Picture waking up to your virtual assistant banker delivering a message that sends a chill down your spine: "Today, there is a significant risk to your investment portfolio, with the potential for a sharp 5.58% downside. This is due to the recent crash in global markets, which has been attributed to the effects of Silicon Valley Bank."

The truth is, the future is not far off when we can expect such personalized alerts thanks to the recent advancements in language models like ChatGPT and BERT.

The rise of AI and conversational technology, though revolutionary, is racing ahead so quickly that regulators are struggling to keep up. Startups and fintech companies are seizing upon the abundant use cases in banking, disrupting the traditional industry as we know it. The impact of AI in finance is profound, from personalized financial advice to automated trading algorithms.
According to a report by McKinsey & Company, AI has the potential to create up to $1 trillion in annual value for the banking industry by 2030. The report also suggests that banks can use AI to improve customer service, enhance risk management, and reduce costs.

The use of ChatGPT in Conversational Banking has several advantages.

Instant Response: Eliminates the need to wait in queues or speak to a customer service representative. This can lead to improved customer satisfaction and loyalty.

Integration across communication Channels: Customers can interact with their bank using their preferred channel like social media, messaging apps, voice assistance, or websites; making banking more accessible and convenient.

Scalable: It can handle a large volume of queries simultaneously, making it more cost-effective than hiring additional resources.

Short term use cases:

1. Customer Service: Handle customer queries and complaints, providing 24/7 service and reducing wait times.
2. Training and Education: Help customers to understand various details of financial products and staff about various scenarios.
3. Fraud Detection: Using AI algorithms to detect fraudulent activities and alert customers and banks in real-time.
4. Loan Processing: Help customers with loan applications and eligibility checks, reducing the time and effort required for manual processing by answering all queries.

Long term use cases:

1. Personalized Financial Planning: Provide customers with personalized financial advice and guidance based on their spending patterns, income, and goals.
2. Risk Management: Analyze and predict risks, such as credit risks and market risks, and take proactive measures to mitigate them.
3. Investment Advisory: Provide investment advice and suggest investment options based on customer preferences and risk appetite.

Success Indicators:

1. Erica by Bank of America:

Erica has over 25 million users, and the bank reported a 45% increase in mobile banking transactions in 2020 due to the adoption of Erica.

2. Olivia by Capital One:

Olivia has over 6 million users and has processed over 70 million customer interactions since its launch in 2018. Capital One reported a 13% increase in credit card usage among Olivia users compared to non-users.

3. Eno by Capital One:

Eno has over 12 million users and has helped prevent over $1 billion in potential fraud since its launch in 2017. Capital One reported a 30% increase in mobile app usage among Eno users compared to non-users.

4. Nina by US Bank:

US Bank reported that Nina has saved the bank 165,000 hours of customer service time since its launch in 2018. Nina has a 90% accuracy rate in understanding customer inquiries and providing relevant responses.

5. Amelia by SEB Bank:

Amelia has handled over 600,000 customer interactions since its launch in 2016. SEB Bank reported a 78% decrease in time spent on manual tasks by customer service agents thanks to Amelia.

6. Ask Arvi by HDFC Bank:

Ask Arvi has over 6 million users and has processed over 200 million customer interactions since its launch in 2017. HDFC Bank reported a 20% increase in customer engagement among Ask Arvi users compared to non-users.