As regulatory scrutiny intensifies and global uncertainty spills into everyday business operations, compliance has become a high-stakes challenge for MSMEs.
Geopolitical disruptions, economic volatility, data silos, and rising cyber risks are forcing smaller enterprises to rethink governance and financial discipline. At the same time, AI is emerging as a critical tool, shifting compliance from reactive box-ticking to proactive risk management.
In an interview with THE WEEK, Alok Lall, a Microsoft veteran and Strategic Advisor at Digital CFO, draws on his experience across technology and finance to explain the structural causes of compliance failures and how AI-driven platforms can help MSMEs navigate an increasingly complex regulatory landscape.
What do you see as the most common root causes of compliance failures in financial services?
Financial services operate in one of the most complex environments with strict regulations and customer accountability. Their processes have been fine-tuned over decades, and with the adoption of technology, have matured into evolving compliances.
That said, compliance failures are no longer a documentation issue but a structural and operational one.
Alok Lall, Strategic Advisor at Digital CFO
The enforcement actions taken by the RBI in 2024 brought to light deficiencies in KYC procedures, cyber incident reporting, and customer account management, indicating underlying systemic weaknesses.
In my discussions with leadership, three focus areas have emerged as particularly significant.
a) Legacy systems and data silos - Infrastructure and application modernisation projects often struggle to overcome the challenges of disjointed data architectures/applications that don’t speak with each other and band-aids patching up gaps in legacy systems. These also inhibit a proactive approach to compliance, rather than a ‘reactive check-box’—one that is omnipresent.
b) Inadequate cyber resilience and reporting - Lack of alerting tools and delayed cyber incident reporting implies technology risk translates directly into compliance failures.
c) Third-party risk management - Accountability of third-party agents for KYC and other outsourced services lies entirely with the entity. The RBI has penalised entities for failing to oversee these agents effectively,
How should financial institutions balance cost-cutting with investing in robust surveillance tech to prevent similar lapses?
Chief Risk Officers and the boards of financial services entities are mindfully aware of the governance needed at all levels to address systemic issues that drive compliance failures.
There is reputation loss, regulatory penalties, and customer retention at stake for every lapse in compliance and hence, the leadership regularly assesses gaps and evaluates technologies that minimise or mitigate.
Process optimisation and governance tech doesn’t require throwing more people at the problem—to strike a balance, these organisations must:
a) Drive cost-cutting towards sunsetting legacy processes and infusing technology in new ones.
b) Prioritise risk based investments - Deploy strong compliance governance technologies in high-risk areas first, aligned with leadership KPIs to drive accountability.
c) Empower people with AI assistants to flag and remediate risks instantaneously or with “human-in-the-loop”.
d) Build a strong ‘Supplier Code of Conduct' to set the tone for third-party agents and value chains, with strong assessment metrics and penalties for failure.
How are geopolitical instability and economic volatility contributing to compliance breakdowns in cross-border financing, and what early warning systems would you recommend for MSMEs navigating these?
Geopolitical instability and economic volatility are landmines that are challenging to navigate through. Economic tariffs, sanctions on shipping routes, currency controls, and sovereign goods are among the many scenarios that disrupt cross-border trade and finance, driving significant operational risk.
While large firms find ways to weather the storm, MSMEs need practical tools to build an early warning system. Heat maps that track issues in trade lanes, FX basis monitoring, bank advisories on settlement delays, or tightening documentation requirements are some of the leading signals MSMEs should be on top of to mitigate compliance breakdowns.
From your experience at Microsoft and now as Digital CFO, what role does siloed data play in compliance failures, and how can integrated platforms mitigate risks like those seen with the regulatory complexity surge?
Fragmented data architectures and unstructured data repositories spread across legacy systems create blind spots in adhering to regulatory compliance. Often, the quest for a ‘single source of truth’ dies as complexity across these systems weighs heavily on time, and the cost required to modernise them.
This also leads to delayed fraud detection and incomplete regulatory submissions. Across all sizes of businesses, it is thus evident that data integration is not just a technology upgrade, but a governance imperative, in a landscape of rising regulatory complexity,
Digital CFO addresses this by unifying financial, operational and regulatory data streams for MSMEs, thus breaking down silos for real time compliance.
