The potential of artificial intelligence for banking, financial services and insurance (BFSI) is increasing all the time. The global market value for AI in finance is set to reach $374 billion by 2029, fueled by the ability to increase customer personalization, combat fraud and better manage risk.
To be able to take full advantage of these opportunities, BFSI firms need to be AI-ready, which means having all the building blocks in place across data, personnel and culture to enable a good implementation. This includes proper preparation: while more than 70% of organizations test and iterate AI on live implementations, only 4% do so in controlled sandbox environments. This is largely down to high costs of computing with large-scale models, and the complexity of recreating real-world data environments.
In this blog, we’ll explore what this looks like in practice, how to address the key challenges along the way such as integrating with legacy systems, and the emerging technologies that will shape the future of AI in finance.
Every successful AI implementation starts with a strong foundation that maximizes available resources and optimizes AI output. In the context of the BFSI sector, that means four things:
A strong cloud-native architecture delivers the flexibility that is essential to adjust the contribution AI makes, depending on business demand. This could be in the form of hybrid cloud, combining on-premise and cloud resources, or multi-cloud where multiple providers’ services are combined without the risk of vendor lock-in.
This enables elastic scalability, where computing and AI resources can be adjusted in real-time through auto-scaling, usage-based billing, and cost observability. For example, performance can be turned up during busy transaction periods, and turned down at quieter times to save on operating costs.
We live in a world of increasing regulatory demands, such as GDPR’s ‘right to be forgotten’, CCPA’s consumer privacy requirements, and the digital operational resilience regulations of DORA. This makes quality, well-governed data essential for BFSI organizations, across data lineage, accuracy and consistency.
Of course, AI systems are only as good as the data they’re supplied with. So robust data governance frameworks, with clarity around policies and controlled roles, are vital to maintain compliance and ensure data integrity.
The rise of APIs means that legacy financial systems are becoming more and more integrated with modern data sources. Having secure, standardized communication across platforms enables these data flows, and real-time data streaming architectures allow customer transactions, market feeds and IoT inputs to be processed continuously. All this can generate new levels of actionable insights that can improve and expedite decision-making.
A good architectural approach can help BFSI organizations strike the right balance between domain-specific expertise and control, and enterprise-wide accessibility, whilst delivering benefits to the AI team through faster feature delivery and improved context relevance.
This can be achieved in two ways. One is through a ‘data fabric’ that brings data together in an operational layer, and uses machine learning to process that data and uncover new insights. The other is a ‘data mesh’, where data ownership and management is decentralized to individual business domains, helping foster a culture of innovation, sharing and collaboration across a workforce. A fabric is generally better where centralized compliance is a priority, while a mesh is the stronger option for domain-aligned ownership.
Putting those building blocks in place can be easier said than done, but it’s by no means impossible. From our experience partnering with BFSI organizations on their AI implementation, these are the solutions to the biggest and most common challenges they face:
Generative AI is reshaping requirements around data quality, curation and governance. Just having ‘big data’ will no longer be enough to leverage insights from Large Language Models: the quality and relevance of that data will be the priority.
The importance of this will be emphasized in a consumer climate where hyper-personalization is transitioning from innovative differentiator to expected necessity. Quality data fed into real-time, unified data platforms will allow BFSI organizations to move beyond segments to engage with individual customers.
All this will take place in the context of consistently increasing regulatory pressures, especially in BFSI where rules and restrictions are especially stringent. The use of Explainable AI (XAI) will have considerable value in adding transparency to AI insights and decision-making, helping meet consumer expectations for ethical practices and building customer trust in the process.
An AI-ready data infrastructure is critical at a time when AI’s influence in BFSI will only continue to increase. This may sound like a complex endeavor, but it’s entirely achievable with the right strategy and expertise in place.
Ciklum has a proven track record working with BFSI organizations just like yours, helping them maximize AI capabilities and optimize cloud spend. We can support you in preparing your data infrastructure for emerging technologies like Generative AI, leveraging cloud-native and FinOps for AI solutions that are cost-effective and scalable, and build data pipelines that can turn unstructured information into actionable insights at pace.
Consider automating insurance claims processing as a practical example: our AI-ready infrastructure enables real-time analysis of claim documents, photos, and historical data to instantly assess validity, estimate costs, and route approvals. This reduces processing time from days to minutes while maintaining regulatory compliance, and underlines the ability of large language models to extract structured data from unstructured data formats.
Our four-step approach covers every base:
To find out more on transforming your data infrastructure in an AI-driven future, contact our team to discuss your specifics, or read our whitepaper to learn more.