Intelligence Fabric Layer Introduced by Backbase to Make Integrating AI Into Banks Easy

Artificial intelligence (AI) can help accelerate banks’ productivity however it can be challenging to integrate. Looking to make this process more seamless, Backbase, the engagement banking platform, has introduced its Intelligence Fabric layer.

Intelligence Fabric will allow banks to embed a powerful set of data and AI infrastructure and development into their platforms. Backbase will enable banks to implement Agentic AI capabilities that unlock massive productivity gains across all their critical service and sales operations. Banks using the Backbase platform will be able to create AI Agents that augment and orchestrate customer journeys in tandem with underlying workflows, integrations, and real-time data interpretation. The Intelligence Fabric leverages Backbase’s Grand Central — its Integration Platform-as-a-Service for banking — which unifies data from diverse sources. This includes core banking systems, payment gateways, fintech capabilities, and non-fintech systems like CRMs. This integration platform provides a single source of truth for banks, consolidating data from all these sources. Additionally, this enhancement allows for democratised data, making it accessible to not only Backbase, but also banks, their partners, and vendors.

Understanding AI’s potential

By 2028, one-third of interactions with generative AI (genAI) services will invoke action models and autonomous agents for task completion according to Gartner. With unified, real-time access to bank-wide data, knowledge, tools, and workflows on the engagement banking platform, Backbase AI Agents can understand and interpret contextual information to provide personalised responses. They can also execute action plans, from handling small tasks like transaction search or bill payment scheduling to managing entire processes such as customer onboarding. Ultimately, the AI Agents have the potential to reinvent customer journeys and workflows across the bank. Furthermore, built-in guardrails will provide robust oversight, allowing banks to implement strict levels of compliance and governance. Jouk Pleiter, founder and CEO at Backbase commented: “The introduction of the Intelligence Fabric marks a pivotal moment in our mission to empower banks to harness the power of data and AI at scale. “Today, we are making a massive leap forward in unveiling our Agentic AI strategy. We see a future where AI Agents will work autonomously in the background, handling tasks, managing processes, and collaborating with customers and employees. Additionally, the adoption and evolution of these new-gen, super-powerful agents will dramatically reduce internal and external labour spend on overheads. For example, spend on sales, marketing, customer service, and compliance operations.”

Making AI available

Thomas Fuss, chief technology officer at Backbase added: “With native AI capabilities embedded directly inside the Backbase platform, we now provide banks with the infrastructure and developer tooling to seamlessly combine data from various sources, create event-driven systems, and adopt or build AI-agents for specific tasks. Banks will remain in full control of their data and can define and monitor all the guardrails to ensure the AI is working within the compliance requirements set by the bank and the regulators.” Backbase is prioritising the availability of its AI-agent capabilities through a variety of use cases.

Conversational banking

It is leveraging large language models (LLMs) to handle daily banking tasks. For example, accessing accounts, making payments, checking transaction histories, and managing cards across digital channels.

Customer lifetime orchestration

Backbase is also creating AI-driven product activation and up-sell campaigns that enable the bank to increase the product holding per customer. AI-driven predictive nudges will provide contextual guidance to customers, promoting relevant new products such as credit cards, loans, savings, investing, and insurance, all based on customer behaviour and financial history.

Advanced financial insights

AI and machine learning models can be harnessed to analyse customer data and behaviour, providing actionable insights such as early warning indicators for retail customers, cash-flow forecasting for SMBs, and customer health and viability information for relationship managers.

AI-augmented customer support

Lastly, it can deploy cutting-edge genAI models to power customer-facing chatbots that provide safe, accurate, and instant responses, drastically reducing support ticket volume. On the agent side, deploy AI capabilities that analyse customer sentiment, summarise issues, and suggest reply options, equipping support teams with the insights they need to craft personalised, effective responses and enhance the overall service experience.