Ashima Bhalla, Head of Risk & Investment Oversight Engineering, Aladdin Product Engineering
Advances in analytics have transformed how asset owners and asset managers identify, measure, and monitor risk. Yet many organizations still struggle to operationalize those insights consistently and at scale.
In this Q&A, Ashima Bhalla shares her perspective on how automation, APIs, and AI can help organizations improve decision-making and move more effectively from risk insight to action.
Analytics capabilities have improved over the last decade. What operational challenges do risk teams still struggle to solve?
Although access to analytics has improved dramatically, many clients face a persistent challenge: converting risk insights into consistent, scalable actions across portfolios and investment decisions. At the same time, risk teams have to manage growing portfolio complexity and wider oversight responsibilities, often without a corresponding increase in resources.
Part of the problem is that operational processes remain heavily dependent on manual workflows and individual expertise. When critical knowledge is not embedded in standardized processes, it becomes more difficult to scale operations, onboard new team members, and have consistent oversight. It can also limit the ability of risk teams to harness automation and get the full value from their risk technology investments.
Many firms can identify risk events quickly. Why is turning that into action still difficult?
For many asset owners and asset managers, the challenge is no longer generating risk insights to detect and measure risk but acting on them.
Organizations are inundated with risk signals. Identifying what requires attention and coordinating appropriate responses is often a fragmented process. Data may be generated in one system, while the authority and tools to act may reside in another.
For example, a portfolio that breaches a risk limit can be identified quickly, but translating those insights into action may require multiple teams, systems, and governance processes. Connecting these workflows typically involves manual communication, creating operational inefficiencies.
The next stage of innovation lies in closing this gap between detecting risk and acting on it. By enabling programmatic connectivity across systems, organizations can incorporate risk insights directly into investment workflows, automate routine processes, and focus human judgment on the decisions that matter most. The goal is to create a responsive operating model that helps organizations act on risk insights more quickly while maintaining strong portfolio oversight and compliance controls.
How are firms' expectations evolving when it comes to enhancing risk capabilities within their workflows?
Organizations are looking to institutionalize risk processes and reduce their reliance on key persons. This means risk insights that flow directly into day-to-day workflows, with only limited manual intervention.
Risk capabilities are becoming embedded into broader enterprise operating models through automation, APIs, and AI, which helps to scale operations without proportionally increasing costs.
Risk Radar APIs expose governance processes such as rules, evaluations and exceptions. What client challenges convinced you that these capabilities needed to be accessible programmatically?
As organizations grow, portfolio oversight and compliance monitoring become more difficult to scale through manual processes alone. Firms are under pressure to oversee more portfolios, manage greater complexity, and maintain consistent governance, often without a corresponding increase in resources.
By exposing portfolio oversight and compliance processes – including rules, evaluations, and exceptions – through APIs, clients can incorporate portfolio oversight and compliance checks directly into their operating workflows rather than treating them as separate control activities.
The result is a more scalable and resilient operating model. Routine oversight can be automated, allowing teams to focus on the exceptions that require human expertise and judgment. At the same time, organizations can improve consistency and reduce key-person dependency.
Aladdin as an open platform emphasizes both flexibility and control. How do you balance interoperability and openness with governance, oversight, and auditability?
Firms are integrating risk capabilities more deeply into their technology ecosystems, which improves efficiency and scalability. But they also need to maintain strong control and accountability of workflows.
Openness and control should not be viewed as competing priorities. Both are essential if clients are to scale their investment and risk management processes responsibly.
Firms should be able to connect systems, automate processes, and innovate within their technology environment without compromising portfolio risk oversight, investment decisions, and compliance requirements.
Open architectures such as Aladdin’s open platform provide flexibility to integrate across systems, while embedded controls help ensure that risk and compliance processes are transparent and auditable – even as firms’ technology ecosystems expand.
As firms increasingly automate workflows and scale AI, what will distinguish successful organizations from the rest?
The organizations that succeed with AI will likely be those that invest first in the fundamentals. Clean, consistent data, trusted analytics, and seamless connectivity across systems are the foundation on which effective AI strategies are built.
Equally important are operating models that are scalable, standardized, and modular. AI delivers more value when it is applied to well-defined processes that can be executed consistently across the firm.
Open architecture and programmatic connectivity will play a critical role in ensuring AI-generated insights can be translated into action and embedded into business workflows, enabling faster and more consistent decision-making.
Ashima Bhalla
Head of Risk & Investment Oversight Engineering, Aladdin Product Engineering
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