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 enquiries or call centre agents’ dashboards. As generative AI continues to evolve, there will be 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 globally, banks could add $1 trillion in value annually through the strategic use of AI.

To take full advantage of AI’s now-and-future potential, banks must take steps to clean up their data, analyse their existing systems, and identify challenges in processes that financial services software can fix. Let's explore four ways forward-thinking banks can use to improve the employee and customer experience, use cases, challenges and future predictions with AI.

• What is the role of AI in banking solutions?
• How banks can safeguard data, privacy, security, and trust with AI
• AI in banking sector in India
• Discover sales opportunities faster with AI
• Scale next-level marketing personalisation with AI
• Examples of AI in banking
• Key considerations and challenges in implementing AI in banking
• The future of AI in banking
• Frequently Asked Questions (FAQs)
  • How can AI be used in banking?
  • What is the future of AI in banking?
  • How is AI disrupting the banking industry?
  • What are the disadvantages of AI in banking?
  • How are banks using generative AI?

The integration of AI offers innovative solutions that enhance efficiency, security, and customer satisfaction. As banks strive to stay competitive, AI is becoming an indispensable tool, transforming various aspects of banking. Here are some of its key applications:

• Automated customer service and chatbots: Enhancing customer experience with AI, integrating chatbots into banking apps, offering personalised 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: Analysing market data to identify trends, and making portfolio recommendations to clients.
• Loan and credit analysis: Adopting an AI-based loan and credit system, analysing behaviour 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 the decision-making process.

These applications highlight the versatility and potential of the role 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 the executives say they believe AI introduces security risks, while 64% of customers feel that companies are reckless with their data, and 61% believe that it is more important than ever, with AI advancements, to protect their data.

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 your banking operations 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.

Indian banks are also rapidly adopting AI to modernise operations, enhance customer service, and protect against fraud. The Reserve Bank of India’s MuleHunter AI and Indian Bank’s WAVE program showcase how AI is driving change across both front-end experiences and back-end risk management.

The Reserve Bank Innovation Hub unveiled MuleHunter.AI in late 2024. This is a machine learning-powered system designed to detect “mule accounts” used in money laundering and digital payment fraud. MuleHunter analyses behavioral patterns across transaction data and account networks, identifying suspicious activity far more accurately than rule-based systems. By proactively flagging high-risk accounts, it helps banks act quickly, prevent misuse, and build trust in the digital payments ecosystem.

In 2023–24, Indian Bank launched a major digital transformation initiative called WAVE (World of Advanced Virtual Experience), built on AI and generative AI. Designed for retail, agricultural, and MSME customers, WAVE introduced tools like the I-Help chatbot, voice-based virtual assistants, and generative AI engines to offer personalised support, faster query resolution, and more intuitive banking journeys. The results were dramatic - its digital business grew by 14x in just one year from Rs 5,600 crore to over Rs 81,200 crore, underscoring the tangible impact of AI-driven customer engagement.

These initiatives reflect the broad utility of transforming how customers interact with banks to reinforce the guardrails that keep financial systems safe. As AI models grow more intelligent and accessible, their role in driving secure, scalable, and inclusive banking in India is only set to grow.

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. Sales 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 required 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 lacklustre and generic. Marketing 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 personalisation tools help marketers build the most relevant offers or promotions quickly, and 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-personalised 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 behaviour and actions, making the recommendations connected and authentic.

With a mission and vision is to enrich the lives of low- and middle-income individuals in unserved and underserved markets, Aavas Financiers digitised loan origination with Salesforce. With automated KYC, AI insights, and on-the-go documentation, its decision time was halved, and the company sanctioned ₹50 billion in loans faster, smarter, and at scale.

Hero FinCorp, one of India’s fastest-growing lenders, transformed its complex two-wheeler loan process with Salesforce Agentforce and Data Cloud. By automating over 100 manual touchpoints, Hero FinCorp cut loan turnaround time from two days to just 30 minutes, even during peak festival seasons. Its MuleSoft integrations ensure real-time data checks with government IDs and financial systems, while Service Cloud and Sales Cloud help deliver seamless customer experiences. With AI-powered agents handling routine tasks, Hero FinCorp’s teams can focus on building relationships, boosting dealer loyalty, and empowering more first-time buyers to achieve their dreams, faster and smarter.

Improve service representative training and customer satisfaction with AI

While turnover among contact centre representatives is common, continuously training and onboarding new employees is expensive and ineffective. Using Service 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 to 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 judgement 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.

- Amir Madjlessi, Managing Director and Banking Industry Advisor, Salesforce

Kotak Mahindra Bank transformed its customer experience by consolidating 12 different systems into a single Salesforce-powered platform for engagement and service. Leads across business lines and products were made visible in one place, ensuring no opportunity was missed. It streamlined and automated loan origination, enabling faster credit decisioning and disbursals. Customers started to benefit from self-service through a home loan portal, while partners could onboard customers directly using a dedicated partner portal.

“With a connected platform for customer relationship management across the bank, we’re able to deliver seamless and consistent experiences at scale,”

- Milind Nagnur, Group President and Chief Technology Officer

A 360-degree view of each customer across channels empowers its service agents to deliver fast, personalised support, no matter how the customer reaches out. With Salesforce Professional Services as a strategic partner, Kotak accelerated innovation and time-to-value.

Similarly, IDFC Asset Management Company Limited (AMC) transformed its customer engagement with intelligent, contextual marketing powered by Salesforce. A sophisticated campaign management solution enabled the team to design, automate, and launch large-scale email and SMS campaigns tailored to diverse customer segments.

By orchestrating personalised journeys based on preferences and behaviour, the brand delivered relevant content that inspired action and loyalty. Built-in analytics provided deep insights into campaign performance, helping the marketing team fine-tune messaging continuously, identify top-performing channels, and optimise spend.

With a complete view of campaign data, it could pivot quickly, double down on what worked, and reduce wasted efforts. Social Studio empowered the team to plan, publish, and monitor campaigns across social platforms, bringing a unified voice across channels.

The impact was compelling: a 2.5x increase in gross sales and an impressively low unsubscribe rate of just 0.06%. By aligning content with customer intent, IDFC AMC built stronger connections, improved retention, and unlocked measurable growth across its customer base.

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

AI 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 personalised and efficient services.

Here are some trends in AI that banks should watch for:

• Advanced personalisation: AI will enable banks to offer hyper-personalised services tailored to individual customers' needs and preferences. By analysing vast amounts of data, AI can provide customised 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.

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