The Hidden Cost of Manual Intervention in Capital Markets

Capital markets have become a vanguard for digital transformation initiatives, leading where other industries follow. But for the modern workforce powering these institutions, which is highly skilled, digitally literate, and increasingly mobile, this vanguard status masks friction points within the sector that are yet to be resolved. Few are proving to be more abrasive than the continuing need for manual intervention within workflows. 

Despite the power and proliferation of data automation technologies, a recent CRISIL Greenwich report shows that 80% of firms still use manual processes for regulatory reporting. The same is true for 79% of firms when it comes to client onboarding. 

Manual intervention consumes valuable time, burying highly paid professionals in clerical tasks that can be easily automated. This runs counter to the expectations and potential of the modern financial workforce, which is not hired for manual rekeying. 

It represents, effectively, a misallocation of capital, but not necessarily an intentional one. Legacy vendor lock-in, change management resistance, lack of digital skills in compliance teams, and regulatory ambiguity that triggers caution, can all contribute. 

A growing mismatch with the modern workforce

For any business to thrive, let alone those operating in capital markets, human capital must be maximised. Every hour an employee spends on a task must deliver measurable value, yet manual data entry (39%) and legacy systems (57%) are recognised as being top barriers to the digital transformation necessary to liberate full human potential.

Modern firms cannot afford to underutilise talent by forcing skilled professionals to act as error chasers and copy-paste operators. Financial services talent is both expensive and scarce, and having skilled individuals engage in low-value tasks is a wasted investment that can damage morale and retention. These employees don’t want to waste their capabilities duplicating data fields or chasing down formatting errors. For a modern workforce increasingly motivated by purposeful work and development opportunities, this mismatch between role and reality is a key contributor to churn. 

Losing specialist, high-earning talent to mundane and repetitive tasks is only part of the problem, however. Remediation is another factor. Some institutions now reserve up to 5% of their capital each year just to correct errors, most of them avoidable. In addition to a strategic liability in an environment that demands precision, speed, and accountability, it’s also a drag on margins. One that smart automation can directly address.

Slower, error-prone manual processes and their associated costs are not the only risk either. They also undermine the audit trail, making traceability harder and increasing the probability of non-compliance. And as regulators begin to shift scrutiny from outputs to how firms validate and transform data beforehand, another layer of urgency is added to the need for transformation.

Automation without intelligence falls short 

By replacing manual tasks with intelligent, data-first automation, firms can reduce remediation spend, free skilled teams to focus on high-value work, and strengthen their compliance posture. The need for intelligent automation is intensified when we consider that less than 10% of data actually requires exception handling. Yet teams spend hours resolving it.

Moreover, the cost of inaction is compounding with rising data complexity and regulatory pressure as firms scramble to retain talent and reduce the cognitive load of repetitive exception handling. According to the CRISIL report, 70% of respondents cited legacy systems and data silos (62%) as barriers to digital transformation.

Introducing technologies to automate key processes without first considering the role of data is likely to lead to a doomed initiative. Rule-based automation alone cannot solve data integrity challenges. Effective transformation begins with a clear understanding of where data originates, how it flows across systems, and where intervention is currently required. Without this data lineage, automation efforts can replicate existing inefficiencies at scale, amplifying risk instead of lowering it.

Intelligent automation, by contrast, is built on strong data management, lessening operational risk from the start. It allows firms to respond faster to regulatory change and market volatility, for example by deploying smart rules engines to detect anomalies in SWIFT messages or using automated data transformation pipelines to reduce reconciliation breaks.

Though moves towards intelligent automation could be quicker, the CRISIL report suggests firms are aware the need for it, with 80% of surveyed firms saying quality data is the basis of future-ready operations.

Modern operations require modern thinking 

Markets are becoming more unpredictable, regulations are more stringent, and data volumes are more vast than they have ever been. 

Firms may have little control over these external forces, but they can prevent them from unduly impacting overall performance. By digitising critical workflows now, the ability to achieve faster time-to-market and robust, holistic compliance is enabled.  

With over 80% of respondents to the CRISIL report agreeing that operational efficiency directly contributes to a competitive edge, the time to shift the modern workforce from clerical work to strategic compliance and decision-making has arrived. And with only 6% of respondents claiming to have fully modernised their post-trade and reporting stack, the opportunity to secure first-mover advantage, for now, remains viable.