Why Banks Must Become AI-First in 2024? 

Generative AI is transforming multiple industries, and the global banking sector is no exception. A McKinsey study estimates that AI has the potential to add an annual value of up to $340 billion to the global banking sector. The biggest role AI plays in the growth of the banking sector is by boosting productivity. 

However, implementing GenAI in banking operations is not all roses. You must be careful about protecting intellectual property and ensuring that your customers' sensitive financial data is not at risk. 

But with carefully planned AI strategies and preventive measures, GenAI execution in banking brings exceptional results. This article will discuss the benefits, challenges, and use cases of applying GenAI technology in banking. 

Why should Banks Consider Becoming AI-first? 

Banks' use of AI and machine learning (ML) is not new. A 2022 study highlighted that banks, financial services, and insurance companies were among the biggest users of ML technology. 

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Different banks use GenAI technology in different cases. For example, Bank of America uses an AI platform called Glass that helps their sales teams analyze hidden patterns in user behavior to sell their products to broader audiences. 

However, there are plenty of other reasons banks should consider becoming GenAI-first. These include: 

• GenAI helps global banks with multiple branches worldwide centralize all their customer profiles in the same place. By centralizing customer databases in real time, banks can uncover trends in users’ activities and preferences. This will mean personalizing products to greater depth and retaining customers for long

• AI tools can free up support agents’ time by automating more than half of their administration activities. This would mean automating user onboarding, accessing knowledge bases to serve users better, and faster documentation of user concerns 

• AI enables technologies like real-time sensing to detect frauds well in advance. This helps banks identify anomalies and unauthorized attempts before their occurrence and take required actions to stop them 

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How are Banks Adopting Generative AI Technology? 

Different banks have different perspectives towards generative AI technology. Which also means that they have their specific use cases to leverage AI. 

Here are a few examples of how some of the popular global banks introduced AI technology to their operations: 

• J.P.Morgan and Barclays used AI-ML technology to detect and prevent frauds related to cards and other form of mechant payment transactions 

• Goldman Sachs has used ML technology in its application called AppBank to automate corporate banking operations 

• HSBC Bank uses automated AI assistants to help new users with product discovery 

• Banco Santander developed a GenAI tool called Kairos to predict the impact of corporate events on a client’s portfolio. This tool helps bankers make better lending and portfolio management decisions for their customers 

• Discover, a financial services brand, uses AI for credit underwriting. It partners with third-party software to reduce default rates in lending 

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Sources: 1, 2 and bank websites & news articles 

What are the Challenges of Using Gen AI in Banking? 

AI might be an emerging technology in banking, but executing it right is no easy task. Without a roadmap in place, implementing GenAI in banking might become critical. 

Here are some of the common challenges that you may face in this process: 

Your AI technology is biased 

A biased AI technology in banking could have disastrous impacts. Imagine AI making biased predictions on a customer’s asset portfolio or discriminating against a customer based on certain factors to manipulate a bank’s credit decisions. 

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Solution: When opting for AI technology, banks should strictly avoid selecting new or unknown tools that don’t provide any information on how their models are trained. There should be a specific method to identify biases. Banks should also build a team of AI engineers who would be responsible for monitoring and maintaining the AI models, updating them regularly, and performing routine mathematical de-biasing to adjust relevant features. 

You don’t have a security framework to protect data 

Banks have a massive database of confidential user data. Therefore, when training an AI model, there are possibilities for using sensitive, copyrighted data. However, that is a critical breach of customers' rights. Once revealed, this could build a permanent negative brand image for your brand, along with penalties. 

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Solution: Select a reliable technology partner or third-party AI vendor. Ensure that customer data is collected through their consent. Maintain a strict system to perform your due diligence, ensuring compliance with privacy regulations. 

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Employees are not ready to accept AI technology 

Since AI literacy is still inadequate worldwide, it is common for employees to protest AI implementation. The fear of being replaced by AI often forces your workforce to fight against this change. When employees don’t cooperate, it leads to delays in AI implementation.

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Solution: Communicating the scope of AI and its benefits with employees can make a huge difference. Arranging AI training and explaining how AI can prove to be an extra arm for banking professionals can be effective in addressing such issues. 

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What are the Use Cases of Gen AI in Banks? 

Alltius, a GenAI assistant, helped a leading Asian bank reduce the time to product information by 95%. The bank had 3000+ product collateral pages and wanted to set up an AI assistant that helps potential customers discover ideal products by answering questions like, "Which credit card is most suitable for someone with a $10,000 monthly income who spends on dining and travel?” 

Alltius’ AI chatbot trained on all product collaterals of the bank reduced product discovery timings from 15 minutes to less than a minute. Read the full case study here.

However, this is just one example of using GenAI in banking. There are more such instances, and here we are listing a few: 

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Marketing 

• Targeting and personalizing marketing campaigns by deep diving into past purchase patterns and online activities of the users 
• Creating real-time 360-degree profiles for each customer to tailor their service needs 
• Integrating chatbots and virtual assistants to build a real-time repository of customer queries, which can later be reused into different marketing initiatives such as ad campaigns and content marketing 
• Simplifying product discovery for potential customers by training the AI chatbot with product information (above example) to increase conversion potential for different services

Sales 

• Creating automated lead qualifiers to predict whether a prospect is a perfect fit for a particular product before involving sales reps
• Scoring each lead based on their purchase intents to help sales reps prioritize leads
• Using predictive analytics to create personalized upselling and cross-selling offers for customers, along with specific product recommendations 

Customer support and experience 

• Creating automated, user-facing assistants that automate repetitive queries without involving human agents in the loop. Tools like Alltius provide over 90% accuracy rate to customer queries and accept not knowing an answer instead of manipulating users with incorrect responses 

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• AI tools like Alltius also help banks create real-time knowledge bases for human agents. It helped Assurance IQ, an insurance company, build a knowledge base to reduce agent ramp-up time from six months to two months. By automating 6000+ insurance plans, Alltius helped agents lift conversations by providing more specific answers and increasing the sell conversation rate from 5% to 15%. Read the full case study here

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Conclusion 

With the insane prospects that AI brings to the table, it would be a mistake not to implement it in the banking sector. Regardless of your use cases, AI has something to offer all business functions, be they marketing, sales, or customer support. 

However, banks must responsibly use AI technology to protect customers’ financial records. If you are relying on third-party technology, it should be as trustworthy as Alltius. 

Alltius has empowered leading players in the banking and financial space, including Assurance IQ, Prudential, AngelOne, and KreditBee. 

To learn how it works: 

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