The digitalization of the wealth management industry has been in full swing for many years, with firms looking to replace outdated and manual workflows with more efficient digital workflows.
A recent survey by PwC of 264 asset managers and 257 institutional investors from across 29 countries and territories, found that 84% of the respondents believe technology will improve operational efficiency. It also indicated that 72% see technology as a way to improve employee productivity. With this backing behind it, it comes as no surprise that digitalisation strategies have been a top priority across wealth management.
Unfortunately, firms can only do so much at a time and as a result, manual workflows are still prevalent across the industry. However, while some processes might seem fine to continue with manual work, they could be causing a significant burden on the business.
FinTech Global recently spoke to several players across the WealthTech sector to get their thoughts on the hidden costs of manual processes in wealth management.
For Hari Menon – Global Delivery & Business Head for Wealth, Capital Markets, and AI at IntellectAI, the real impact of manual workflows is the cumulative impact. A simple manual task that only takes the user a minute to complete does not seem that big of an impact. However, that quickly adds up if the process must be repeated hundreds of times each week. Similarly, if there are other manual tasks that have a similar small-time cost, it spirals to take up a huge chunk of an employee’s time.
Menon noted, “The cumulative impact of manual processes, though individually appearing low-risk, results in high hidden costs. These costs manifest primarily as decision latency, operational drag, and control overhead, rather than direct expenditure.
“Studies in financial services consistently indicate that operational staff dedicate 30–40% of their time to manual reconciliation, exception handling, and rework. A less obvious consequence is the effect on organisational judgment. Relying on manual intervention slows decision-making, introduces inconsistencies, and quietly escalates operational risk.”
A study from McKinsey claimed that relationship managers spend between 60 to 70% of their time dealing with non-revenue activities. A major cause of this is attributed to a reliance on legacy systems and spreadsheets. By neglecting to replace outdated tools prevents employees from focusing their time on higher-value tasks.
However, Menon noted that the deepest cost lies elsewhere. They said, “The most profound cost is not the manual nature of the processes themselves, but the resulting tendency for organisations to structure their decision-making around these inherent limitations. Over time, this subtly yet significantly molds organizational behavior.”
Petr Brezina, GRC implementation at KBC Asset Management, also noted that the hidden costs are a lot more subtle than operational expenses. Instead, it is the risk, operational disturbance and lost opportunity.
He said, “Manual interventions introduce latency (time to respond), increase operational risk and most importantly – make processes harder to scale and flexibly adapt when regulations, products or expectations of our clients change. Over time, this stacks in form of a ‘process debt’ that is far more expensive (taking all the costs into account), than it appears on paper.”
Elsewhere, fincite founder & co-CEO Friedhelm A. Schmitt pointed to four core hidden costs, the first of which being variability. He noted that manually entered data varies in quality from person to person. This difference can force additional checks, reconciliations and exception handling. “What looks like a simple manual step at the beginning becomes an entire secondary workflow later in the process.”
The second cost is the non-usability. He noted that manually entered data typically has inconsistent formats that cannot be used in downstream processes. This means firms are forced to collect the same data multiple times, once to enter it and then again to clean, normalise or re-enter it into a usable form.
Schmitt’s third hidden cost is AI paralysis. While AI has been the centre of focus for many in wealth management, many have incompatible data foundations that lack the quality or consistency needed for the technology to work. “The result is a widening gap between digital ambition and operational reality.”
The final cost is that it creates an invisible tax on the firm. “Every handwritten note, every bespoke spreadsheet, every exception triggers follow-up tasks, escalations and audit trails. Individually they look small, but at scale they silently erode margin and slow down advisors.”
He added, “In short: Manual work doesn’t just add cost, it blocks reuse, prevents automation and delays the transition to AI-driven decision-making. The hidden cost is not the manual step itself, but the tail of complexity it leaves behind.”
In a similar vein, Fredrik Davéus, CEO and co-founder of Kidbrooke, believes the biggest cost caused by manual workloads is lost capacity and missed opportunity. Manual work strips time away from advisors, which could have been used for high-value activities, such as understanding clients, managing risks and delivering better advice. Instead of fostering the client relationship, advisors are re-keying data, reconciling reports or manually interpreting documents.
He said, “That cost rarely appears on a balance sheet, but it does directly limit growth. There’s also a compounding effect. Manual processes introduce delays, inconsistencies, and errors that reduce confidence in data. Once trust in data erodes, decision-making slows down and firms become more conservative than they need to be, which is a hidden strategic cost. In short, manual processes make firms slower and less responsive.”
