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Navigating Risk in Capital Markets Using AI & Modern Tech: Exclusive Interview with Girish Gajwani, VP, Barclays
Financial markets are being reshaped by predictive analytics, agentic AI, and hybrid cloud, driving faster, smarter risk management in loan trading and securitization. Emerging technologies like tokenization, privacy-preserving compute, and quantum computing promise to redefine capital markets.
In this exclusive interview, Girish Gajwani, VP at Barclays, shares how these innovations enable real-time insights, proactive decision-making, and resilient, compliant financial workflows.
With over two decades of experience, Girish is recognized for his expertise in AI, cloud-native architectures, and large-scale modernization programs, having led the design and delivery of mission-critical platforms across loan trading, securitized products, and asset-backed securitization.
AI adoption in financial markets is exploding. How do you see predictive analytics reshaping risk management in loan trading and securitization over the next 2–3 years?
Predictive analytics is moving us from reactive risk management to proactive decision-making. In loan trading, models can now analyze servicing data, borrower behavior, market sentiment, and macroeconomic indicators in real time. That means risks such as delinquency, prepayment, or refinancing pressure can be flagged weeks in advance, giving traders and risk managers room to act before they become costly issues.
In securitization, predictive models can simulate tranche-level cash flows and stress-test scenarios far more quickly, allowing more accurate pricing and structuring. By embedding predictive insights directly into workflows, firms can evaluate portfolio risk exposures in minutes rather than waiting on overnight processes. The industry is shifting toward continuous, event-driven monitoring where analytics don’t just describe what’s happening but actively recommend next steps.
Hybrid cloud is now the default architecture in finance. What’s the real challenge—technology integration, compliance, or cultural shift, and how should firms address it?
All three matter, but the real challenge is integration combined with culture. Hybrid cloud brings enormous flexibility, but financial institutions still operate with legacy systems and tight regulatory controls. Without consistent API governance, strong data practices, and compliance-by-design, hybrid deployments can become more fragmented rather than streamlined.
The path forward lies in three pillars:
Technology: Invest in containerization, event streaming, and clear interoperability standards so systems can talk to each other seamlessly.
Compliance: Automate security and regulatory checks inside DevSecOps pipelines so controls are part of the release process, not an afterthought.
Culture: Shift the mindset, cloud is not a side tool, it’s part of the core operating model. Teams must be empowered and accountable for full lifecycle ownership, which accelerates both adoption and trust.
Agentic AI is making headlines, but in financial workflows, where is the industry still lagging and what’s blocking faster adoption?
Agentic AI, AI that can reason, plan, and act, has clear promise in areas like trade exception handling, compliance monitoring, and regulatory reporting. But two major hurdles remain.
Trust and explainability: Decisions in finance carry real risk. If an AI agent flags a suspicious transaction or recommends a trade adjustment, decision-makers need to understand why. Black-box reasoning doesn’t hold up under regulatory or risk oversight.
Integration complexity: Many financial workflows still run on legacy batch systems. Introducing real-time AI agents requires re-architecting for orchestration and auditability, which takes time.
The path to adoption is human-in-the-loop. AI agents can propose actions, while humans validate and build confidence. Over time, with stronger explainability and audit trails, more responsibility can shift to autonomous systems.
Domain-driven and event-driven architectures are gaining traction. How do they change the way financial platforms are designed for resilience and scalability?
Domain-driven design (DDD) and event-driven architecture (EDA) reshape financial platforms by aligning technology more closely with business reality. With DDD, systems are modeled around natural business domains, trading, settlement, risk, and reporting, each operating independently. This reduces coupling and makes platforms more adaptable.
EDA adds resilience and scalability. Instead of request-response patterns, services communicate through events. For example, when a trade is booked, that event flows to risk, settlement, and compliance simultaneously. If one service is down, the event is still captured and replayed later. The result: higher throughput, fault tolerance, and real-time responsiveness.
Together, DDD and EDA allow financial firms to evolve platforms incrementally, add new capabilities faster, and scale with confidence while maintaining regulatory reliability.
Looking ahead, which emerging tech beyond AI and cloud do you see transforming capital markets in the next five years, and how do you see companies preparing for it?
Beyond AI and cloud, several technologies stand out:
Tokenization and distributed ledgers: Moving securities, loans, and collateral onto tokenized rails will improve transparency, settlement speed, and liquidity. Firms are already testing pilots in repo markets and securitized assets while running them in parallel with legacy rails.
Privacy-preserving compute (MPC and confidential computing): This enables secure, cross-institution analytics, fraud detection, and counterparty exposure without exposing sensitive data. Financial institutions are building “clean rooms” and confidential enclaves for this purpose.
Quantum computing will bring breakthroughs in risk modeling and portfolio optimization. Problems that currently require approximations, like simulating mortgage-backed security prepayment behavior, could be solved in near real time.
Interoperability standards like ISO 20022: Richer, structured data is becoming essential for real-time payments, settlement, and risk reporting. Firms are modernizing data models to align with these standards, which will be the backbone of future event-driven platforms.
What’s common across all of these is preparedness. Leading firms aren’t waiting for disruption; they’re running pilots, modernizing data foundations, and embedding compliance-by-design so new technologies can scale quickly when the time comes.
Closing Thought
Capital markets are entering a period where speed, transparency, and resilience will define competitiveness. Predictive analytics, hybrid cloud, and agentic AI are shaping the present. But tokenization, privacy-preserving compute, and quantum resilience will define the future. Success won’t just come from adopting new technology; it will come from building cultures, architectures, and compliance models that allow innovation to scale responsibly.
