{"id":6683,"date":"2024-05-07T00:44:24","date_gmt":"2024-05-07T00:44:24","guid":{"rendered":"https:\/\/www.salesforce.com\/?p=6683"},"modified":"2024-07-11T10:22:42","modified_gmt":"2024-07-11T10:22:42","slug":"ai-in-banking","status":"publish","type":"post","link":"https:\/\/www.salesforce.com\/ap\/blog\/ai-in-banking\/","title":{"rendered":"AI in Banking: How to Reduce Costs and Improve Service"},"content":{"rendered":"\n

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 centre agents\u2019 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\u2019s ability to produce natural language content, images, and coding. McKinsey estimates that banks could add $1 trillion<\/a> in value annually through strategic use of AI. <\/p>\n\n\n\n

To take full advantage of AI\u2019s now-and-future potential, banks must take steps to clean up their data, analyse their existing systems, and identify process challenges that could benefit from the technology. Here are four ways we expect forward-thinking banks will use AI to improve both the employee and customer experience.<\/p>\n\n\n\n

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The future of banking with trusted generative AI<\/strong><\/h2>\n\t\t\t\t\t\t\t

Discover how AI is becoming a core component of financial services strategy with this generative AI in banking playbook.<\/p>\n\t\t\t\n\t\t\t\n\t\t\t\t\t\t\t

\n\t\t\t\t\tSee what\u2019s possible<\/a>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\n\t\t
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Data: Safeguard privacy, security, and trust with financial AI<\/h2>\n\n\n\n

Nearly nine in 10<\/a> 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\u2019s fundamental to building trust with customers. Yet nearly half of executives say they believe AI introduces security risks<\/a>, while 59% of consumers say they don\u2019t believe AI is secure<\/a>. Banking regulators are concerned as well, especially when it comes to generative AI, which relies on large language models<\/a> (LLM) to generate responses.<\/p>\n\n\n\n

\u201cGetting your data in order is fundamental,\u201d says Amir Madjlessi, Managing Director and Banking Industry Advisor at Salesforce. \u201cYou 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\u2019t be able to extract relevant and accurate insights from your systems.\u201d<\/p>\n\n\n\n

Once you\u2019ve 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. <\/p>\n\n\n\n

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 \u2014 we don\u2019t share client data with external LLMs.<\/p>\n\n\n\n

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Sales Reps gain 5 hours a day with AI<\/strong><\/h2>\n\t\t\t\t\t\t\t

Learn how Crexi\u2019s sales team focuses 80% of its time on customers when AI takes on admin tasks.<\/p>\n\t\t\t\n\t\t\t\n\t\t\t\t\t\t\t

\n\t\t\t\t\tRead now<\/a>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\n\t\t
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Sales: Discover opportunities faster<\/h2>\n\n\n\n

AI can act like a personal assistant, helping relationship managers improve their lead and opportunity scoring across all kinds of services and products \u2014 from checking bundles to secured loans. AI improves forecasting by predicting likely performance outcomes for different business lines, whether investment, commercial, or retail banking. <\/p>\n\n\n\n

In a single dashboard, predictive AI can surface relevant insights to deepen existing relationships or capture new clients for the bank. Generative AI<\/a> 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. <\/p>\n\n\n\n

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.<\/p>\n\n\n\n

Marketing: Scale next-level personalization<\/h2>\n\n\n\n

Creating marketing segments and subsegments used to take weeks, and results could be lacklustre and generic. Generative AI<\/a> is changing that, enabling marketers to create segments within the client database using natural language prompts \u2014 and the results are available in just seconds.<\/p>\n\n\n\n

These 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 Einstein Copilot<\/a> 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.<\/p>\n\n\n\n

One bank testing Einstein Copilot 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. <\/p>\n\n\n\n

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Improving the PenFed member experience<\/strong><\/h2>\n\t\t\t\t\t\t\t

Learn how PenFed agents have more time to help 2.9 million members achieve their goals with automation and chat.<\/p>\n\t\t\t\n\t\t\t\n\t\t\t\t\t\t\t

\n\t\t\t\t\tLearn how<\/a>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\n\t\t
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Service: Improve agent training and customer satisfaction<\/h2>\n\n\n\n

Turnover among contact centre agents 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.<\/p>\n\n\n\n

Generative AI can help surface the precise information contact agents 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 agents to make smart decisions, and that\u2019s important in cases that require judgment calls \u2014 like whether it\u2019s OK to reverse a charge for an unhappy customer.<\/p>\n\n\n\n

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.<\/p>\n\n\n

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AI gives agents what they need to make the right call for the client and for the organisation.<\/p>\nAmir Madjlessi<\/cite><\/blockquote>\nManaging Director and Banking Industry Advisor, Salesforce<\/span>\n