In an exclusive interaction with Adlin Pertishya Jebaraj, correspondent of Finance Outlook Magazine, Soundar Raj, CFO- SAPAC at NXP Semiconductors, explains how agentic AI and predictive analytics are converting finance from a reporting function to a proactive, AI-based, decisioning ecosystem where routine operations are automated.

Soundar is a finance leader with focus on transformation with over 25 years of global experience in finance, risk management, M&A, and digital transformation. He is known for advocating strong corporate governance, scalable financial infrastructure, and an approach focused on enterprise-wide value creation.

How do you see agentic AI and predictive analytics redefining the operating models of financial services?

Agentic AI and predictive analytics are fundamentally redefining finance operating models by shifting them from a reactive, human-centric process to a proactive, autonomous, and real-time decision-making system.

This transformation moves the finance function from a focus on recording and reporting to one of strategy, oversight, and value creation.

Agentic AI (AI that acts autonomously, coordinates multiple models/agents and executes workflows) and predictive analytics will push finance from a reporting/control-centric operating model to a decisioning and outcomes model where AI agents handle routine end-to-end processes and humans focus on oversight, judgment and strategy.

We can expect three shifts:

• Batch reporting- continuous forecasting & closed-loop execution (real-time cash, balance-sheet and risk adjustments).  
• Siloed teams- agentic workflows (multi-agent orchestration across FP&A, treasury, tax, compliance that route tasks, escalate exceptions and close books faster).  
• Tool pilots- embedded enterprise rewiring (data, control, talent and vendor landscapes are redesigned so models are production-grade rather than experiments).  

Please provide some examples where AI-driven predictive insights have unlocked the financial value of common institutions?

AI-driven predictive insights have unlocked significant financial value across various common institutions by optimizing operations, enhancing decision-making, and identifying new opportunities.

Let’s delve into some practical examples where AI-driven predictive insights unlocked financial value:

• Retail Banking: Personalized product offerings & churn prevention: AI Solution: Predictive analytics analyses customer transaction history, demographics, online behaviour, and even social sentiment to predict which helps in personalisation and reduce the churn.
• Fraud & AML: Banks using ML/predictive models improved detection rates and reduced false positives — saving investigation cost and shrinking regulatory fines exposure. (Large banks’ cases reported improvements in detection and operational efficiency).
• Credit underwriting & portfolio management: Predictive credit scoring and behavioural models reduce default rates and improve risk-adjusted yields (examples from lending fintechs and incumbents).
• Cash & working-capital forecasting: Predictive cash forecasting in treasury reduces unnecessary borrowing and unlocks liquidity, improving interest income and reducing fees.  
• Collections / DSO optimization: Machine-learning models that predict payment propensity enable targeted collections strategies and improved DSO.  
• Revenue / fraud leakage prevention in payments: Predictive routing and anomaly detection reduce chargebacks and operational loss. (Documented client case studies and industry reports show measurable ROI).  

What role are regulators and policy frameworks having on the rate of AI adoption across financial services?

The role of regulators and policy frameworks on the rate of AI adoption in financial services is particularly acute, as the sector is already one of the most heavily regulated. Regulators are acting as both a major accelerator in certain low-risk areas (like fraud detection) and a significant handbrake in high-risk, customer-facing applications (like credit scoring), creating a uniquely cautious adoption curve.

This caution is primarily driven by the need to apply existing, high-stakes financial regulations to novel, opaque AI systems.

Regulators are both an enabler and a governor: they create guardrails that change adoption speed and shape vendor/architecture choices.

Regulators create a structured environment that influences the speed and manner of AI adoption, directly affecting vendor offerings and architectural choices.

Higher compliance burden in regulated jurisdictions raises compliance costs and favours firms with mature data/control stacks. Net effect jurisdictions with clear, proportionate rules (and regulatory sandboxes) tend to accelerate safe adoption; ambiguous or highly prescriptive regimes slow pilots and push spend toward compliance and third-party assurance.  

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What are the challenges facing financial institutions in the incorporation of AI and predictive analytics - data quality, legacy systems, cultural adoption?

Main challenges revolve around data quality, legacy systems, culture, etc.

Firstly, data quality & lineage is a predictive and agentic systems that needs high-fidelity, well-governed data; many firms lack consistent master data and metadata to make models reliable.  Secondly, legacy cores & integration are monolithic core banking/ERP systems that make real-time access and orchestration costly — cloud migration and API enabling are often prerequisites.  

Thirdly, governance & model risk are boards and risk functions must adopt model governance, explainability, validation and monitoring for continuously learning systems, this is nontrivial operationally.  

Finally, the talent & ways of working is the scarcity of people who combine finance domain expertise with ML engineering; existing finance culture may distrust “autonomous decisions.”  

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What is your future vision in AI and predictive analytics transforming financial decisions over the next 5 years?

My vision for the next five years is the transformation of financial decision-making from a human-augmented model to an AI-driven, human-governed ecosystem, characterized by real-time insights, radical personalization, and proactive risk management.

Routine operational decisions increasingly automated — up to a meaningful minority of day-to-day operational finance decisions will be autonomously executed (agentic triage, payment routing, exception resolution), with humans focused on strategy and exception governance. From periodic to perpetual planning, rolling, probabilistic FP&A will become the norm; scenario-driven, model-backed decisions will replace static annual budgets.  

Embedded compliance & explainability, regulatory pressure will push explainability, audit trails and continuous validation into production models making compliance a built-in feature, not an afterthought.  

Value quantification & faster ROI cycles, firms that combine clean data platforms, prudent agentic pilots and strong governance will achieve measurable ROIs, and these success stories will spread across the industry.