- “Banks and financial services institutions face increased competition not only from peer organizations within the industry, but also now from FinTech startups, Neobanks, and others. The way to compete is to deliver highly personalized services and innovative offerings. And increasingly, the way to do that is using AI/ML to derive data-driven insights upon which those services and offerings can be based.
- The potential for industry disruption is enormous. Open banking enables the exposure of customer financial data via APIs, extending an organization’s reach far beyond traditional financial services institutions. The open banking market is expected to reach $43.15 billion by 2026, growing at a compound annual growth rate (CAGR) of 24.4% through 2026, according to Allied Market Research.”
- “Due to increasing implementation of AI-driven applications in the banks, including customer relationship management (CRM), data analytics & visualization, and chatbot to enhance customer experience and back-office activities, the software segment is projected to register a significant revenue CAGR of 43.1% during the forecast period.
- In terms of market share, the deep learning & machine learning segment is expected to lead among the other technology segments in the global AI in the banking market during the forecast period due to growing adoption of deep learning & machine learning approach for risk assessment in banks.
- Increasing need to optimize customer engagement by introducing AI-driven virtual assistance and provide 24/7 customer services and answer customer queries and grievances is expected to contribute to revenue growth of the customer service segment in the global AI in the banking market during the forecast period.
- Due to growing need to offer improved customer service in the banking industry, the Chatbot segment is projected to lead in terms of revenue share in the global AI in the banking market during the forecast period.
- Factors such as growing emphasis of banks in countries in North America on enhancing banking operations with the use of advanced technologies are resulting in the market in the region accounting for comparatively larger revenue share than other regional markets.”
- “One of the fastest areas of adoption for AI in the enterprise is chatbot applications. It’s often a good place for companies to get started with AI and see quick results. By 2024, Insider Intelligence predicts that consumer retail spend via chatbots worldwide will reach $142 billion– up from just $2.8 billion in 2019.
- TechStyle, an online retailer, implemented AI to stand apart from the competition. With 5 million members, 6 million phone calls per year and 3 million chats per year, communication is core to its business. By integrating AI, TechStyle saved $1.1 million in the first year in operations costs and achieved a score of 92 percent in its member satisfaction survey.
- The volume of customer interactions agents handle has increased by nearly 20 percent on average and spiked 35-40 percent in some cases during the pandemic, according to a poll among Genesys Customer Advisory Board members. This puts tremendous pressure on agents and technology on the front lines of these interactions.”
“Back in 2019, Porter and Thomas decided that the primary focus of the bank’s analytics and AI activity should be customers — hence the “customer insights, data, and analytics” label. Thomas, Lee, and their colleagues felt that improving processes and making better decisions within the bank was the best way to catch up to and surpass competitors.
Thomas felt that, given the bank’s relatively late start, a results-oriented approach to AI was necessary. That’s why there are no “big bang” projects, and there is little pure experimentation or research. Instead, the bank’s key use cases focus on continuous improvement of its operations and customer relationships. Lee told us that as a result, most AI projects are deployed into production, with about 80% of Scotiabank’s analytics and AI models already in place and the other 20% pending.”
“In August 2020, This is Money found that – in some cases – it took three hours for customers to get through to a human when contacting their bank. Of course, this was in the middle of a global pandemic, so you can excuse a certain amount of disruption to services, but this is where conversational AI is a boon.
How can banks use it to improve customer service?
- Conversational AI can lead to faster resolution times
- Using conversational AI can result in a better resolution for the customer
- Conversational AI can help generate personalised recommendations”