By Steven Chung, President of Starburst examines how AI is only as good as the data that supports it
AI has the potential to revolutionize banking and financial services, but its effectiveness hinges on data integrity, context, and relevancy. AI-powered risk assessment models, personalized banking experiences, and fraud detection systems all depend on high-quality, well-governed data. AI is only as good as the data that supports it, like the old saying ‘you get out what you put in’. Yet, many financial institutions struggle to harness the full value of their data because it sits in many different silos and suffers from issues related to quality, relevance, performance and cost. What financial services companies need to capitalize on their AI investments is an AI data architecture that spans their cloud and on premises environments, accelerates AI innovation, and solves business problems faster.
Removing the hidden barriers to AI adoption
Most banks operate using fragmented data architectures, often the result of stitching together decades of multi-generational tech from legacy on-premises systems to modern cloud platforms. Data is stored across disparate silos, making it difficult for AI systems to access a holistic view of customer profiles, transaction history and risk assessments. Without interoperability, financial services organizations risk drawing inaccurate conclusions from incomplete data, leading to false positives in fraud detection and compliance gaps. Not only that, but data issues can also stifle innovation, holding back the development of highly targeted and personalized services and applications.
Banking and financial services are a commodity business, where service quality and price drive differentiation set the stage for winners and losers. Investment in a new AI data architecture provides them with the opportunity to remove these barriers and improve access to the banking and third-party data stored across silos. By connecting their data silos, financial services companies will be able to generate real-time views of customers, run faster analytics, and maximize their bottom line while effectively managing risks. Crucially, this will provide them with the foundation they need to power data analytics and their AI data stack to help them get the most value from their data.
Building a scalable AI infrastructure
However, there are considerations that need to be made. AI models require massive computational power and real-time processing capabilities. Traditional banking infrastructures aren’t equipped to handle these new AI workloads. Shifting some workloads to cloud-based architectures can help improve scale, but only 40% of financial institutions have fully integrated cloud solutions.
AI needs a scalable data architecture that accesses compliant data from multiple sources in multiple formats. Financial services companies can ‘future proof’ their AI capabilities by investing in hybrid data architectures that support interoperability between their cloud and on-premises technologies. This will also ensure that data pipelines are structured for scalability, helping to reduce latency issues in AI model deployment. Finally, they should implement robust API frameworks to facilitate data exchange between AI-driven platforms and traditional banking systems. This will remove bottlenecks in AI data workflows and accelerate AI deployments.
Achieving AI data governance
For highly regulated industries like financial services, AI must operate with strong data governance. Poor governance leads to biased models, security vulnerabilities, compliance failures, and reputational damage. While financial institutions acknowledge the importance of data governance, according to a recent Deloitte study, less than a third have a fully operational data governance framework in place. However, the situation is evolving, and financial services organizations can adopt solutions that are designed to scale data governance as quickly as it scales data itself.
They can secure the foundation of their AI data architecture by establishing enterprise-wide data governance policies to ensure accuracy, security and compliance. This is supported by the creation of cross-functional AI data governance teams made up of compliance officers, data scientists and cybersecurity experts. The next step will be to implement data platforms to automate compliance and governance requirements in a predictable, cost-effective manner. With these systems in place, they will be able to conduct continuous audits of AI-generated decisions to prevent biases and regulatory risks.
This will allow banks to unlock the true potential of AI to create operational efficiencies, improve fraud detection and prevention, and enhance customer experience; without sacrificing security or compliance.
Forging the link between data analytics and AI
The template for AI success lies in data analytics. Data analytics and AI have a lot in common. They both rely on data, and both require a firm data foundation to be successful. Many of the practices, systems and solutions covered in this article were originally developed for data analytics. Big data can’t function without access to data lakes, data warehouses and databases that span organizations, stored in silos and held in different formats across different technologies. Similarly, AI models can’t learn from data they can’t access or reach and delivering datasets to AI models in near-real time is not easy. That’s why banks and financial services companies need to look at solutions that have been honed on data analytics and will provide them with a good foundation for all their AI data.
These new AI data architectures will empower financial services organisations to roll out new AI products, while also pushing forward their analytics projects. They can leverage a new suite of AI accelerators to get the most value from their data. In an era where data breaches have replaced bank heists, financial institutions will be able to treat data as a strategic asset, helping them to combat fraud, secure operations, maintain regulatory approval and build a more trusting relationship with customers.
