The financial services landscape of 2026 is defined by a paradox: decision-making has never been faster, yet it has never been harder.
In today’s financial services ecosystem, risk does not announce itself politely. Today, a “Black Swan” event or a coordinated fraud spike can manifest in milliseconds. Sudden liquidity shocks. Data drift in credit models. Even, cascading market sentiment triggered by global events. The traditional reliance on retrospective data and manual oversight has become a systemic liability.
As global cybercrime costs soar and exemplified by India’s 15 billion monthly UPI transactions digital payment volumes reach unprecedented scales, financial institutions are pivoting toward Deeptech.
Deeptech represents a fundamental shift in the “scientific plumbing” of finance. Unlike traditional Fintech, which often focuses on the user interface and incremental digitization, Deeptech leverages scientific breakthroughs to create a strategic intelligence layer. Advanced AI systems built on machine learning (ML), deep neural networks (DNN), natural language processing (NLP), reinforcement learning (RL), and emerging quantum techniques, have begun to reshape financial decision-making.
This evolution moves the industry from a reactive posture of “what happened?” to a predictive and prescriptive stance of “what will happen, and how should we respond?”
Across fraud prevention, credit assessment, portfolio strategy, and enterprise-level decision-making, AI is no longer an efficiency tool. As McKinsey estimates suggest trillions in banking value potential, deeptech is becoming a strategic capability. One that increasingly sits at the intersection of technology, risk, and governance.
From Rulebooks to Real-Time Intelligence
For decades, pre-dominently in the 1980s–1990s, financial risk was governed by static, rule-based systems. Fraud systems relied on predefined thresholds, creditworthiness was assessed through limited historical variables, and risk management operated on quarterly or annual review cycles. These models relied on “if-then” logic that were interpretable and compliant, yet were easily circumvented and struggled to scale.
The transition began in the big data era (2010–2015), when exploding transaction volumes and digital payments drove ML models to supplement rule-based systems. They enabled pattern recognition at scale, reducing latency and improving accuracy. Yet these models often ran in batch cycles and struggled with concept drift where historical patterns no longer reflected present risk.
It was the adoption of deep learning post-2016 that accelerated the transformation. Today, adaptive AI systems dominate, continuously learning from behavioural signals, network relationships, and contextual data. On the contrary, traditional systems triggered false-positive rates of 20–30%, causing alert fatigue and lost revenue.
These systems ingest petabytes of unstructured data in real-time to identify patterns invisible to the human eye. This evolution addresses scale and speed imperatives – fraud spikes or market shifts now demand immediate probabilistic judgment rather than hindsight review.
The Move Toward Proactive Prevention
Fraud detection is the most mature battleground for Deeptech. Traditional systems focused on detecting known fraud patterns after transactions occurred, often generating high false-positive rates that frustrated customers and overwhelmed compliance teams. They achieved roughly 85% accuracy with significant false positives. Deeptech-driven fraud systems operate differently. These systems elevate accuracy to 99% in cases, reducing false positives by 60–80%.
Institutions like JPMorgan have already demonstrated the power of this shift, utilizing Large Language Models (LLMs) to cut fraudulent losses by 40%. In India, the RBI’s MuleHunter.AI has successfully flagged over one million mule accounts, proving that anomaly detection is now a scalable defensive shield.
By employing behavioral biometrics, continuous auditing, and Graph Neural Networks (GNNs), banks can further reduce false positives by 62%. This proactive prevention is no longer about catching a thief after the vault is empty, rather about recognizing the “digital fingerprint” of a threat before the transaction even occurs.
Reinventing Credit Assessment
Deeptech is fundamentally reshaping who gets access to capital and how that risk is priced. AI-driven credit models today have moved beyond traditional FICO scores. They incorporate alternative data such as social signals, transaction histories, and behavioral patterns with ML models like XGBoost ensembles for dynamic scoring. This enables real-time “Dynamic Risk Recalibration”, where credit limits and interest rates adjust based on economic shifts. It expands access for underserved segments, including India’s 200 million unbanked individuals.
Platforms like Upstart have demonstrated 15%+ more approvals and 25% default reductions through inclusive, accurate pricing. This is not just an operational win; it is a strategic one.
Yet as deeptech bridges financial inclusion by turning data into a proxy for collateral, it introduces new tensions. As models grow more complex, explainability becomes non-negotiable, especially in regulated lending. Bias risks must be actively managed, and adverse decisions must remain interpretable to regulators and customers.
Most mature institutions counter this by embedding explainability layers, fairness audits, and human oversight – making assessment faster and more accountable.
Augmenting Human Judgment Under Uncertainty
This accountable foundation in credit extends naturally to higher-level strategy, where deeptech augments portfolio and investment judgment under uncertainty. It has moved beyond automation to become a probabilistic decision-support system. Long Short-Term Memory (LSTM) models capture long-term dependencies in time-series data, excelling at volatility forecasting and stock pattern recognition. RL introduces adaptive decision-making, enabling dynamic portfolio rebalancing and optimization in shifting markets. Hybrid setups where LSTM forecasts inform RL agents strike a balance between prediction and action, delivering enhanced returns and accuracy in credit risk strategies. BlackRock’s Aladdin platform illustrates this at institutional scale, leveraging AI for forecasting and scenario modeling across vast assets under management. Sentiment analysis via NLP extracts contextual insights from news, earnings calls, and unstructured filings to inform ESG adjustments and stress testing.
