By Anandhan Kannan
The operational challenge of translating complex regulatory texts into consistent, auditable action represents a persistent point of friction across regulated industries. In financial services, this often manifests as a reliance on manual interpretation, procedural checklists, and disparate systems that struggle to align global policy with local jurisdictional requirements. McKinsey insights indicate that banks paid $19.3 billion in penalties in 2024 alone, a figure higher than ever before, and that two-thirds of global banks have lost clients due to slow and inefficient onboarding and KYC processes. This environment creates significant operational latency, introduces inconsistencies, and embeds considerable audit and regulatory risk within core client lifecycle processes.
My engagement with this challenge began within the operational core of client due diligence in 2006, where early systems prioritised document storage over decision logic. At that time, processes were largely manual, running on platforms like IBM Lotus Notes and FileNet, and lacking integration, risk-modelling, or scalability. The pivotal evolution in my approach occurred during my responsibility for global sign-off on a large-scale client lifecycle management platform deployment for a major international bank. That was a role that challenged me for a transition: moving away from assessing procedural compliance to guaranteeing engineered outcomes. It was during that experience that I discovered a fundamental gap: compliance platforms were often built as digitised checklists rather than as systems capable of applying policy logic.
This insight informed the development of a systematic methodology designed to bridge policy, process, and technology. The approach, known as Policy-as-Code, is centred on the principle that regulatory intelligence should be embedded within systems as executable logic rather than confined to procedural manuals. The following blueprint details the practical components of this methodology, providing a structured framework for deconstructing regulatory language, architecting scalable decisioning systems, and implementing a sustainable model for governed, business-led compliance operations.
Decomposing Policy into Regulatory Atoms
The first and most critical step is to decompose the voluminous amount of compliance manuals and regulatory texts into their fundamental components. A full policy document must be broken down into discrete logic statements. This process involves identifying the universal building blocks, which I refer to as regulatory atoms, that constitute regulatory requirements across all jurisdictions.
These regulatory atoms typically fall into several core categories: the client’s industry classification, their geographic jurisdiction, the financial products they intend to use, and specific AML/CFT risk factors such as ownership structure or anticipated transaction patterns. Furthermore, each requirement dictates necessary data points to be collected and validated, as well as the documents that serve as evidence. A regulation does not simply state “perform enhanced due diligence”. What it should do in reality is to specify that for a corporate client in a high-risk industry, operating in a particular jurisdiction, specific data on ultimate beneficial owners must be gathered and certified articles of incorporation must be obtained.
The outcome of this decomposition is a structured taxonomy of policy, which transforms subjective interpretation into objective classification. It allows any regulatory requirement to be expressed as a combination of these atoms, such as: if the industry is classified as high-risk and the jurisdiction carries elevated regulatory requirements, then the risk tier is set to high and specific documentary evidence is required. This structured logic becomes the foundation for all subsequent coding, and such taxonomies support transparency, auditability through version control, and facilitate compliance with regulatory standards across multiple jurisdictions.
Architectural Foundations: The 80/20 Model
With a clear taxonomy defined, an architecture must be established to host and execute this logic at scale. The most effective model for global operations is what I call the “80/20 architecture,” a design that separates the immutable core of an institution’s global policy from the necessary configurations for local regulatory variation.
The 80% core represents the enterprise’s standardised compliance framework, encoding the principles that apply to every client irrespective of location. This includes the master data model defining client and counterparty entities, the foundational risk-scoring methodology, the standard lifecycle stages from onboarding to offboarding, and universal control procedures like global sanctions screening. This core is designed for stability and auditability, providing the consistent backbone of the compliance programme.
The 20% configuration layer is reserved for jurisdictional implementation, where the decomposed regulatory atoms for specific countries are stored and managed. It contains the local rules that modify or augment the global core, such as a requirement for a jurisdiction specific due diligence data and document collection, unique national tax identifier, a jurisdiction-specific risk assessment form, or a locally mandated additional controls and cooling-off period. This layer is designed for managed adaptability without requiring changes to the core system’s fundamental code.
This architectural separation proves vital in practice. It ensures that the institution maintains global consistency on principles while permitting necessary local flexibility in practice. Updates to local regulations are confined to the configurable 20% layer, which dramatically reduces the complexity and risk of change management compared to modifying a monolithic application. In my experience implementing this architecture across 50+ markets, the approach enabled unified platforms for CDD, MiFID, CRS/FATCA, and full lifecycle events while supporting regional variation without creating silos.
Implementation: Selecting and Employing Rules Engines
The codified logic defined by the policy taxonomy must reside within a dedicated software component designed for this purpose: and the name for it is a business rules engine. According to Gartner, a business rule engine is “a specific collection of design-time and runtime software that enables an enterprise to explicitly define, analyse, execute, audit and maintain a wide variety of business logic, collectively referred to as rules”. Platforms such as Drools, which supports Decision Model and Notation standards, or Oracle Policy Automation are specialised for managing and executing conditional logic separately from core application code.
Implementing Policy-as-Code involves mapping each decomposed rule into the syntax of the chosen engine. A rule from a compliance manual is transformed into a formal business rule within the repository. The natural language requirement “verify the identity of directors holding more than 25% ownership” becomes a programmed rule that evaluates ownership percentage and role, then mandates identity verification when both conditions are met.
These rules are stored as version-controlled assets within the engine’s repository, which provides several key advantages. Rules can be independently tested, validated, and deployed. Their execution is deterministic, ensuring the same input always yields the same compliant output. Perhaps most importantly, it creates a clear separation of concerns: compliance professionals own the business logic within the rules engine, while technology teams maintain the surrounding platform infrastructure. This specialisation increases both the quality of the rules and the stability of the overall system.
