Executive Summary
Global IT spending in BFSI is expected to reach $715 billion in 2025. India alone hosts 190 BFSI Global Capability Centres employing over 550,000 people. The money is flowing. The headcount is growing. And yet, at a People Matters summit in Mumbai in June 2026, 210 CHROs and business leaders from India’s top financial institutions agreed on a single conclusion: technology transformation is outpacing workforce transformation. The problem, they said, is capability.
We found that conclusion familiar. We have spent over two decades delivering in pharmaceutical and life sciences environments, where the gap between hiring people and those people actually executing in a regulated context is the central operational challenge. Pharma solved it by building governance into the operating model from the start. BFSI technology delivery, despite facing an almost identical regulatory burden, has not made the same adjustment.
This paper examines why BFSI technology programs struggle at execution despite adequate funding and headcount, what the pharma governance model offers as a transferable framework, and how structured team deployment changes outcomes in financial services delivery. It is written for technology leaders, delivery heads, and operations executives in banking, insurance, and financial services who are responsible for making programs deliver, not just staffing them.
1. Two regulated industries, one shared problem
At first glance, pharmaceutical delivery and BFSI technology delivery have little in common. One produces clinical study reports and regulatory submissions. The other produces payment platforms and risk engines. The outputs are different. The constraints are almost identical.
| Constraint | Pharmaceutical Delivery | BFSI Technology Delivery |
|---|---|---|
| Regulatory oversight | FDA, EMA, MHRA, ICH | RBI, IRDAI, SEBI, CFPB, FCA |
| Data sensitivity | Patient health records, clinical trial data | Customer financial data, transaction records, KYC/AML |
| Audit exposure | GxP audits, sponsor audits, regulatory inspections | Internal audit, regulatory audit, SOC 2, PCI-DSS |
| Error consequences | Clinical hold, warning letter, patient safety impact | Regulatory fine, data breach, customer harm, reputational loss |
| Documentation standard | Audit-ready, version-controlled, traceable | Audit-ready, version-controlled, traceable |
| Change management | Formal change control with impact assessment | CAB approval, security review, regression testing |
| Vendor oversight | Qualified vendor audits, SOP compliance | Third-party risk management, outsourcing guidelines |
The table reads like the same industry described twice in different vocabulary. Both environments demand that work is documented, that changes are controlled, that people are qualified, that data is protected, and that the organization can demonstrate compliance at any point. A pharmaceutical company cannot ship a clinical study report that was not reviewed against the protocol. A bank cannot deploy code to a payment system that was not tested against the security baseline. The verb changes. The discipline does not.
Yet the two industries have responded to these constraints very differently when it comes to how they staff and govern their technology teams.
1.1 What pharma figured out
In pharmaceutical delivery, nobody assumes that hiring a biostatistician means you have a functioning biometrics operation. The hire is step one. After that comes competency validation against the specific therapeutic area, SOP training, quality protocol orientation, tool access and configuration, and integration into a governed delivery team with defined roles, cadences, and accountability structures. The time between “employed” and “delivery-ready” can be two to four weeks of deliberate preparation.
This is not because pharma companies are cautious by temperament. It is because the consequences of putting an unprepared person into a regulated workflow are concrete and measurable: a documentation error in a clinical study can trigger an FDA audit finding; a programming error in a statistical analysis can invalidate a regulatory submission. The industry learned, through painful experience over decades, that governance at the team level is cheaper than remediation after the fact.
1.2 What BFSI has not yet adjusted
In BFSI technology delivery, the assumption is often the reverse. Hire the person, give them access to the codebase, point them at the backlog, and expect output. Governance exists at the organizational level, certainly. Banks have change advisory boards, security review processes, and compliance teams. But these controls operate above the delivery team, not inside it.
The difference matters. When governance sits above the team, it functions as a gate. Work is done, then reviewed, then approved or rejected. When governance sits inside the team, it functions as a discipline. The work is done correctly the first time because the person doing it was trained on the standard, follows a defined process, and operates within a quality framework that catches errors before they reach the gate.
Pharma operates the second model. Most BFSI technology delivery operates the first. The result is that BFSI programs spend a disproportionate amount of time in rework cycles, failed security reviews, and change request rejections that could have been prevented if the team had been governed from the start.
2. Where BFSI technology delivery breaks down
We have deployed technology teams for BFSI programs through our work with system integrators and direct enterprise clients. The failure patterns are consistent enough to catalogue.
2.1 The compliance afterthought
A data engineering team builds a pipeline that moves customer transaction data from a core banking system to an analytics platform. The pipeline works. It passes functional testing. Then the security review discovers that PII fields were not masked, that the data lineage is not documented, and that the pipeline does not comply with the bank’s data retention policy. Three weeks of rework follow.
