IBM’s watsonx Platform Goes the Distance on AI Governance for Financial Institutions

Balancing the Risks and Rewards of AI in Finance

Survey data shows that business leaders are positive about the potential of AI. In related research conducted by CDW, more than half of respondents said their company is leveraging AI to improve cybersecurity (55%), speed up innovation (52%) and improve the customer experience (51%).

Financial institutions are seeing those benefits in specific ways:

• Customer experience: AI-powered virtual agents, next-best-action recommendations and personalized financial wellness tools
• Risk reduction: Faster detection of fraud patterns, account takeover attempts and anomalous trading behavior
• Operational efficiency: Automated document processing for onboarding, loan origination and claims, plus smarter routing of service requests
• Decision velocity: Better and faster insights for underwriting, pricing, liquidity forecasting and compliance operations

But Sridharabalan also identifies three risks that can impact AI adoption: regulation, reputation and operation.

Regulation risks focus on compliance. For example, are AI operations aligned with industry, government and international expectations? In financial services, that means meeting stringent requirements for data residency, auditability, model explainability, consumer protection and third-party risk — often across multiple jurisdictions.

Reputational concerns are centered on data misuse. Even if this misuse is unintended and entirely accidental, it can negatively impact client and consumer perception. A single biased lending model or opaque underwriting decision can erode trust that took decades to build.

Operational risks are tied to the efficacy of AI deployments. If financial institutions have committed significant resources to AI but are stuck in preproduction, they stand to lose both time and money, and they run the risk of falling behind more agile competitors.

“If you put all of that together, compliance is the leading concern,” she says. “Businesses need to think about compliance and governance more proactively. As a result, we’re seeing a shift in the market.”

EXPLORE: Demystify artificial intelligence adoption in your organization.

IBM’s watsonx AI Platform for Banks, Insurers and Investment Firms

IBM is no stranger to AI. “We’ve been in the field of cognitive solutions and AI for quite a few years,” says Sridharabalan. “We saw momentum with ChatGPT. Our watsonx AI and data platform enables customers and partners to develop, improve, and govern generative AI and machine learning models.”

For financial services IT leaders, that means you can pursue generative AI and ML initiatives without compromising the controls that regulators and your own risk teams require.

The watsonx platform follows suit with a sophisticated approach to AI that includes three core components:

1. Data: Data is a trusted lakehouse that lets companies access data where it lives. This approach is especially relevant for financial institutions dealing with data spread across core banking systems, claims platforms, trading environments, customer channels and compliance repositories. This arrangement can save companies up to 50% on data warehouse costs, Sridharabalan notes, while still supporting governance and lineage across structured and unstructured data.
2. AI: The platform is entirely customizable, so whatever financial institutions want to build — fraud models, credit risk scoring, claims triage, contact center copilots or investment research summarization — and however they want to build it, watsonx.ai can help. IBM experts are also there to guide IT leaders strategically. “Companies can use one of our foundational models,” says Sridharabalan. “They can also bring their own model to the platform. It’s completely open from that perspective.”
3. Governance: With watsonx.governance, organizations get oversight at the model lifecycle level. Risk can be understood and governed at each stage of development, deployment and production — aligning to banking and insurance MRM standards, internal audit requirements and emerging AI regulations.

Sridharabalan notes that it’s a priority for IBM to meet customers where they are.

“Watsonx is available both as on-premises software and as a service,” she says. “It’s built on RedHat OpenShift. Companies can deploy it in the cloud of their choice.”

For financial institutions, that flexibility supports:

• Hybrid and multicloud strategies
• Data residency and sovereignty requirements
• Latency-sensitive workloads (such as real-time fraud scoring)
• Clear separation between dev/test and regulated production environments

Why watsonx.governance Gives Financial Teams More Confidence

Without a clear governance plan in place, no AI strategies will be successful — especially in regulated environments. Watsonx.governance (the governance component of the all-in-one platform) takes that guesswork out of the equation.

Financial institutions can:

• Evaluate, track and document the history of machine learning model iterations to satisfy audit and validation needs.
• Monitor models in production to ensure outputs remain valid and accurate as market conditions, fraud patterns or consumer behaviors change.
• Evaluate models for potential bias — critical for fair lending, equitable insurance pricing and responsible wealth advice.
• Automate retraining and review workflows based on production feedback, while keeping human risk oversight in the loop.

“Think of the watsonx platform as a vehicle to reach your desired outcomes,” says Sridharabalan. “Watsonx.ai is the accelerator. Watsonx.data is the fuel. And watsonx.governance is the safety features that keep you on track.”

For IT leaders in banking, credit unions, insurance and investment firms, that “safety features” layer is what unlocks scale: It helps teams move from promising pilots to confident production deployments, along with governance that stands up to regulators, auditors and the expectations of your customers.