Fraud analytics – Building governance intelligence by enabling early detection
By Deepjee Singhal & Manish Pipalia, Co-founders, Sama Audit Systems & Software Pvt. Ltd.
Every year, organisations lose an estimated 5% of their annual revenue to occupational fraud, according to the Association of Certified Fraud Examiners (ACFE). In India, the challenge is equally compelling. The Reserve Bank of India’s Annual Report found that 89.2% of the value of frauds reported by banks in FY2023–24 related to frauds that had actually occurred in previous financial years, highlighting a significant gap between the occurrence of fraud and its detection. Organisations that deploy proactive data monitoring and analytics consistently detect fraud earlier and experience significantly lower losses than those relying solely on traditional control mechanisms.
Every invoice approved, purchase order released, journal entry posted, vendor onboarded, payroll processed and user access modified leaves behind a digital footprint. Modern enterprises generate millions of transactional records every day across ERP platforms, cloud applications and automated workflows. Hidden within this vast volume of data are early indicators of fraud, control failures and operational risk. The challenge is no longer collecting data. It is interpreting it before risk becomes reality.
For decades, fraud investigations began with physical vouchers, manipulated ledgers, forged signatures and missing documentation. Today, those same risks exist within digital ecosystems. As organisations accelerate digital transformation, automate business processes and integrate AI into finance and operations, the volume, velocity and complexity of enterprise data continue to grow. While these technologies improve efficiency and decision-making, they also expand the potential for control failures, unauthorised access, vendor manipulation and sophisticated financial misconduct. This is fundamentally changing the role of finance.
For today’s CFOs, chartered accountants and audit committees, the expectation extends far beyond preparing accurate financial statements. Boards, investors and regulators increasingly expect finance leaders to provide early visibility into emerging risks, strengthen governance and ensure that internal controls evolve alongside the business. In other words, finance is no longer expected to report history. It is expected to anticipate risk.
Yet many organisations continue to rely on assurance models designed for a different era. Periodic audits and sample-based testing have served businesses well for decades, but they were developed when transaction volumes were manageable and business processes were largely manual. Today’s digital enterprises generate millions of records across multiple systems every month. Statistical sampling can no longer provide the level of assurance that boards and management teams increasingly expect.
The warning signs are often already present. Duplicate payments. Vendor master manipulation. Dormant suppliers suddenly becoming active. Unusual journal entries posted outside normal approval cycles. Segregation-of-duties conflicts. Privileged user access. Pricing anomalies. Procurement irregularities. Payroll exceptions. Individually, these may appear insignificant. Collectively, they often represent the earliest indicators of much larger governance issues.
Recent corporate governance failures across industries have reinforced a common lesson. Whether involving accounting irregularities in high-growth enterprises, governance concerns within large financial institutions or procurement and payment-related investigations across sectors, material failures rarely occur without warning. More often, they are preceded by subtle anomalies embedded within transactional data – signals that remain unnoticed until the consequences become material.
The question for every finance leader is therefore straightforward.
If enterprise data already contains the warning signs, why are organisations still discovering issues only after they become crises?
The answer lies in how organisations monitor risk. Fraud analytics has become an enterprise intelligence capability. It enables organisations to continuously monitor financial controls, identify emerging operational risks, improve process discipline, strengthen regulatory compliance and support faster, evidence-based decision-making.
In our experience of over twi decades, we have seen this shift unfold firsthand. What once centred largely on improving audit efficiency has evolved into a broader focus on enterprise intelligence, continuous monitoring and greater visibility into risk. As organisations become increasingly data-driven, analytics-led approaches are enabling audit and finance teams to move beyond traditional sampling and examine transactional data more comprehensively, while working within existing enterprise environments.
This evolution is changing not just how audits are conducted, but also how organisations identify patterns, anticipate risks and use financial and operational data to support better governance and decision-making.
Technology, however, is only one part of the equation. Analytics identifies patterns. Data highlights anomalies. Professional judgement determines whether those anomalies reflect process inefficiencies, control weaknesses, policy violations or deliberate misconduct. The future of corporate governance will belong to organisations that combine intelligent analytics with informed professional judgement to create continuous assurance, stronger governance and better decision-making.