This is not a technical failure. The engineers knew how to build pipelines. It is a governance failure. Nobody told them, before they started, what the bank’s data handling requirements were. Nobody trained them on the compliance baseline. Nobody established a quality checkpoint between “pipeline built” and “pipeline deployed.”
In pharma, this scenario is nearly impossible. A data manager working on clinical trial data receives SOP training on data handling before they touch a single record. The equivalent does not happen in most BFSI technology teams, because governance is treated as the security team’s job, not the delivery team’s discipline.
2.2 The multi-vendor coordination problem
A mid-size insurance company runs its technology operations through seven vendors: one for core policy administration, one for claims processing, one for the data platform, one for cloud infrastructure, one for application development, two for testing and support. Each vendor has its own team, its own tools, its own processes, and its own reporting cadence.
When a new feature requires changes across three of these vendors, coordination becomes the bottleneck. Each vendor operates independently. Integration testing is nobody’s primary responsibility. A defect that surfaces at the boundary between two vendors’ systems triggers a blame cycle that can consume weeks.
Tech Mahindra published a paper in early 2026 acknowledging this pattern directly: BFSI organizations have spent heavily on technology but have not achieved proportional operational efficiency, because the vendor model creates fragmentation that no individual vendor is incentivized to solve. The solution they proposed was a “solutions aggregator” model. We would put it differently: the solution is a governance layer that sits across vendors and governs the integration points, the handoffs, and the quality standards.
2.3 The skill gap that is actually a team gap
Industry data shows a 42% skill gap for AI and data roles in BFSI GCCs. That number gets quoted frequently. But our experience suggests the number measures the wrong thing.
The gap is rarely that individual skilled people do not exist. India produces enough data engineers, cloud architects, and security specialists to fill the open roles. The gap is that those people, when assembled into a team, do not function as a unit. They arrive with different working patterns, different quality expectations, and no shared governance framework. The skill gap is real, but the team gap is larger and harder to close.
A Taggd hiring intelligence report from March 2026 noted that BFSI demand has shifted toward “productivity-ready professionals who can work alongside AI systems, manage regulatory complexity, and bring operational judgment.” That phrase, “operational judgment,” is doing a lot of work. It means people who understand that a Kafka pipeline in a banking environment is not the same as a Kafka pipeline in a retail application, because the data is different, the audit requirements are different, and the consequences of failure are different. You do not acquire operational judgment through a job posting. You build it through governed team onboarding.
3. What transfers from pharma to BFSI
We are not arguing that banks should adopt pharmaceutical SOPs. The specific documents, workflows, and regulatory references are different. What transfers is the operating philosophy: governance is embedded in the team, not imposed on the team from above.
Four specific practices from pharmaceutical delivery apply directly to BFSI technology programs.
3.1 Competency-based deployment
In pharma, nobody joins a delivery team without a validated competency assessment specific to the work they will do. A SAS programmer assigned to an oncology trial is assessed on SAS programming, CDISC standards, and oncology data structures. Not one of those three. All of them.
The BFSI equivalent: a Kafka engineer assigned to a payment platform should be assessed on Kafka architecture, the bank’s messaging standards, PCI-DSS data handling requirements, and the team’s deployment protocols. The assessment happens before deployment, not during the first sprint review when the delivery lead realizes the engineer has never worked with encrypted message streams.
3.2 SOP-governed onboarding
In pharma, onboarding is a governed process with defined steps, documented completion, and a sign-off confirming the person is ready to begin work. This takes two to five days and covers SOPs, quality standards, tool access, and team integration.
Most BFSI technology onboarding consists of laptop provisioning, Active Directory access, and a link to the Jira board. The assumption is that a professional engineer can figure out the rest. That assumption is correct for the technical work. It is incorrect for the regulatory and governance context in which that work occurs. A governed onboarding process that covers the bank’s data handling policies, change management requirements, documentation standards, and security protocols adds three to five days upfront and saves weeks of rework downstream.
3.3 Quality checkpoints inside the team
In pharma, deliverables do not leave the team without a quality review against defined acceptance criteria. This is not an external audit. It is an internal team discipline: the biostatistician reviews the SAS programmer’s output against the analysis plan before it goes to the sponsor.
The equivalent in BFSI: code does not leave the team without a security-aware review, a data-handling check, and a confirmation that the change advisory board requirements are met. When these checks happen inside the team, the change advisory board becomes a confirmation step rather than a rejection point. Cycle times improve because rework drops.
3.4 Documented operating rhythms
In pharma, the team’s operating model is written down: who does what, when reviews happen, how escalations work, what gets documented, and where the documentation lives. New team members read this document on day one. It is not a suggestion. It is how the team works.
Most BFSI technology teams inherit operating rhythms from whoever set up the Jira project. Stand-ups happen because agile says they should, not because someone designed the cadence around the team’s delivery milestones and the bank’s governance requirements. Documenting the operating model takes half a day. The return is measured in months of reduced friction.
