Role of AI in Financial Services

Due to the difficulty of shifting sensitive data to the cloud, financial services are migrating their compute-intensive operations to a hybrid cloud using AI. This makes cost-effective AI training and interference optimisation possible.

FREMONT, CA: From banking and fintech institutions to insurance and asset management firms, the goal remains to find ways to manage risk more accurately, enhance efficiencies to reduce operating costs, and improve experiences for clients and customers.

Hybrid Cloud Is Coming on Strong

Many financial service firms are moving their compute-intensive workloads to hybrid cloud as sensitive data is challenging to be migrated to the cloud. This helps make the optimisation of AI training and interference cost-effective.

Nearly half of the firms are moving to a hybrid cloud to reduce the cost of up gradation. By making data portable, standardising MLOps management, software across cloud and on-prem hints at a strategic option for cost reduction and efficiency.

Large Language Models Top the List of AI Use Cases

Natural language processing and huge language models, recommender systems, and next-best-action were identified as the top AI use case. Fraud detection and portfolio optimisation, virtual worlds, synthetic data production, and emerging workloads for the metaverse are also prevalent.

These technologies are being adopted by banks, trading companies, and hedge funds to provide individualised consumer experiences.

Banks Witnessing More Potential for AI to Grow Revenue

AI has a measurable effect on financial institutions. Studies respond AI will both help their organisation's annual revenue and annual costs. Professionals in the financial services industry emphasised the progress AI can bring in business operations by enhancing customer experience, generating operational efficiency, and lowering the total cost of ownership.

For instance, automated financial document analysis and claims processing are being made possible by computer vision and natural language processing, saving businesses money, time, and resources. While recommenders offer individualised digital experiences for a company's consumers or clients, AI also aids in the prevention of fraud by boosting anti-money laundering and know-your-customer procedures.

Financial services professionals have also identified another urgent issue: insufficient data sizes for model training and accuracy. Generative AI, which creates precise synthetic financial data used to train AI models, could be used to remedy this.

Executive Support for AI at New High

A recent development is the growing CEO backing for AI. Financial organisations want to keep developing corporate AI in the future. This will include scaling up and scaling down the hardware, software, and services that make up the AI infrastructure. To empower data scientists, quants, and engineers while easing bottlenecks, a strong, full-stack AI platform is required.