IBM recently took the time to brief analysts on its activities in Financial Services, a sector which represents roughly 30% of total revenue.. It is applying its technology and consulting assets to support the banking sector’s front-to-back transformation in terms of the customer-facing platforms it is deploying, as well as the back-to-front transformation that is being driven by gen AI.
Because the banking industry is especially wary of the risks gen AI poses around data sovereignty and privacy, customer-facing AI is proceeding far more slowly in this sector than the technology’s adoption in the banking middle and back offices. The challenge with this back-to-front approach is that greater business returns on investment are typically found closer to the customer interface.
Bringing a surgeon’s scalpel to transformation
At a very high level, IBM’s transformation work within financial services primarily addresses migrating infrastructure to hybrid cloud, and decoupling monolithic systems at the application level. Irrespective of the starting point for transformation, Shanker Ramamurthy, IBM’s Global Managing Partner for Banking says he is confident that “IBM can land anywhere and expand from there,” because IBM has an end-to-end stack spanning IBM infrastructure, its hybrid cloud platform, AI and data platform, AI assistants and products as well as IBM Consulting.
Gen AI is a huge opportunity for the banking industry with Ramamurthy stating that “code modernization represents the largest gen AI opportunity for the banking industry today.” The use of IBM watsonx Code Assistant to modernize code while simplifying system context, design and redundancies is proving a strong capability. Other opportunities include supporting lending functions with automated underwriting and credit decisioning as well as improving the accuracy and effectiveness of fraud, Know Your Customer (KYC) and anti-money laundering (AML) detection models.
Ramamurthy explained that IBM Consulting is taking the Banking Industry Architecture Network (BIAN) to unlock value using gen AI. By applying this framework IBM can deconstruct each bank into functional blocks in its Component Business Model (CBM) which means that consultants using CBM can feed transformation directly into the BIAN model. Ramamurthy likens this to “bringing a surgeon’s scalpel rather than a blunt axe to transformation.”
Fastest growing gen AI GSI
Glenn Finch, IBM’s Global Managing Partner for Data and AI, explains that 24 consulting offers from IBM generate 80% of the revenue, so it was decided that the company would retrofit those offerings with Gen AI. So far, 22 are now retro-fitted, which Finch believes has helped make IBM Consulting the fastest growing Gen AI GSI. There are many nuances to this kind of statement, as Finch himself is aware. It is one thing to train and engage consultants to use AI assistants as tooling for ad-hoc daily tasks, it is another level of adoption for consultants to use AI to fully automate routine tasks and another step again to get to a point where consultants are co-operating with multiple AI assets and agents to deliver continuously optimized services. IBM’s Consulting Advantage platform is supporting consultants on this new way of working, using a layered framework that keeps business functions isolated from the technology and models used.
IBM is, as ever, client zero for the application of new technology and so is helpfully sharing its own metrics to indicate the value that gen AI can bring. For example, SAP is a big consultancy capability for IBM, and AI has enabled the company to cut the costs of delivering deployments, on average by 50%. IBM has tuned AI assistants and agents for SAP by getting the LLM to recognise the SAP closed garden. On this basis IBM consultancy is currently working with clients on their SAP systems.
In banking, as in other industries, securing AI is a big consideration. Leah Generao, a Partner with IBM Security Consulting, explained that from an AI perspective, IBM is both improving the productivity of teams with AI as well as managing the security and compliance for clients in AI deployments. IBM has built for example, Autonomous Threat Operations Management Platform (ATOM), which is a multi-agent system to perform triage on security alerts, data correlation and enrichment, risk analyses and using Gen AI to suggest investigations and remediations, which can reduce triage and investigation time. Within IBM itself, Generao says that AI in security service delivery has saved $8 million in threat operations cost avoidance, and is projected to provide $7.5 million cost savings over three years. Gen AI has enabled automation of over 40% of client requests, while using AI in security testing workflows which has yielded 5% cost savings in XForce Adversary Services Red Team testing for their clients.
My take
IBM’s challenge in promoting its ability to apply AI to banking transformation projects is that customers are being coy about the technology’s use in their organizations and this shyness is exacerbated by the fact that most projects are still works in flight. However, it is interesting that where other sectors are starting by applying gen AI to customer-facing processes, financial services is getting started with gen AI in internally-facing processes. This, of course, is one of the reasons IBM sees code generation as the big growth area for AI in financial services. Monolithic core banking apps remain on mainframes and while some workflows will stay there, the main modernization opus in the banking sector continues to be transforming mainframe code into modular code to create more flexible and agile apps. Gen AI is a technology that can do this faster and more accurately than human coders. This is the main opportunity at the moment and IBM is on it, and, is also, helpfully, sharing its own metrics as it does so.