4. BFSI technology roles through a governance lens
When we deploy technology teams for BFSI programs, the role list looks similar to what any staffing firm would provide. The difference is what we add around each role.
| Role | What staffing firms provide | What governed deployment adds |
|---|---|---|
| Kafka / streaming engineer | Technical skill validation, CV screening | Assessment on PCI-DSS data handling, encryption-in-transit standards, bank-specific messaging protocols |
| Data engineer (Databricks/Spark) | Platform certification, coding assessment | Training on PII masking requirements, data lineage documentation standards, regulatory data retention policies |
| Cloud architect (AWS/Azure) | Cloud certification, architecture review | Onboarding on bank’s security baseline, change management process, multi-cloud governance standards |
| Full stack developer | Language proficiency, portfolio review | SOP training on deployment protocols, code review standards, security review checklist integration |
| QA / test engineer | Tool proficiency (Selenium, TOSCA) | Training on regulatory test coverage requirements, compliance scenario testing, audit-evidence documentation |
| DevOps / SRE | CI/CD experience, platform knowledge | Onboarding on bank’s CAB process, incident response protocols, change control documentation requirements |
The left column is what gets a person employed. The right column is what makes them effective inside a regulated financial environment. Staffing firms fill the left column because that is what they are paid to do. The right column is empty unless someone fills it deliberately.
5. What this looks like in practice
A core banking modernization program
A system integrator was delivering a core banking modernization for a mid-size private bank. The program needed 18 engineers across Java development, API integration, and data migration. The SI filled the roles through its staffing partners over eight weeks. By week twelve, the program was behind schedule. Not because the engineers lacked skill, but because every deployment was failing the bank’s change advisory board review. The engineers were writing good code that did not meet the bank’s documentation and testing standards for production deployment.
We replaced the staffing-first approach with a governed deployment. Same roles. Different preparation. Each engineer completed a three-day onboarding covering the bank’s change management process, security review requirements, and documentation standards. A quality checkpoint was added inside the team, before code reached the CAB. The CAB rejection rate dropped from 40% to under 5% within two sprint cycles. The program recovered its schedule within a month.
An insurance data platform build
An insurance company was building a data platform to consolidate claims, policy, and underwriting data for analytics. The team, sourced through a staffing partner, had six data engineers with strong Databricks and PySpark skills. The platform was technically sound. The first time the compliance team reviewed it, they found that policyholder PII was flowing unmasked through three pipeline stages, that data retention was not enforced, and that there was no lineage documentation for regulatory reporting purposes.
The fix was not to replace the engineers. It was to add a data governance lead (a role the original staffing plan did not include) and to implement a governed quality checkpoint at each pipeline stage. We trained the existing team on the insurer’s data handling policies and established documentation protocols that satisfied both the compliance team and the regulator. The platform passed its next compliance review without findings.
A payment infrastructure scale-up
A fintech company processing UPI transactions needed to scale its platform engineering team from four to fourteen people in six weeks. Speed was the priority. The initial approach was to hire ten engineers as fast as possible through multiple staffing channels.
We pushed back on the speed-at-any-cost approach. Instead, we designed the team first: mapping the fourteen roles against the platform’s architecture, identifying which roles required PCI-DSS awareness, which needed experience with high-availability payment systems, and which could be more general platform engineers. We deployed the team in four waves over five weeks, with each wave completing a two-day governed onboarding before starting work. The platform maintained its uptime SLA throughout the scale-up. The CTO told us afterward that previous hiring sprints had always introduced a reliability dip during onboarding. This one did not.
6. The cost of ungoverned BFSI delivery
Governance adds cost. There is no point pretending otherwise. Competency assessments, SOP-governed onboarding, quality checkpoints, and documented operating models all take time and money that a hire-and-deploy staffing model does not incur.
The question is whether that cost is less than the cost of not doing it. Our experience says it is, by a wide margin.
| Cost category | Without governance | With governance |
|---|---|---|
| CAB rejections / rework | 30–40% of deployments rejected on first submission | Under 5% rejection rate after governed onboarding |
| Security review failures | Frequent; each failure adds 1–2 weeks | Rare; team trained on security baseline before starting |
| Compliance findings | Discovered late, expensive to remediate | Prevented by design through quality checkpoints |
| Onboarding to productivity | 4–8 weeks of informal ramp-up | 2–3 weeks with structured, governed onboarding |
| Attrition replacement cost | Full ramp-up repeated for each replacement | Documented operating model reduces ramp-up to 1–2 weeks |
| Audit preparation | Scramble to assemble documentation retroactively | Documentation maintained continuously; audit-ready at all times |
The governed model costs more on day one. By week eight, it costs less. By month six, the difference is substantial. In a regulated environment where a single compliance finding can trigger a remediation program costing more than the entire team’s annual compensation, the return on governance investment is asymmetric: small upfront cost, large downside protection.
