Generative AI use cases across industries
A key example of generative AI is large language models (LLMs) like generative pre-trained transformers (GPT), ChatGPT's foundation. Today, LLMs are improving business processes by acting as virtual assistants that can crunch numbers, retrieve insights from legal documents, and manage customer queries. As a result, these applications can improve experiences, reduce costs, and increase revenues.
From developing a vision to scaling across the enterprise, we already see generative AI in key industries.
Generative AI has the potential to minimize risk, deliver personalized banking experiences, improve advisor productivity, boost customer and employee satisfaction, and enhance regulatory compliance.
- Risk mitigation and portfolio optimization: Generative AI can create synthetic data for rare market events with limited data, such as stock market crashes. This allows banks to build a solid data foundation for developing risk models to identify how such events are likely to impact the bank, how to mitigate that risk, and how to optimize the bank's portfolio accordingly.
- Client engagement and prospect profiling: Generative AI can analyze patterns in historic banking data at scale, helping relationship managers and customer care representatives more easily identify customer preferences, anticipate needs, and create personalized banking experiences.
- Financial advisory: Advisors can use generative AI to automate customer service, identify trends in customer behavior, predict customer needs and preferences, develop financial planning tools to support their customers better, and create more engaging and informative natural language content for customers. The result? Increased customer understanding and better, more personalized advice.
- Compliance: Conducting due diligence against anti-money laundering regulations involves analyzing and managing various content, including sanctions lists, data on ultimate beneficial owners and politically exposed persons, and adverse media information. With generative AI, compliance teams can structure, categorize, tag, clean, and make data searchable. This removes human error, saves time for compliance professionals to focus on higher-value tasks, and improves overall compliance.
While generative AI helps healthcare professionals be more productive – essential in an industry struggling with labor shortages – it also provides insights that can deliver interconnected health and improve patient outcomes.
- Better employee and customer experiences: Generative AI can automate administrative tasks, such as processing claims, scheduling appointments, and managing medical records. It can also provide healthcare decision support by generating personalized patient health summaries based on encounter and claims data. AI is speeding up patient response times and improving the patient experience.
- Analyzing vast volumes of data quickly: With generative AI, healthcare companies can gather crucial insights from relevant media on tonality, branding sentiment, and product perception. One company reduced the time to create media reports by 90%. AI can also decrease the time it takes to create research publications on specific drugs by analyzing vast amounts of data from multiple sources faster than ever.
- Accelerating the speed and quality of care: Using AI as a clinical assistant, it can analyze and summarize electronic medical records, medical imaging data, lab results, and more to give clinicians customized treatment recommendations and flag potential adverse effects. Combined with data from across the value chain, including social determinants of health, it can also help improve drug adherence.
Insurance is all about probability and statistics – a sweet spot for AI. Generative AI can analyze large amounts of data from customer feedback, claims artifacts, climate change records, local weather patterns, economic conditions, and demographic trends. This gives underwriters and actuaries insights that support more accurate risk assessments and pricing to make the claims process effective and efficient.
- Product management and innovation: Generative AI can analyze and summarize customer feedback on insurance products created and owned by actuaries. This helps actuaries to innovate new products and accelerate the product development lifecycle.
- Better policies and claim management: Generative AI can identify potential risks and inform underwriting decisions by analyzing large amounts of unstructured data, such as customer reviews, social media posts, and news articles. Generative AI can also analyze and summarize various claims artifacts to enhance the overall efficiency and effectiveness of claims management.
- Improved communications and customer service: Better communication leads to better customer relationships. Generative AI empowers insurers to automate policy summaries and coverage explanations in language that's easy for the customer to understand.
- Marketing and new business development: Generative AI can provide insurers with a 360-degree view of the customer by integrating and analyzing external and internal data – even across different business units. This highlights customer events to insurers – such as buying a house or purchasing a car – empowering them to cross-sell and upsell the right products at the right time.
- Global agility: For many insurers, conducting business in one language significantly inhibits growth. Generative AI can provide multilingual customer service by translating customer queries and responding in the preferred language.
While generative AI presents a massive opportunity for businesses, it's not without its challenges. Building responsible language and learning models requires balancing data quality, security, and ethics. For instance, LLMs can produce output that seems plausible but may contain errors or biases. Which is why keeping humans in the loop is crucial.