In practice, I led the migration of risk assessment models and policy codification engines from Oracle Policy Automation to Drools, creating modular risk assessment models covering industry, geography, product, and AML factors. Business rules that previously were confined to Excel checklists were industrialised into API-driven, testable decision services, becoming the single reference point across platforms and reducing manual intervention by 50% across Client LifeCycle journey including due diligence, sanctions, adverse media screening, PEP screening, periodic and trigger event processing.
Operational Governance: Empowering Business Users
A central tenet of sustainable Policy-as-Code practice is that the individuals who understand the regulation must be able to manage its digital counterpart. If rule maintenance requires software development skills, the model will fail due to bottlenecked change cycles. Providing an intuitive administrative platform for the rules engine is therefore a necessity for operational resilience rather than merely a convenience.
These administrative platformare designed for non-technical business analysts, compliance officers, and legal team members. They typically present rule management through structured forms, drop-down menus referencing the regulatory atom taxonomy, and clear test environments. Authorised personnel can log into a portal to modify a rule’s threshold, add a new document requirement for a specific jurisdiction, or update an approval workflow based on a regulatory change.
This capability transforms the compliance operating model. A regulatory update that once required a formal IT project with a multi-month timeline can be implemented by a business user within days. The change is made in the controlled admin UI, tested in a sandbox environment that simulates client scenarios, and then promoted to production. RegTech implementations with such capabilities have achieved compliance rates higher than 95% while eliminating manual processes.
This velocity and direct ownership are in fact fundamental to keeping pace with the regulatory environment. Analysis shows that multinationals now confront an average of 234 regulatory events per day in 2025, with divergent classifications for crypto assets, data privacy, and cybersecurity across jurisdictions. Maintaining relevance in this environment requires the exact kind of business-led agility that policy-as-code enables.
Ensuring Auditability and Explainability
The primary commercial benefit of Policy-as-Code extends beyond automation; it is the creation of transparent, incontrovertible audit evidence. Every decision made by the system must be explainable and traceable back to the specific rule that dictated it, and this structured rule management is the cornerstone of demonstrable control.
In practice, this means that every action within the client lifecycle platform is logged against the version of the rule that triggered it. When an auditor, regulator, or internal control officer questions why a client was classified as high-risk, or why a particular document was requested, the system produces an unambiguous audit trail. This trail displays the exact client attributes, the specific rule that was invoked based on those attributes, the version of that rule active at the time, and the resulting action taken.
This level of traceability eliminates the weeks of forensic investigation typically required during an audit to reconcile practice with policy. It moves compliance from a defensive posture, based on sampled evidence and manual attestations, to one of proactive transparency. The rules repository itself becomes the first point of reference for any audit, providing a complete and accurate picture of the regulatory logic in force at any point in time.
Studies on Compliance-as-Code implementations confirm the value of this approach, showing 95% audit evidence completeness and 82% effectiveness in detecting policy violations early in the process. The platforms I have deployed across global banking operations have consistently achieved zero high-severity audit findings, precisely because explainable logic and version-controlled rules enable comprehensive regulatory alignment.
Practical Application: The Local Compliance Annex tool
The principles of Policy-as-Code find their most impactful application in tools that directly serve frontline teams. A representative example is the The Local Compliance Annex tool, a solution designed to solve the acute problem of multi-jurisdictional onboarding.
The tool functions as an intelligent requirement assembler. When a relationship manager initiates an onboarding case, they provide basic client profile information: the legal entity type, the primary jurisdictions of operation, and the products requested. Rather than presenting a static, generic checklist, the tool queries the central rules engine in real time, executing a cascading logic that first applies all relevant global core policies (the 80%), then layers every configured local rule for the specified jurisdictions (the 20%).
What the tool produces as a final output is a dynamically generated, client-specific due diligence checklist. This Local Compliance Annex tool is a precise blueprint for the relationship manager, detailing only the data and documents required for that particular client in those particular markets. For a multinational corporation onboarding across five countries, the tool generates a consolidated yet distinct view of requirements for each legal entity simultaneously.
This capability converts a process that was once sequential, opaque, and slow into one that is parallel, transparent, and accelerated. It ensures compliance is comprehensively addressed while materially reducing the time to revenue by allowing commercial activation to proceed in stages across different jurisdictions. In implementations I have led, onboarding timelines reduced from months to days for corporate and institutional clients, with 30-70% faster client activation and the elimination of dependency on 20+ regional specialists who previously interpreted cross-border rules manually.
From Textual Interpretation to Engineered Assurance
The transformation of regulatory text into executable code represents a fundamental maturation of the compliance function. The practical blueprint describes a set of consequential steps that compliance teams can follow: deconstructing policies into a logical taxonomy, hosting that logic within an 80/20 architectural model, implementing it via dedicated business rules engines, and, finally, empowering business users through intuitive management interfaces. In the end, the company should end up with a system where auditability is inherent, explainability is automatic, and adaptability is efficient.
This approach transcends mere process digitisation, establishing compliance as almost a distinct engineering discipline within the financial institution. By building systems where policy is an active, managed asset rather than a passive document, institutions can replace operational uncertainty with systematic control. The strategic advantage should be evident by now: in an industry governed by rules, the entity that can interpret, implement, and justify those rules with the greatest speed, consistency, and transparency will inevitably manage risk more effectively and create a more resilient platform for growth.
About the Author
Anandhan Kannan is a Product and Digital Transformation Leader in Banking and Financial Services, with over 18 years of experience delivering large-scale digital and regulatory change across global financial institutions. He specialises in designing end-to-end customer journeys that balance commercial outcomes, regulatory obligations, and long-term platform scalability.
