How Financial Services and Insurance Streamline AI Initiatives with a Hybrid Data Platform

By neub9
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Posted in Business | September 07, 2023

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The emergence of new creative AI algorithms like large language models (LLM) from OpenAI’s ChatGPT, Google’s Bard, Meta’s LLaMa, and Bloomberg’s BloombergGPT has led to increased awareness, interest, and adoption of AI use cases across industries. However, in highly regulated industries where these technologies may be prohibited, the focus is less on off-the-shelf generative AI and more on the relationship between their data and how AI can transform their business.

AI has given financial institutions and insurance companies the ability to automate or augment complex decision-making processes, deliver highly personalized client experiences, create individualized customer education materials, and match the appropriate financial and investment products to each customer’s needs. While this technological development is revolutionary, it comes with risks. Institutions must ensure that their AI systems are transparent, reliable, fair, and accountable, comply with privacy and security regulations, and align with human values and norms.

The European Union’s AI Act categorizes AI applications into “banned practices,” “high-risk systems,” and “other AI systems,” with stringent assessment requirements for “high-risk” AI systems. With the complexity of the datasets used to train AI systems and the tendency of generative AI systems to invent non-factual information, institutions must implement robust data encryption standards and automate auditing. They also need to negotiate clear ownership clauses in their service agreements to protect proprietary information.

Generative AI and LLMs are a new type of neural network architecture that captures relationships and context across different parts of a sentence or sequence. While these technologies are revolutionary, implementing AI in financial institutions requires reshaping core business processes, transforming corporate cultures, and managing complex, opaque models. Incorporating data integration, data quality-monitoring, and other capabilities into the data platform itself can streamline these processes and allow financial firms to focus on operationalizing AI solutions.

Commercial AI services present various challenges, such as protecting business-critical IP, safeguarding PII, addressing the “black-box” element, and avoiding vendor lock-in. To address these issues, financial institutions can adopt “Trusted AI,” which involves training models on secure data, deploying and running them internally or in a virtual private cloud, ensuring transparency, and safeguarding the integrity of proprietary assets and sensitive data. Open-source AI models are quickly catching up to commercial providers, offering flexibility, affordability, and transparency.

“Trusted AI” is based on a hybrid data platform that presents a unified view of data distributed across on-premises and multi-cloud environments. This platform uses AI and automation to abstract the complexity of data access, movement, integration, and analysis, enabling financial institutions to embed Trusted AI across their operations.

Financial institutions can kick off their AI strategies by prioritizing open-source AI tools, deploying a hybrid data platform, automating basic processes, and training employees. As they progress, they can enhance the user experience, promote data-driven decision-making, and implement robust cybersecurity defenses.

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