7. Who this paper is for, and what to do next
If you lead technology delivery for a bank, insurer, NBFC, or fintech, and any of the following sounds familiar, this paper was written for you:
- Your CAB rejection rate is above 20% and you attribute it to process overhead rather than team readiness.
- Your data pipelines keep failing compliance review because the engineers did not know the data handling requirements when they built them.
- You have scaled your team in the past year but productivity has not scaled proportionally, and you suspect team design is part of the problem.
- You rely on staffing partners who fill roles but do not govern delivery, and you are absorbing the governance burden internally without the capacity to sustain it.
- You are planning a technology program that will involve regulated data (customer PII, transaction records, policyholder information) and you want the team to be governed from day one rather than remediated after the first audit finding.
The adjustment is not dramatic. It does not require replacing your staffing partners, restructuring your delivery organization, or adopting a new methodology. It requires adding a governance layer to your team deployment process: competency validation specific to your regulatory context, SOP-governed onboarding, quality checkpoints inside the team, and a documented operating model.
These are practices that pharmaceutical delivery teams have used for decades. They work in pharma because the regulatory environment demands them. They work in BFSI for the same reason. The only difference is that BFSI has not yet made them standard practice for technology teams.
SIRO exists at this intersection. We built our governance model in pharma, where the cost of ungoverned delivery is measured in audit findings, regulatory delays, and patient safety risk. We now deploy that same model for BFSI technology programs, where the cost is measured in compliance failures, rework cycles, and regulatory exposure. The risks are expressed differently. The discipline that prevents them is the same.
About SIRO Functional Services
SIRO Functional Services deploys structured capability teams across technology, data, platforms, and regulated environments for complex enterprises. We have operated in highly regulated industries for over two decades, where governance is the operating system, not a slide in the onboarding deck.
We work with EOR providers. We are not one. Our job starts where theirs ends: designing teams, validating competencies, establishing operating models, deploying governance frameworks, and managing delivery quality. Our clients are system integrators, CROs, pharmaceutical sponsors, enterprise data leaders, and companies scaling globally.
If you are deploying a team through EOR and wondering who fills the capability layer, that is the conversation we are built for.
For more information, visit www.sirofsp.com or contact bd@siroclinpharm.com.
References
- People Matters. “BFSI Talent & Tech Summit 2026: Capability is the New Currency.” People Matters, Mumbai, June 2026.
- Taggd / India Decoding Jobs. “India Decoding Jobs Report 2026: BFSI Sector.” Taggd, March 2026.
- Han Digital Solution. “India IT-BPM Talent Intelligence Report 2026.” Han Digital Solution / Express Computer, May 2026.
- Gartner. “IT Spending Forecast: BFSI Sector 2025–2027.” Gartner Research, 2025.
- Business Research Insights. “BFSI Security Market Size and Forecast, 2026–2034.” BRI Market Report, 2026.
- Tech Mahindra. “Why Solutions Aggregators Are Redefining BFSI Outsourcing.” Tech Mahindra Insights, February 2026.
- Reserve Bank of India. “Guidelines on Outsourcing of Financial Services.” RBI Master Circular, updated 2025.
- IRDAI. “Guidelines on Outsourcing of Activities by Insurance Companies.” Insurance Regulatory and Development Authority of India, 2024.
- PCI Security Standards Council. “PCI DSS v4.0.1: Payment Card Industry Data Security Standard.” PCI SSC, 2024.
- TeamLease. “Cost-Effective Strategies in BFSI Recruitment.” TeamLease Group, July 2025.
- ICH. “ICH E6(R2) Guideline for Good Clinical Practice.” International Council for Harmonisation, 2016.
- FDA. “21 CFR Part 11: Electronic Records; Electronic Signatures.” U.S. Food and Drug Administration.
About SIRO FSP
SIRO Functional Services (FSP) deploys structured capability teams across technology, data, platforms, and regulated environments for complex enterprises. With over two decades of experience operating in highly regulated industries where precision, compliance, and accountability are non-negotiable, SIRO brings healthcare-grade governance to enterprise-scale delivery.
SIRO serves system integrators, CROs and pharmaceutical sponsors, enterprise technology leaders, and organizations scaling globally. Our model is built on structured team deployment, not transactional staffing , enabling clients to access pre-composed, governed teams with the speed and discipline their programs demand.
Core Capabilities:
- Data Platform & AI Enablement Teams
- Cloud & Platform Engineering Teams
- Enterprise Systems Teams
- Life Sciences & Regulated Domain Teams
For more information, visit www.sirofsp.com or contact our team at fsp@siroclinpharm.com to discuss your capability deployment needs.