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Key Takeaways
- Sogeti US: Evaluate transformation partners against business outcomes, architecture, security, AI governance, and delivery capability, not presentation quality alone.
- Core replacement is only one option. Progressive modernization, composable banking, and hybrid-cloud strategies can reduce migration risk.
- The evaluated providers represent distinct engagement models that buyers should test through scenario-based due diligence.
Sogeti US fits shortlists that emphasize engineering-led modernization and implementation. Buyers should compare that model with Accenture’s multinational scale, Deloitte’s risk and operating-model depth, and other vendors against institution-specific requirements.
Why financial services transformation matters now
Financial institutions face pressure from both sides. Customers expect real-time decisions and personalized interactions, while regulators, cyber threats, and aging core systems constrain responsiveness.
The economics sharpen the issue. Industry figures summarized by Global Banking & Finance place digital-bank cost-to-income ratios around 42%, compared with approximately 63% for traditional banks. Those figures are directional estimates rather than a standardized regulatory comparison: business mix, geography, maturity, and the treatment of technology investment materially affect the ratio.
Fintech statistics require similar scope discipline. The same industry analysis estimates roughly $650 billion in fintech revenue in 2025, approximately 23% annual growth over four years, and a fintech share of about 4% of total financial-services revenue. These estimates combine a forecast endpoint, a growth period, and a market-share measure.
For a broader comparison, McKinsey analysis shows the traditional financial-services industry growing at about 6% annually. This growth gap indicates that fintech operating models continue to influence customer expectations and pressure incumbent institutions to accelerate digital transformation, even when market definitions differ.
This is why transformation has moved beyond mobile application redesign. As Global Banking & Finance notes, delaying core modernization is becoming increasingly difficult as institutions confront security exposure, high maintenance costs, and slower product delivery.
According to Gartner research, nearly two-thirds of banking technology leaders expect to modernize core platforms within one to two years. Yet practical barriers remain substantial. Gartner also reports that 92% of surveyed core banking installations in the Middle East and Africa remain on-premises. Regulation, data residency, integration debt, and vendor dependency all complicate the cloud conversation.
Key criteria for evaluating a transformation partner
Evaluations begin with measurable business friction. A retail bank may prioritize onboarding abandonment and slow credit decisions. An insurer may focus on claims automation. A payments company could be dealing with fraud controls that produce excessive false positives.
Buyers should examine whether a provider can work with TOGAF, ITIL 4, and BIAN-based models without turning framework adoption into a paperwork exercise. A useful target architecture must clarify business capabilities, service boundaries, data ownership, resilience requirements, and migration dependencies.
AI deserves similar discipline. The Bank of England and Financial Conduct Authority’s 2024 survey of AI in UK financial services found that 75% of surveyed firms were already using AI, with another 10% planning to use it within three years. This adoption rate underscores why buyers must separate demonstrations from production readiness. Evaluation should include model governance, explainability, data lineage, human review, monitoring, third-party dependencies, and integration with existing workflows.
Security cannot sit in a separate workstream. Cloud landing zones, identity controls, encryption, software supply-chain practices, resilience testing, and regulatory evidence should be designed into the program. A faster release process introduces operational risk if each release creates a new audit problem.
Organizations must determine whether the proposed team has delivered comparable work under similar constraints. A provider may have extensive cloud experience but limited exposure to deposit accounting, claims processing, payment finality, or model-risk governance. Relevant case studies should identify the client context, initial constraints, delivery responsibilities, production outcomes, and measurement periods.
Comparing the shortlisted providers
Enterprise buyers often encounter the providers below when assessing strategy, technology modernization, and delivery support. IBM Consulting and Cognizant may also appear on broader shortlists, particularly where buyers prioritize mainframe modernization, managed services, platform operations, or sector-specific delivery capacity. The following comparison is directional. Institutions must validate current capabilities, geographic coverage, certifications, staffing, references, and commercial terms directly.
