Leveraging generative AI can provide the banking sector an additional value of $200 billion to $340 billion annually as per
McKinsey. The banking industry, characterized as an early adopter to technology, is in fact pinpointed to be one of the industries that can benefit the most with generative AI. However, the introduction of this new technology poses both competitive advantages and challenges.
Today, understanding the value and risk generative AI can bring forward, several banks are experimenting with this technology and many are reaping the benefits of integrating generative AI into their tech stack. Interestingly, WIZ.AI’s whitepaper, Increasing Bank Competitiveness with Generative AI, takes an in-depth dive into these elements while presenting the significance of increasing banking competitiveness.
About the Whitepaper: The whitepaper,
Increasing Bank Competitiveness with Generative AI, offers insights into the challenges faced by banks in adopting new technology, industry implications of generative AI’s rapid development, and how WIZ.AI’s solutions can help enterprises optimize their operations. The whitepaper further analyses how banks can enhance their competitiveness with generative AI.
This blog will walk you through the challenges of adopting technology in the banking sector, the benefits of generative AI in banking, and the future of generative artificial intelligence in banking.
Challenges in Adopting Technology in the Banking Sector
The banking sector, akin to others, is now undergoing a rapid digital transformation with an increased adoption of technology. This is in the form of the adoption of Artificial Intelligence in banking, big data, cloud computing, mobile banking, and so on. Among them, generative artificial intelligence continues to evolve the fastest. This modernization is happening in customer and employee services, marketing, sales, HR, and other divisions in banking. These digital advancements are highly beneficial in obtaining better business outcomes. However, it can be challenging for traditional banks to move forward at the same pace as their peers.
As per WIZ.AI’s whitepaper,
Increasing Bank Competitiveness with Generative AI, several forces contribute to and result in technological developments in traditional banks, such as the rise of FinTech, evolving customer expectations, and bloated cost-to-serve ratio. Similarly, the whitepaper highlights operational challenges like outdated tech systems, service inefficiencies, customer leakage, and others that banks need to address to enhance technological adoption. This can improve their competitiveness and also maintain their service relevance.
Although transformative changes are imperative in every industry, banks need to put security at the forefront of tech adoption. Institutions need to take measures such as gradual replacement of legacy systems,
adopting integration, and ensuring compliance with strong protocols and standards such as data encryption and multifactor authentication.
The Benefits of Leveraging Generative AI in Banking
In a recent
EY-Parthenon study, the respondents revealed that enhancing productivity through automated sales activities, improving existing technological capabilities, and accelerating broader innovation are three key benefits of leveraging generative AI in banking. This is just the tip of the iceberg. There are several other advantages of using generative AI in banking. Based on a study from
McKinsey Global Institute, banking, especially in the corporate and retail sectors, is identified as one sector that will have the potential to attain 9% to 15% higher operating profits.
Furthermore, generative AI is a favorite in the banking sector, especially for providing personalized customer service via chatbots, voice assistants, and voicebots. This benefits banks in improving customer engagement and satisfaction. A research study by
Emplifi identified that 86% of consumers would leave a brand if they had two to three bad customer service experiences. This is when leveraging generative AI for banking becomes vital as these solutions will go the extra mile in improving customer experience.
As per WIZ.AI’s whitepaper,
Increasing Bank Competitiveness with Generative AI, using generative AI to improve customer and employee experience can boost customer growth through different strategies like lead filtration, aid with debt collection, scale up customer support, streamline
operational management, and more. The paper also throws light on different generative AI applications like inbound voicebot, outbound voicebot, post-call messaging, call transcription, and call analysis.
These applications can handle various factors associated with customer engagement, including lead generation and analytics. This aids banks in handling labor shortages, reducing manual errors, streamlining workflows, and allowing employees to focus on more critical tasks.
The Future of Generative Artificial Intelligence in Banking
Indeed, the future of generative AI in banking looks promising. Currently, banks are exploring the different options put forward by generative AI in transforming operations. It is not just limited to customer service, but; it is widely used in other use cases like customer retention, debt collection, fraud detection, and so on.
Since generative AI is an evolving technology, there are some limitations and risks associated with it. However, understanding that AI is here to stay, countries are now creating
regulatory frameworks to ensure the safe and secure usage of AI technologies like generative AI. Singapore and the European Union recently came up with their AI regulatory frameworks. With these frameworks and future developments, generative AI adoption will be on the rise in every sector, especially banking and the financial industries.
When we consider the future of generative AI, banks will invest more in generative AI-powered solutions for specific use cases. The focus will be on enhancing the customer and employee experience. Currently, implementing these solutions takes a lot of time. However, in the future, the time taken would be reduced to a few weeks or even days.While looking into different use cases, generative AI could be beneficial in providing personalized financial advice on a much deeper level by offering tailored investment strategies and budgeting plans based on financial history, goals, and other data metrics. It could even generate error-free and detailed financial reports, and take fraud detection and prevention to the next level by accurately analyzing datasets to identify anomalies and suspicious behaviors.
Generative AI could perform predictive analysis by monitoring market trends, customer behavior, and economic conditions. Further to that, it could also enhance regulatory compliance. In the current scenario, generative AI can cater to some of these use cases. However, there is a need for fine-tuning, achieving better interpretability, explainability, transparency, and accuracy. The advancement in generative AI as a technology would, thus, open up new avenues of opportunities and benefits for banks, facilitating them with a competitive advantage.
