AI in Banking: Transforming the Future of Financial Services

The potential role of artificial intelligence (AI) in banking is massive, especially as banks adopt modern banking software and accelerate digital transformation in banking. Predictive AI already supports many standard banking practices, such as chatbots managing routine inquiries or call center agents’ dashboards, often powered by intelligent agents that analyze customer data and automate responses. 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.

• AI in banking is transforming financial services by improving fraud detection, customer service, and operational efficiency.
• Banks are using AI to improve credit risk analysis, regulatory compliance, and investment decision-making.
• Strong data governance and ethical AI frameworks are essential for maintaining trust and regulatory compliance.
• Agentic AI is emerging as the next frontier in banking, enabling autonomous agents to handle complex, multi-step workflows without constant human intervention.
• Platforms like Agentforce for Financial Services help banks operationalize AI with built-in automation, insights, and security.

AI in banking refers to the use of artificial intelligence technologies such as machine learning, predictive analytics, and generative AI to automate processes, analyze financial data, detect fraud, personalize customer experiences, and improve operational efficiency within financial institutions.

Banks use AI systems to process large volumes of data, identify patterns, and support decision-making across areas such as lending, compliance, customer service, and investment management.

Agentic AI represents a shift toward autonomy. Unlike standard predictive systems, it refers to AI designed to act proactively toward specific goals. It can independently plan, reason, and execute multistep tasks such as updating customer profiles or initiating compliance reviews, with minimal human oversight.

The integration of AI in banking offers innovative solutions that enhance efficiency, security, and customer satisfaction across modern financial services cloud platforms. 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

AI chatbots and AI assistants help banks deliver 24/7 customer support. These tools answer routine questions, assist with transactions, and help agents resolve issues faster.

• Fraud detection and risk management

Fraud detection using AI in banking analyzes transaction patterns to identify suspicious activity in real time, helping financial institutions prevent fraud and protect customer accounts.

• AI-powered investment and wealth management

AI tools analyze market data, economic indicators, and portfolio performance to help advisors and clients make smarter investment decisions.

• AI for credit scoring and lending

AI models evaluate borrower behavior, alternative data, and financial history to assess creditworthiness and improve lending decisions.

• Operational automation

Agentic AI transforms operational automation by moving from simple task execution to autonomous workflow management. Rather than just identifying a compliance gap, an agentic system can automatically trigger the corrective workflow, document the action, and notify the relevant compliance officer.

• Process automation

Increase operational efficiency and accuracy by automating time-consuming tasks with finance automation solutions.

• 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. Fifty-nine percent of consumers say they don’t believe AI is secure, so it’s essential for leaders to understand the risks and take a secure approach to AI adoption so that their organizations can build and retain customer trust. 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.

Moving beyond reactive selling is essential to deepening client relationships. Rather than relying on fragmented data, banks can now leverage agentic AI as a force multiplier for their bankers and marketing teams. By autonomously synthesizing account data and real-time transaction signals, these agents do more than just surface high-potential leads; they proactively draft personalized outreach, schedule follow-ups, and manage the referral pipeline. This allows bankers to minimize administrative burdens and focus on the personal, trusted engagement that truly drives growth and wallet share.

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, improving customer engagement in agentic banking, 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, often using autonomous agents to guide next best actions, 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

The use of AI in banking delivers several major advantages for financial institutions and their customers:

• Improved fraud detection: AI models analyze transaction patterns in real time to identify suspicious behavior.
• Better customer experiences: AI assistants and chatbots provide instant support and personalized recommendations.
• Faster decision-making: AI can process large datasets quickly, improving credit scoring and investment analysis.
• Operational efficiency: Automation reduces manual tasks and streamlines internal workflows.
• Personalized financial services: AI enables banks to deliver targeted offers based on real customer behavior.

Here are some real-world examples of how Salesforce customers use or plan to use AI across retail banks, credit unions, and other financial institutions:

PenFed Credit Union plans on adding Agentforce to internal (and eventually customer-facing) processes with generative AI. Agentforce 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 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.
• The autonomy of agentic AI necessitates a robust "human-in-the-loop" governance framework: Since these agents can take independent action, banks must define clear guardrails to ensure that critical decisions—especially those involving capital movement or significant client communication—remain under human supervision.

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 as financial institutions continue advancing AI in retail banking and broader digital innovation, 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. The industry is moving toward an "agentic enterprise," where AI agents act as the connective tissue across front, middle, and back-office functions, coordinating across different platforms to resolve complex client issues instantly.

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.

Banks looking to operationalize AI at scale need a secure platform that connects data, workflows, and automation. Agentforce for Financial Services helps institutions deploy AI agents, automate processes, and deliver personalized financial experiences while maintaining regulatory compliance.

Explore how Agentforce can help your organization build the next generation of AI in banking.