services industry, with plans to incorporate ESG ratings and sustainability features.

While the adoption of AI in banking offers numerous benefits, it also presents several challenges that financial institutions must navigate carefully. As AI becomes more integrated into banking operations, addressing these issues is crucial for maintaining trust, fairness, and compliance. Here are some of the key challenges associated with AI in banking:

• Regulatory considerations for AI adoption in financial services: The financial sector is heavily regulated, and the adoption of AI introduces new regulatory challenges. Banks must comply with existing regulations while also staying ahead of emerging guidelines specific to AI. This includes ensuring data privacy, managing cybersecurity risks, and adhering to ethical standards. Collaboration with regulatory bodies and continuous monitoring of AI systems are essential for navigating this complex landscape.
• Addressing potential biases and discrimination: AI systems can inadvertently perpetuate biases present in the data they're trained on. This can lead to biased outcomes in areas such as loan approvals and risk assessments. Banks must actively work to identify and mitigate these biases to make sure that AI-driven decisions are equitable and don't put certain groups at a disadvantage.

By addressing these challenges proactively, banks can use the full potential of AI while maintaining the integrity and trust that are fundamental to the banking industry.

The future of AI in banking is positioned to be transformative, with advancements that promise to reshape the industry in profound ways. As technology continues to evolve, banks are expected to leverage AI to deliver even more personalized and efficient services.

Here are some trends that are likely to define the future of AI in banking:

• Advanced personalization: AI will enable banks to offer hyper-personalized services tailored to individual customers' needs and preferences. By analyzing vast amounts of data, AI can provide customized financial advice, product recommendations, and real-time support, enhancing the overall customer experience.
• Enhanced security measures: With the increasing sophistication of cyber threats, AI will play a critical role in bolstering security. Advanced AI algorithms will be able to detect and respond to fraudulent activities in real-time, ensuring the protection of customers' assets and sensitive information.
• Automated compliance: As regulatory requirements become more complex, AI will help banks automate compliance processes. Machine learning models can continuously monitor transactions and flag potential violations, reducing the risk of non-compliance and streamlining regulatory reporting.
• Expansion into new services: AI will open up new avenues for banks to offer innovative services, such as AI-driven investment platforms, robo-advisors, and smart contracts. These services will not only attract new customers but also create additional revenue streams for banks.
• Ethical AI development: There will be a growing emphasis on ethical AI development, ensuring that AI systems are fair, transparent, and free from biases. Banks will invest in frameworks and guidelines to govern the responsible use of AI, fostering trust among customers and stakeholders.

As AI continues to integrate more deeply into banking operations, the industry will become more agile, customer-centric, and secure. The future of AI in banking isn't just about technological progress — it's about creating a more intelligent and inclusive financial ecosystem that benefits everyone.

AI in banking can be used in various ways to enhance efficiency and customer experience. Some key applications include automated customer service and AI chatbots for personalized support, risk assessment and fraud detection to identify potential threats, AI-powered investment and wealth management solutions for market analysis and portfolio recommendations, loan and credit analysis to evaluate customers with limited credit history, process automation to increase operational efficiency, and regulatory compliance to improve decision-making processes. Many banking CRMs (customer relationship management) use generative AI and agentic AI, particularly AI agents, to manage these capabilities.

The future of AI in banking is promising and transformative. Banks are expected to use AI for advanced personalization, offering hyperpersonalized services tailored to individual customers' needs. Enhanced security measures will be implemented to detect and respond to fraudulent activities in real-time. Automated compliance processes will simplify regulatory reporting and reduce the risk of non-compliance. Additionally, banks will expand into new services such as AI-driven investment platforms and robo-advisors, while emphasizing ethical AI development to ensure fairness and transparency.

AI is disrupting the banking industry by revolutionizing traditional processes and improving customer experiences. Automated customer service and chatbots are providing 24/7 support, while AI-powered risk assessment and fraud detection systems are improving security. AI is also changing investment and wealth management by analyzing market data and offering personalized recommendations. Loan and credit analysis is becoming more accurate with AI, and process automation is increasing operational efficiency. Furthermore, AI is helping banks navigate regulatory compliance more effectively.

While AI offers numerous benefits, there are also some disadvantages to consider. Ensuring fairness and transparency in AI algorithms is a significant challenge, as banks must provide clear explanations for AI-driven decisions. Addressing potential biases and discrimination is crucial to prevent unfair outcomes in areas like loan approvals. Additionally, regulatory considerations for AI adoption in finance require banks to comply with existing regulations and stay ahead of emerging guidelines, which can be complex and time-consuming.

Banks are using generative AI to create innovative solutions and enhance customer experiences. Generative AI in banking can be employed to develop personalized financial reports, generate realistic training data for fraud detection models, and create virtual assistants that provide tailored financial advice. Additionally, generative AI can help in scenario planning and risk management by simulating various market conditions and predicting potential outcomes, enabling banks to make more informed decisions.