
From Model Governance to Machine Agency: Governing AI that Acts, Not Just Answers
Learn how to govern Agentic AI with human oversight, delegated authority, runtime controls, and accountability to enable secure, responsible enterprise AI.
Embracing modernization in the banking sector comes with a degree of risk. Conventional mainframe systems need a cautious revamp without hindering business and offering customer-centric services.
The primary objective of mainframe modernization is to renovate monolithic operations into modular functions, thus enhancing scalability, reliability, agility, resilience, customer satisfaction, and productivity. This reduces costs, workforce, and human error to ensure sustainability in an ambitious market.
Legacy code base and skills: Financial institutions have lined up to modernize their legacy codes, but with a few issues:
Cost Management: Archaic Legacy codes such as transaction and risk management systems are so entangled with the entire system that undoing and transferring can affect revenues.
Performance and Scalability: The mainframe Modernization of core systems must be robust to manage real-time data processing and high-velocity transactions, providing scalability to accommodate future growth. In such a scenario, handling overloads and delivering persistent performance is a complex engineering challenge that requires timely testing and optimization.
Regulatory and Compliance requirements: Failing to adhere to regulatory requirements such as regulations on data privacy, for instance, CCPA, NIST and financial regulations such as IFRS 9, CFPB, FRS, and FDIC, in the hustle of automation, is one of the drawbacks faced by financial institutions.
Total cost of ownership (TCO): A significant bottleneck is managing the costs involved in planning, selection of vendors, under-maintenance revenue loss, employee training, the configuration of a new system, hardware upgrade, productivity downtime, compliance, regulatory administration, engagement of adept consultants, and licensing of new software.
Mainframe Operating systems such as IBM zSeries, Unisys MCP, Fujitsu and Hitachi Mainframe Systems, and Bull GCOS are extensively used in both the US and European banking industry owing to their myriad advantages such as supporting open standards, enabling scalability, and enhancing performance. They are modernization-ready as they can easily interface with cutting-edge technology.
Support for contemporary database features like SQL improvements, interaction with big data and analytics platforms, ACID compliance, and adoption of cloud-native database technologies are all indicators for preparedness of mainframe database management systems like NoSQL, cloud-based databases, IBM Db2, CA IDMS, or Adabas, towards modernization.
Security frameworks are modernization-ready since they extend access controls, encryption mechanisms, competency audits, measure compliance, handle new cyber threats, have sophisticated authentication procedures, and lucidly integrate with contemporary security.
Decomposition Levers:
After a detailed inspection, financial institutions usually inherit seven levers to decompose monolithic mainframe software into smaller, manageable components, as follows:
Reducing complexity: Removing complex and redundant functionalities, rewriting obsolete legacy codes, dropping manual interventions, and automating workflow to simplify business processes and increase efficiency.
Optimizing Costs: Migrating to cloud technologies through a hybrid approach or transferring specific applications and understanding and downsizing the mainframe systems through resource scrutiny, hardware consolidation, and virtualization technology applications can reduce functionality costs.
Without tampering with the ongoing applications of legacy systems or processes, it is vital to figure out solutions offered by modernization and approaches that Financial institutions should consider
The CBS has become more agile due to mainframe modernization, making it simpler for business functions to access crucial data, implement price changes more quickly, and create novel recommendations to satisfy both present and future client needs.

Learn how to govern Agentic AI with human oversight, delegated authority, runtime controls, and accountability to enable secure, responsible enterprise AI.

Swift’s SR 2026 delay changes the ISO 20022 roadmap. Learn what banks need to do now on structured addresses, compliance, data quality and migration.

Code generation got fast. Getting an autonomous workflow into a bank’s production control environment did not. In Banking & Financial Services (BFS), the blocker is rarely the model; it’s trust. A risk officer will not sign off on a system that cannot say why it flagged something, that does its arithmetic inside a language model, or that leaves no audit trail an examiner can open. That is exactly the gap most agentic pilots die in.
Over the last two quarters, we built a portfolio of ten BFS agentic offerings on Alti AIOS™, Altimetrik’s AI Operating System, and deployed them on two stacks: Google Cloud with Gemini and AWS with OpenAI. This piece is the architecture and design principle behind them.
USA (Southfield) - HQ
2000 Town Center, Suite 170
Southfield, MI 48075
+1 248-281-2500