What are the generative AI implications for the non-bank financial sector?
We expect generative AI to reshape the financial sector to a greater degree than others, given its vast data accumulation, labour intensity and high mix of language-related work. We have identified over 50 application scenarios in the China non-bank financial space. We expect that, in the best case, generative AI could lower industry costs by 20% in 2025 and 40% in 2050, incrementally boosting brokers/insurers' valuation by 21%/18% in 2025 and 38%/26% in 2030. With implementation of the Interim Administrative Measures for Generative AI Service Management, we expect generative AI applications to kick off in H223, with improved automation in simple application scenarios in 2024- 25. By 2026-30, we expect generative AI to be widely adopted in complex client-facing application.
How will generative AI be applied in the securities and AM sectors?
- Marketing and customer acquisition: generative AI-driven customer service (7*24), marketing content generation and potential customer identification.
- Wealth management: from customisation, asset allocation analysis to generative AI-enabled information search.
- Investment research: from the use of database, compiling meeting minutes and regular reports to virtual marketing.
- Investment: data mining analysis, stock price prediction, and decision aids.
- Investment banking: writing application files such as prospectus, and verifying bank statements.
- Trading: optimising trading algorithm, coding.
- Mid- and back-office operations: risk management, daily operations, code development, and employee training among others.
How will generative AI be applied in the insurance sector?
- Product design: generative AI models can help facilitate actuarial analysis, capture demand, realise flexible pricing, and prepare product documents.
- Marketing and customer service: Generate tailor- made marketing content automatically helping agents in business acquisition, and underpin smart client service and smart training solutions, etc.
- Underwriting: Help collect and analyse electronic medical records automatically and optimise risk assessment;
- Claims: Help realise the automatic collection, processing and review of claims, optimise fraud detection, analyse historical settlement cases, etc. Some overseas insurers have integrated AI into their business. For example, a Swiss insurer is testing the use of ChatGPT to improve customer services.
