AI will fundamentally change the banking industry — and society — in a positive way. New roles will be created, such as generative AI security or generative AI customer service. AI can make it easier to help customers with long-term financial planning, decide when to pay off their mortgage, or how to grow their retirement savings.
The immediate imperative is to learn and keep up with the pace with which generative AI technology is moving. Find the places in your organization where generative AI will produce the most benefit.
For banks, acting now will offer the opportunity to start small, iterate and learn, and quickly benefit from the efficiencies generative AI drives. Start by identifying pain points in your customer and employee journey to understand where generative AI can produce the most benefit to your organization. By improving personalization and customer experiences, banks can future-proof and grow business over the long-term.
AI will fundamentally change the banking industry — and society — in a positive way. New roles will be created, such as generative AI security or generative AI customer service. AI can make it easier to help customers with long-term financial planning, decide when to pay off their mortgage, or how to grow their retirement savings.
The immediate imperative is to learn and keep up with the pace with which generative AI technology is moving. Find the places in your organization where generative AI will produce the most benefit.
For banks, acting now will offer the opportunity to start small, iterate and learn, and quickly benefit from the efficiencies generative AI drives. Start by identifying pain points in your customer and employee journey to understand where generative AI can produce the most benefit to your organization. By improving personalization and customer experiences, banks can future-proof and grow business over the long-term.
AI for Banking in 2024
Although the concept of AI has been around since the 1950s, banks didn’t begin exploring AI for basic tasks like data
management and customer service until the 1980s and 1990s. The explosion of big data and the refinement of machine
learning led to more sophisticated AI applications. With AI, we are now seeing AI move from being an operational to a core component of banking strategy.
While generative AI also relies on historical data, its primary goal is to learn the underlying patterns that are not restricted to replicating past examples. With generative AI, you can potentially create new financial strategies, create diverse customer engagement materials, or even assist in product design.
Use Cases
With generative AI, both bank employees and customers are empowered with a powerful partner that drives better experiences all from natural language. Customers can receive hyper-personalized offers based on data they shared about their financial needs and goals, or resolve complex service issues through self-service. Bankers might leverage a virtual assistant to compose personalized emails or guide clients to customized products in real time, just to name a few benefits.
Service
AI can auto-generate service responses, write case summaries, and create knowledge articles to expand knowledge across the bank.
An example is automated customer service. Generative AI can quickly craft the exact response customers need by combining knowledge culled from multiple articles and sources. Using customer resolution data to analyze sentiment and patterns, service teams can accelerate chatbot training and expand automated, self-service capabilities.
Sales
AI can auto-generate client outreach, summarize interactions and next steps, and augment client and account research.
An example is customer insights. Generative AI can act like a personal data analyst assistant to uncover patterns and relationships in CRM data. Bankers are pointed toward high-value deals and deals likely to close. Even better, it adapts to changing deals and customer information in real time, so teams can modify their approach.
Marketing
AI can create more personalized content, tailor messaging and offers, and automatically create web pages and campaigns, thereby increasing campaign effectiveness and ROI.
An example is content creation. Generative AI can produce subject lines and email body copy that resemble human writing. By combining this with customer behavior predictions forecasted by predictive AI, marketers can send timely and relevant communications that match customer needs to increase engagement.
IT Departments
AI can auto-generate document summaries, produce code from natural language prompts, provide chat-based assistance and auto-completion for coding.
An example is fraud detection and prevention. Generative AI can be trained to simulate fraudulent activities and behavior. This can help teams enhance detection algorithms and protect against new patterns of fraud.
CHALLENGES WITH AI
Banks should establish strict governance as well as a dedicated compliance framework for generative AI, ensuring that these systems are not just effective but also legally sound. Some challenges – and how to address them – include:
- Risk management: Banks should develop generative AI models in tandem with risk experts to tailor outputs to regulatory standards.
- Data privacy and security: Banks must enforce strict data governance policies, use advanced encryption methods, safeguard against using public LLMs, and continuously update their security protocols to ensure that employees don’t share sensitive customer data.
- Bias and fairness: Regular auditing of generative AI models is essential to check for bias, risk of hallucinations, or language toxicity. Training these systems with diverse datasets, and establishing guidelines to ensure decisions are fair and equitable, are crucial steps.
- Change management: Integrating generative AI into existing banking infrastructures requires a strategic change management approach, encompassing clear communication, stakeholder engagement, and a phased implementation of generative AI models.
- AI expertise: Banks should invest in continuous upskilling and training programs to build AI expertise across their workforce, while also tapping into new talent pools through collaborations with academic institutions and tech companies
DATA SECURITY AND OVERSIGHT
Despite the best safeguards against fraud or data leakage, the reality is that bad actors exist. Strike the right balance between automated processes and human oversight. Start with automating smaller, low-risk tasks, then gradually scale up as the system proves its reliability and efficiency. Banks need to be extra cautious and diligent to ensure quality and limit risk.
This includes:
- Conducting regular security audits and updates, and keeping software current to best protect against new threats.
- Reviewing relevant regulations regarding data privacy, intellectual property, and AI ethics.
- Developing or updating policies that reflect ethical AI usage, especially in the context of generative models that can create new content.
Implement AI
Take an iterative approach: Allow employees to experiment with generative AI on internal systems, so they can grow more comfortable and build their skill set.
Continuously monitor and evaluate: Regularly monitor generative AI systems, and their outputs, to ensure they operate as intended and adhere to ethical standards.
Conduct employee training: Educate employees on how to interact with AI systems and understand their outputs.