AI Expands Banks’ Options for Modernizing Legacy Cores
PYMNTS | ·
Artificial intelligence is opening new routes between banks’ legacy cores and the applications built on top of them, expanding what banks can build around systems they already have. The change reaches beyond chatbots and employee copilots. AI agents retrieve information from banking systems, invoke software functions, move information between applications and complete defined portions of […] The post AI Expands Banks’ Options for Modernizing Legacy Cores appeared first on PYMNTS.com .
Artificial intelligence is opening new routes between banks’ legacy cores and the applications built on top of them, expanding what banks can build around systems they already have.
The change reaches beyond chatbots and employee copilots. AI agents retrieve information from banking systems, invoke software functions, move information between applications and complete defined portions of workflows. Coding agents help engineers understand and modify the software connecting old and new systems.
A PYMNTS examination of OpenAI’s GPT-6 Astra looked at computer use, which allows AI to operate existing applications through their interfaces. OpenAI also identifies legacy-system modernization as a financial services use case, including migrating COBOL and other legacy code. At the same time, banking technology providers are making application programming interfaces (APIs) and core functions accessible to AI agents, creating additional ways to connect newer applications with established systems.
A modern banking application typically reaches a core through APIs, which retrieve an account balance, open an account or initiate another defined function. Middleware between systems handles jobs such as authentication, routing and data translation. An AI agent can call those same APIs.
Model Context Protocol, or MCP, adds a way for the agent’s discovery of the functions are available and how to use them. Instead of developers constructing a separate AI integration for each function, an MCP server can present approved capabilities to an AI application as tools.
Mambu’s Core MCP offers an example of how that works in banking. It exposes hundreds of core operations to AI clients. Functions that retrieve information can be made available separately from functions that alter information or execute actions, giving institutions control over what an agent can do.
Computer use addresses another part of the installed technology base. An agent can navigate software through the same interface an employee uses, providing access to applications that have not been fully exposed through APIs.
The result is a broader connectivity toolkit: APIs for direct system access, middleware for translation and routing, MCP for presenting approved capabilities to agents, and computer use for applications that still depend heavily on screens and manual workflows.
Connectivity is a pressing issue as banks embrace artificial intelligence.
Recent PYMNTS Intelligence research found that 95% of surveyed large financial services firms have broadly deployed or embedded newer AI tools in data and technology processes.
Integration has further to go. PYMNTS Intelligence found in a separate report a 25% adoption rate for AI in API orchestration and integration. Thirty percent of financial services firms identified data quality and fragmentation as their leading obstacle to further AI deployment.
Legacy infrastructure presents a related problem. As noted here, we found 52% of financial institutions identified legacy technology as an obstacle to real-time payments modernization, while 53% cited manual-intensive internal processes.
Connecting AI with existing systems addresses both the technology estate and the workflows surrounding it.
Artificial intelligence could also change the economics for systems integrators such as Accenture, Cognizant and Capgemini. Coding agents can automate portions of the mapping, coding, testing and documentation involved in connecting bank systems, reducing some project work while giving those firms tools to deliver integrations faster.
Coding agents can help analyze older applications, generate integration code, map data and test connections. MCP can make existing APIs easier for AI agents to discover and use.
At the same time, more agents create more connections to govern. Banks still need to decide which system an agent can access, which customer information it can retrieve, which transactions it can initiate and which actions require human approval. Integration platforms can supply those controls as well as the routing, monitoring and audit records surrounding them.
The practical effect appears in what banks can put in front of end-users, including employees and customers.
A commercial lending application can pull customer and account information from several systems. A servicing agent can assemble payment histories and account records before an employee handles an exception. A treasury application can connect payment capabilities with the workflow in which a business decides when and how to move money.
The economics of modernization change as well. A bank does not have to replace a core merely because it wants a better way to reach information stored there. Core replacement remains relevant when the underlying system cannot support the processing, products or architecture the institution requires.
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