Banks have spent years buying analytics tools and automation software. Now some are taking a different step: building internal spaces where AI can be tested directly on real banking problems.
One example emerged in India this month. City Union Bank recently entered a four-party agreement to create a Centre of Excellence for Artificial Intelligence in Banking. The goal is to develop AI systems that may support banking work such as fraud monitoring, credit analysis, and regulatory compliance. The agreement was disclosed in a stock exchange filing by the bank.
The project involves several partners. City Union Bank is participating as the banking partner and will contribute industry knowledge and domain expertise. Technology firm Centific Global Solutions is listed as the technology partner. SASTRA University will act as the knowledge partner supporting research and training, while nStore Retech will serve as the implementation partner responsible for deploying solutions.
The structure reflects a model where banks collaborate with technology firms and academic institutions to explore how AI may be applied to banking operations.
Turning AI experiments into operational tools
According to the bank’s disclosure, the planned centre will focus on four main areas: fraud detection, credit risk analytics, customer behaviour modelling, and automation of regulatory compliance processes.
These are not new goals. Banks have used statistical models for many years to assess credit risk and detect suspicious activity. What is changing is the scale of data available to financial institutions and the ability of machine learning systems to process large datasets.
Fraud monitoring is one example. Banks process a large number of transactions every day across payment systems, transfers, and card networks. AI models can examine patterns across these transactions and flag activity that appears unusual. Similar approaches can analyse credit histories, spending patterns, and repayment records to help assess lending risk.
The Centre of Excellence will also explore how AI may assist with compliance tasks. Banks operate under strict regulatory reporting requirements, and preparing those reports often requires teams to review large volumes of transaction records and documentation. AI tools may help classify documents, identify anomalies, and support audit preparation.
City Union Bank said in its filing that it will contribute domain knowledge and industry insight so that the systems developed through the centre reflect real banking operations.
Building talent alongside technology
Another objective of the centre is talent development. The partners plan to support academic programs, internships, and certification courses focused on AI applications in banking, according to the disclosure.
This reflects a broader need within the financial sector for engineers and data specialists who understand both machine learning and banking processes.
Universities are often included in such collaborations because they can link research with industry use cases. In this initiative, SASTRA University will contribute academic research and training aimed at preparing students and professionals to work with AI systems used in financial services.
Why banks are exploring AI centres
Financial institutions face pressure to improve efficiency while maintaining strong risk controls. AI systems are being studied as one way to support tasks that involve analysing large amounts of financial data.
At the same time, deploying AI in regulated industries can be complex. Banks must ensure that systems are secure, reliable, and compliant with financial regulations. Development programs such as Centres of Excellence can provide a setting where models are designed and tested before they are used in operational systems.
The partnership behind the City Union Bank initiative combines several types of expertise: banking knowledge from the bank itself, technical development from a technology provider, academic research from a university, and implementation support from an integration partner.
AI’s growing role in banking
Artificial intelligence is already used in several areas of banking, including fraud detection systems, customer support chatbots, and risk modelling for loans. As computing capacity grows and financial institutions collect larger datasets, banks are studying additional ways to apply machine learning to operations.
Customer behaviour analysis is one area under study. AI models can analyse transaction histories and account activity to help banks understand how customers use financial services. Those insights can influence decisions about product design, lending policies, and risk management.
Another area is operational automation. Tasks such as document classification, transaction monitoring, and compliance reporting generate large volumes of administrative work. AI systems may help sort and review these records more quickly.
Still, adoption tends to move cautiously in banking because errors can create financial and legal risks. Testing environments such as AI development centres may allow institutions to experiment with new tools before integrating them into core systems.
What other banks may learn
The City Union Bank project shows how some financial institutions are structuring AI work through partnerships that bring together banks, technology firms, and universities.
Whether these initiatives translate into widely deployed systems will depend on how effectively the research and development work moves into operational banking tools.
For now, the new centre represents an effort to build expertise around AI within the banking sector while exploring how the technology may support tasks such as fraud monitoring, risk analysis, and regulatory reporting in the years ahead.
(Photo by Etienne Martin)
See also: JPMorgan expands AI investment as tech spending nears $20B
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