The platform has been engineered with internal controls embedded at the transaction level. Coupled with structured bookkeeping and financial management, MSMEs can mitigate errors that distort ledgers, compliance filings, cash flow statements, and management insights.
There are reports suggesting that AI has driven efficiency in areas like transaction monitoring and fraud detection this year. How has this impacted compliance costs and accuracy for smaller financial players?
AI in financial management and bookkeeping builds a strong foundation for decision insights and compliance automation. It is truly a democratising force.
Technologically mature financial services organisations have led adoption, while smaller players are moving from pilots to production.
Across a diversity of compliance use cases, organisations are deploying AI to reduce false positives, infer patterns to detect frauds, and for multi-modal capabilities to digitize KYC, among others, thereby improving accuracy, reducing manual reviews, and mitigating compliance fatigue.
For example, RBI Innovation Hub’s Mulehunter.AI and NPCI’s AI pilots show how collaborative models improve fraud detection accuracy by up to 85 per cent, and reduce mule account risks.
Data quality and integration is foundational to AI-driven efficiency. As I had mentioned earlier, data silos are the bane of any AI-led impact. Hence, it is critical that organisations accelerate modern data architecture projects, or in the case of MSMEs, migrate to modern AI based bookkeeping platforms.
What challenges have you observed in AI's integration for compliance, such as data privacy concerns or the 'black box' problem, and how is Digital CFO addressing them in its AI-powered platforms?
Customers and partners with whom I have engaged on AI point out two concern areas associated with AI (apart from data silos)—data privacy and explainability. 4
With the recent notification of the DPDP Act 2023, accountability, strict consent, breach notifications, and penalties require organisations to institutionalise platforms that classify data, and minimise data required by AI for lawful processing of privacy data.
Similarly, regulators across the world are demanding explainable models whose algorithms uphold trust and accountability.
Digital CFO’s architecture makes it easy to embed privacy-by-design, ensuring data inventories, minimisation and immutable audit logs.
How has AI shifted financing from reactive to proactive models—like predictive risk scoring—and what governance frameworks are essential to avoid the ethical pitfalls?
With the rapid advancement in AI technologies, models are becoming smarter, less computing-intensive, and hence cheaper to adopt. AI has therefore accelerated the reactive to proactive (or even pre-emptive) shift across all sizes of business.
RBI’s FREE-AI principles provide foundational guardrails on AI adoption in financial services:
a) Predictive risk scoring - Lenders depend upon AI to analyse supply chain stability, and cash flow patterns to determine customer risk trajectory.
b) Simulations - Domain-specific models drive stress-tests for both local and global scenarios
c) Bias audits - With inherent bias in training data, bias testing, audit trails, and human oversight ensure bias doesn’t perpetuate unfair outcomes.
d) Explainability - Critical decisions must have an audit trail explainable to a customer/decision makers.
Looking to 2026, with nearly 80 per cent of institutions investing in AI for financial crime compliance, what ROI timelines and use cases do you anticipate for MSMEs?
ROI timelines for MSMEs vary by the industry they are in (or support), and their own preparedness in AI for financial management and compliance. Choice of use cases and ready solutions from providers also determine impact timelines.
MSMEs have already started leveraging AI for fraud detection and prevention among others. The impact is being realised through a reduction in false positives and compliance failures. Agentic AI is helping MSMEs move from flagging errors to recommending corrections in regulatory filings.
In a year of tempered AI optimism and rising tech-driven risks, what balanced strategies should finance leaders adopt to harness AI without over-reliance?
2025 has seen AI democratise technology across a diverse set of industries.
While tempered AI optimism may be driven by unclear ROI, the need to hold accountability on the right KPIs is critical for the success of AI based projects. Organisations that have yet to see impact must also ensure every project prioritises explainability, auditability, and cybersecurity.
Technology resilience requires platforms to be highly available. Again, data readiness—quality, lineage and integration—ensures compliance screening becomes mainstream.
Lastly due diligence on the provider’s compliance and security standards is critical.
By balancing innovation with discipline, leaders can harness AI’s benefits while mitigating risks effectively.