Similarly, robo-advisory platforms are evolving from simple allocation tools into strategic prescribers. Automated tax-loss harvesting, for instance, transforms market volatility into opportunities for tax efficiency, improving after-tax outcomes while allowing fund managers to retain contextual judgment and final accountability.
The emerging consensus is clear: though AI excels at probabilistic reasoning across vast datasets, humans provide ethical guardrails and market nuance. Together, they create a decision system better suited to volatile environments.
Strategic Decision-Making Beyond Transactions
While augmentation delivers superior judgment in portfolio decisions, deeptech’s greatest strategic power lies in extending that same foresight to the highest levels of the organization. It is increasingly shaping board-level decisions on market expansion, capital allocation, pricing strategy, and risk appetite calibration.
On one hand, the hyper-personalization via CLTV models drives 10–15% revenue lifts through tailored products and dynamic pricing. On the other hand, causal machine learning enables precise A/B testing to reveal true outcome drivers. AI not only generates multi-scenario forecasts for market expansion, capital allocation, and risk appetite, but also frees leadership to emphasize strategic prevention over reactive correction.
The real value is not operational efficiency, but strategic optionality. Institutions realizing the highest returns treat deeptech as co-owned across technology, risk, and business leadership. They use it as a shared strategic asset that dissolves silos to build resilience in uncertain markets.
The CXO Decision Lens
The co-ownership is more a boardroom necessity than merely being an abstract or another “IT spend”. For the C-suite, it is a balance-sheet transformer. Yet many boards still underestimate the interplay between project execution and technological risk.
Strategic decision-making now requires a CIO-CRO co-ownership model. While the CIOs own scalable pipelines and data infrastructure, the CRO must govern the “endogenous risks” created by the models themselves, such as drift or biased outcomes, that directly impact financial stability. However, delivering on this co-ownership model brings its own set of practical and structural challenges.
Challenges, Trade-Offs, and Organisational Readiness
Legacy integration remains a major barrier for many institutions, as modern microservices must connect with decades-old core banking systems. Talent shortages are acute, particularly in India, where demand for AI-literate professionals significantly exceeds supply.
Model-specific risks such as drift, overfitting, and overreliance, require regular retraining in volatile conditions. Bias risks from skewed data and the need for decision traceability (through tools such as SHAP and LIME) add further complexity. Yet most implementation failures stem not from technology but from organisational shortcomings: unclear accountability, functional silos, and insufficient oversight.
Effective CIO-CRO alignment turns the co-ownership model into operational reality. Boards should institutionalise AI charters and embed risk practices early in the lifecycle to tackle both immediate and emerging systemic threats, including quantum cryptographic vulnerabilities anticipated by 2030.
These internal measures must, however, operate within an increasingly stringent external regulatory environment.
Regulatory Adherence in an AI-Driven Financial World
Regulatory frameworks are evolving in step with AI’s influence. The EU AI Act classifies credit scoring and algorithmic trading as high-risk applications, requiring transparency, formal audits, and impact assessments. In India, the RBI’s FREE-AI framework defines six core pillars – Infrastructure, Policy, Capacity, Governance, Protection, and Assurance – supported by 26 recommendations for ethical and robust implementation.
The focus has shifted to demonstrable outcomes. Institutions must show clear ownership, comprehensive documentation, third-party oversight, and testing under adverse scenarios. Global and local regulations (Basel III for transparency in models, GDPR/DPDP for data privacy, and governance structures enable cross-border alignment) force banks to be careful with AI but allow controlled innovation within defined boundaries.
The Future of AI-Led Financial Decisioning
By 2027, agentic AI and executive co-pilots will deliver real-time scenario simulation directly to leadership teams. Regulation-first environments driven by EU enforcement and the maturation of RBI’s FREE-AI framework, will define acceptable innovation boundaries.
Quantum analytics will enable massive parallel processing for complex risk and optimisation problems. For starters, HSBC pilots have shown materially improved bond prediction accuracy. Similarly, JPMorgan has demonstrated significant acceleration in Monte Carlo simulations compared with classical methods. Neuromorphic chips and federated networks will support distributed, privacy-preserving intelligence at global scale. As we move toward 2030, the transition to Post-Quantum Cryptography (PQC) will become a mandate to protect the financial system from “harvest now, decrypt later” threats.
Endogenous risks such as instability arising from the interconnected behaviour of AI systems themselves, will demand proactive governance. Boards must invest today in continuous observability, multi-jurisdictional alignment, and adaptive charters to ensure responsible scaling tomorrow.
Conclusion
The next era of finance will not be defined by automation, but by those that govern intelligence at scale.
Deeptech is transforming finance into an intelligence-driven domain. It has evolved from merely a tool for doing things faster; it is a framework for thinking better. For the modern financial leader, success would come by treating AI as strategic intelligence. Through deliberate CIO-CRO co-ownership, regulation-aligned innovation, and retained leadership discretion, they can build lasting resilience and trust in an uncertain world.
The article has been written by Yuvraj Bhardwaj, Co-founder and CEO, Petonic AI
