4 Steps to Strengthen Risk Management Starting Today

Gone are the days when CIOs focused solely on infrastructure. As today’s CIO, you’re expected to drive innovation and shape the direction of the bank in a tech-driven world. A key part of this shift is rethinking how risks are identified, handled, and anticipated.

As a leader, you must navigate a tough mix of regulations, security threats, and growing customer expectations. While some see this as pressure, I see it as an opportunity - it can be a chance to make bold moves, treating challenges as a driver of growth and digital transformation.   

In this article, I'll explore how risk management in banking is evolving through AI, automation, and alignment between technology and business objectives, helping you respond to today’s demands and prepare for those ahead.  

What Today’s Risk Management in Banking Is Up Against 

Risk today goes beyond a compliance checklist. These four challenges show where it’s headed - and how to stay ahead.  

Keeping Up with Growing Compliance Demands

Regulatory tolerance in the banking sector has narrowed significantly in recent years. Governments are stepping up oversight - think of CFPA’s Section 1033, for example, or the Basel updates - making regulatory compliance for financial institutions more demanding than ever. Today, regulators focus less on checking boxes and more on fairness, transparency, and ethical treatment of customers. This shift is reflected in the numbers, as well: 99% of financial institutions in the U.S. and Canada saw rising financial crime compliance costs in 2024, reaching a staggering $61 billion. The message is clear - compliance is obviously a growing priority, as well as a pressure point for the entire industry.  

The most effective way to keep pace is to embed it into technology architecture as early as possible. That means building flexibility from the start, using configurable workflows instead of hardcoded rules, and making transparency part of the system’s foundation. This helps teams stay aligned with changing regulations and reduces the risk of costly rework later in the process.  

Embedding compliance early also improves how teams manage risk in practice. When systems log actions, track decisions, and surface rationale automatically, it becomes much easier to prepare for audits and respond to reviews. Automation plays a big role here. Reducing reliance on manual checks, helps ensure that every step is applied consistently, documented correctly, and free from the variability that comes with human input.   

This consistency is especially valuable in high-stakes areas like AI-driven decision-making. For example, when banks use AI models to assess creditworthiness, early safeguards - such as explainability checks and bias detection thresholds - can help identify risks before they affect real customers or raise red flags with regulators. This approach strengthens compliance readiness by making reviews more efficient, reducing the risk of late-stage issues, and giving teams the confidence that regulatory expectations are being met from the start.  

Anticipating Cyber Threats as You Scale 

As digital banking expands, so does the attack surface, turning every new channel or integration point into a potential vulnerability. The stakes are high: 62% of customers lose trust after a breach, and nearly half stop using the bank altogether.   

To mitigate this, cybersecurity must be built into the product lifecycle. Including checks like identity verification, access controls, and behavioral monitoring during the design stage helps reduce risk without slowing things down. It also makes it easier to meet regulatory requirements and grow securely as your services expand.  

One such example is Standard Chartered. The bank integrated AI models into its name and transaction screening workflows to detect anomalies in real time. Instead of relying on manual post-event reviews, now they identify threats as they occur. It’s a strategic shift that requires aligning technology with risk, meaning choosing vendors and designing workflows that support early threat detection and automated safeguards. This shift goes beyond adopting new tools. It requires evaluating whether your systems can handle continuous monitoring at scale, retraining teams to act on live risk signals, and coordinating deployment with compliance checkpoints.  

Banking Cybersecurity Showdown: Can Your Bank Outsmart AI Deepfakes?  

Using AI to Strengthen Risk Detection and Response  

Technology is opening the door to smarter, faster ways to navigate risk. As banks gain deeper visibility into customer behavior, they can pair those insights with scalable AI models to prevent threats more effectively.  

At Accedia, I saw this firsthand in our work with Castle Trust Bank. To strengthen their cybersecurity strategy and KYC compliance, we integrated an AI-based identity verification model using computer vision to authenticate documents and facial data in real time. Additionally, we developed a Proof of Concept for an advanced fraud detection solution that leverages predictive analytics to spot and stop suspicious transactions in real time.  

What stood out was how specific improvements uncovered weaknesses in the overall risk approach. To adopt AI-driven risk management for banks effectively, start by reviewing internal processes. Are decisions delayed by outdated tools? Is data siloed? Are risk signals acted on too late?  

Map the lifecycle of high-risk decisions and target technology initiatives that address bottlenecks while aligning with business goals. A centralized data lake, for instance, can eliminate data fragmentation and support a long-term analytics strategy. Real-time monitoring tools detect threats faster and reduce time-to-market for new services. In fact, organizations using AI and automation spot and contain security incidents nearly 100 days sooner, according to IBM’s 2024 Cost of a Data Breach Report. Focus on actions that improve visibility, streamline workflows, and lower reliance on manual work, instead of adopting tools without a clear strategic fit. 

Meeting Customer Expectations   

While technological advances help organizations move faster, they also raise expectations, turning customer behavior into a major source of pressure. Today’s customers want instant credit decisions, fast onboarding, and personalized services across digital channels. Far from being a background concern, these expectations directly influence how banks engage, compete, and grow. According to Accenture, banking institutions with the most loyal and satisfied customers achieve revenue growth nearly 1.7 times faster than their competitors.  

To meet new standards, banks may need to rethink their entire organization around customer experience. We supported a leading European bank in doing just that, restructuring their operations using AI-driven insights to deliver value at every stage of the client journey. The improvements were felt immediately. Response times dropped by 18%, while hyper-personalized marketing campaigns became more effective, boosting customer engagement and overall satisfaction by 25%. The solution also improved debt collection by identifying the most effective outreach strategy for each customer, which led to a 19% cut in average overdue balances and helped build stronger relationships between clients and the bank. 

However, from a risk perspective, this creates two challenges. The first is speed. Customers anticipate instant credit decisions, approvals, and onboarding, leaving little room for manual checks or lengthy reviews. The second is complexity. As services become more personalized, the processes behind them involve more data and, respectively, greater scrutiny from regulators.  

To manage both, understand where in the customer journey risk is introduced. Then implement measures like explainable AI, traceable logic, and selective human oversight. This allows you to deliver the responsiveness customers expect while maintaining control where needed.  

What comes next 

Risk management in banking has moved beyond ticking boxes. It’s about making informed decisions at the right time, across every layer of the organization. That requires embedding risk thinking into digital workflows, using automation and data where it adds value, and building systems that are secure from the ground up. The goal isn’t just to adapt to change, but to shape it in a way that supports growth, earns customer trust, and builds long-term resilience. Is your risk management strategy keeping pace with your innovation goals? Reach out to explore how we can support your next steps. 

This article was originally published by Dimitar Dimitrov, Managing Partner at Accedia, as a contribution to the Forbes Technology Council.