The stubborn manual work
Despite digital transformation being a major priority for firms, there are still areas where manual workflows remain widespread. While every firm is different, there are many common areas that have been missed by digitalisation. Client onboarding and remediation, client onboarding, suitability reviews, portfolio rebalancing exceptions, ongoing advice reviews, risk and compliance checks, and data reconciliation, to name just a few.
Menon added, “Research from McKinsey & Company suggests that over half of end-to-end processes in financial services still require manual intervention, even in organisations that consider themselves digitally mature. The issue is rarely technology availability. It is structural. Many workflows cut across functions, data domains, and governance boundaries. Automation initiatives often focus on isolated steps rather than the decision logic that connects them.”
Whilst a widespread problem, one area seems to be causing a significant burden on teams, handling unstructured data.
Schmitt explained, “Some of the most stubbornly manual workflows sit exactly where the most valuable data is created: in the front office. Even after years of digital transformation, many client-facing processes still rely on paper, handwritten notes, PDFs or ad-hoc email exchanges. Profiling questionnaires are completed offline, advisor notes are transferred manually into CRM systems, and suitability information is re-entered into core platforms. Front-end digitisation has improved the interface, but it has not eliminated manual data capture behind the scenes.”
While not perfect, in the middle and back-office data is stored in structured databases, he added. While there is still manual reconciliation and exception handling, it is on a different scale, as teams work with bulk data and not fragile, high-value client input. This lack of automation and digital support creates a major bottleneck between the front and middle office. Whether it is account openings, portfolio onboarding, the handover of client instructions, trade orders or mandate updates, the processes require manual validation, enrichment and intervention due to inconsistent formats, taxonomies and validations, he stated.
“That is why front-office workflows have resisted automation: they produce high-value, high-context data, but in formats that are still too unstructured, too fragmented or too manual to be consumed by downstream systems or AI. Digital front ends have improved the experience, but the data layer behind them is still largely manual.”
Davéus shared a similar opinion, noting that while firms have understood the importance of customer experience and ensuring streamlined interactions, they have neglected the second half of that issue, connecting the dots. Many firms have updated their front-end experiences with new digital forms and portals, but the underlying work is largely unchanged.
Measuring the success of automation
When automating a workflow, firms will want to know the ROI, but what is the best thing to track? While the most obvious answer might be the cost reduction, it might not be the best metric to measure.
Menon noted that it is far more informative to assess the capacity released, error rate reduction, decision cycle times and consistency outcomes. These will help show whether automation is changing operations rather than just how much is spent. “According to benchmarks published by Deloitte, firms that take this broader view typically see 15–25% improvements in operational efficiency, alongside better control and scalability. The most telling metric is whether automation allows experienced teams to focus on judgment rather than reconciliation. If it doesn’t, the ROI is usually overstated.
Brezina shared a similar view. He pointed to error reduction, faster process cycles, improved traceability and the ability to absorb higher volumes without increasing team size as far more important metrics to monitor.
Adding to this list, Davéus sees the most meaningful metrics being how many more clients an advisor can manage without dropping quality, how quickly insights move from data to decisions, and whether there is consistency across teams.
“There’s also an important risk-adjusted ROI. Automation that improves auditability, traceability, and documentation quality significantly reduces regulatory exposure, even if that benefit isn’t immediately visible in P&L. The strongest ROI cases combine efficiency gains with better outcomes.”
Where manual oversight will still be vital
While this has focused on the hidden cost of manual work, this is not to say manual intervention is not necessary. As mentioned, technology is there to reduce the tedious admin work so teams can focus efforts on important tasks. Whether it is a simple automation tool, or a more advanced AI-powered solution, humans are still vital.
Brezina said, “We are cautious to keep humans in the loop where necessary. Investment decisions, complex client situations, complex products, interpretation of ambiguous regulatory requirements still require some degree of human oversight. Technology, including AI, is most effective when it augments expertise, improves consistency and transparency, and frees people to focus on higher value decisions, rather than replacing accountability all together.”
Menon added, “Automation is most effective where rules are clear and outcomes are repeatable. It is less effective where context, discretion, or client nuance matters.” This is becoming increasingly emphasised by regulation and accountability rules. “The risk is not automating too much, but automating without clarity on where judgment sits. When automation obscures responsibility rather than supporting it, firms tend to introduce new controls to compensate – often recreating manual work in a different form.”
Looking ahead, Schmitt urges firms to be ambitious with automation, but very intentional. “The rule of thumb is simple: automation should handle the process, but humans should own the judgment. The benchmark is clear: Automate when it increases scalability and returns time to humans. Hold back when it adds complexity or reduces learning. The firms that get this balance right will build organisations that scale faster, learn faster and deliver more meaningful work.”
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