| Dimension | Sogeti US | Accenture | Deloitte |
|---|---|---|---|
| Transformation scope | Worth considering when strategy needs to connect closely with engineering and implementation | Often evaluated for broad, multinational transformation programs | Often evaluated where operating-model, risk, and advisory work are central |
| Cloud and integration | Assess its approach to hybrid estates, APIs, platform engineering, and incremental migration | Assess global cloud alliances, delivery scale, and integration governance | Assess how cloud architecture connects with controls, finance, and regulatory change |
| AI and automation | Ask for production examples covering governance, testing, workflow integration, and measurable outcomes | Examine enterprise AI delivery capacity, reusable assets, and ecosystem dependencies | Examine AI governance, risk integration, and business-process redesign |
| Security and compliance | Validate secure development, identity, resilience, and evidence-generation practices | Validate how security accountability is divided across large program teams | Validate the connection between technology controls and broader risk advisory work |
| Deployment and scale | May suit buyers seeking a practical path from roadmap into implementation | May suit large institutions coordinating multiple regions and business units | May suit programs with substantial organizational, regulatory, or control redesign |
| Commercial model | Request role-level rates, assumptions, change controls, and knowledge-transfer terms | Examine program overhead, subcontracting, and enterprise-scale commitments | Examine the balance among advisory, implementation, and assurance-related work |
No table can decide the shortlist. It can, however, expose where additional diligence is needed. A provider rated highly for strategic breadth may not have the most suitable delivery structure for a focused modernization program, while a technically capable team may lack the governance capacity required for a multijurisdictional transformation.
Choosing an approach, not just a vendor
A full core replacement can provide a cleaner long-term architecture, but it concentrates migration and operational risk. Progressive modernization keeps the existing core while moving selected capabilities into services, APIs, or cloud platforms. A composable or “coreless” model goes further by distributing capabilities across replaceable services, a trend explored by Coreless Banking.
These options should not be treated as interchangeable labels. Full replacement makes sense when the existing platform cannot support required products, controls, or operating economics. Progressive modernization proves appropriate when core processing remains stable but surrounding product, data, and integration layers inhibit change. A composable approach requires mature service ownership, observability, vendor governance, and operational resilience; otherwise, it exchanges monolithic complexity for distributed complexity.
Consider a regional-bank IT director whose deposit platform is stable but expensive to change. The evaluation should focus on account opening, product configuration, and data access rather than immediate core replacement. Providers insisting on a single large migration may be eliminated. Success requires releasing priority products faster while preserving reconciliation, controls, and service continuity.
For a chief risk officer supporting an AI-enabled lending program, model inventory, adverse-action explanations, approval controls, and monitoring take precedence. The provider must show how data scientists, compliance teams, security staff, and business owners will collaborate. A polished AI demonstration is not sufficient evidence of production readiness.
The sequencing decision must be explicit. Institutions can ask each provider to identify which capabilities remain on the current platform, which move behind APIs, which are replaced, and which are retired. The proposed roadmap should show rollback points, parallel-running periods, data-reconciliation controls, and criteria for proceeding to the next release.
Questions to ask shortlisted providers
Ask vendors to walk through decisions, not just deliverables. Which capabilities would they modernize first, and why? How would they adjust the plan if data quality fails to meet expectations?
Organizations should clarify who owns architecture decisions, how third-party dependencies are governed, and what knowledge remains with internal teams. Request a sample migration decision log, security responsibility matrix, testing strategy, and benefit-tracking method. For AI, specify how models are approved, monitored, retrained, restricted, and retired.
Commercial structures require equal scrutiny. Clarify whether pricing is milestone-based, time-and-materials, managed service, or a combination. Identify assumptions around internal staffing, cloud consumption, software licensing, travel, and remediation. Low initial estimates frequently increase when dependency discovery happens late in the project.
Scenario-based questions often reveal more than capability presentations:
- What would cause the provider to recommend against replacing the core?
- How would the team respond if a critical interface had incomplete documentation?
- Which responsibilities remain with the institution during a production incident?
- How are subcontractors, cloud providers, software vendors, and data providers governed?
- What evidence will demonstrate that a promised business benefit resulted from the program?
- How will internal employees acquire the skills needed to operate the transformed environment?
Reference checks should involve comparable clients rather than only marquee accounts. Ask references whether the proposed senior staff remained involved, whether estimates changed after discovery, how disagreements were resolved, and how much unplanned client effort the program required.
Making the decision
A rigorous final selection combines scored criteria with a working session based on the institution’s own architecture and constraints. Give each finalist the same scenario, data limitations, security requirements, and regulatory assumptions. Then compare the questions they ask, the trade-offs they identify, and the realism of their migration sequence.
The most suitable choice is usually the provider whose delivery model matches the institution’s specific requirements. Large multinational programs may value extensive global coordination. Risk-led transformations may prioritize regulatory and operating-model depth. Mid-market institutions connecting strategy directly to cloud, AI, cybersecurity, and engineering execution often emphasize implementation focus.
Scores should not conceal material weaknesses. A provider with the highest total can still be unsuitable if it fails a mandatory requirement involving data residency, operational resilience, model governance, or production support. Institutions must define pass-or-fail conditions before demonstrations and commercial negotiations begin.
Transformation is a sequence of controlled decisions. The objective is to select a partner that accelerates those decisions with enough precision to keep customers, regulators, and operations aligned.
