DCF Research

Directory · Financial services

Financial Services Data Consulting Firms, Ranked by Public Evidence

Of the 50 data and AI consulting firms DCF Research tracks, 48 show public evidence of financial services work: a dedicated banking, insurance, capital markets or payments page, a published case study in the sector, or a Databricks partner listing for Financial Services. Among them, the highest overall DCF Evidence Scores belong to DataArt, N-iX and NTT DATA. The list shows visible sector experience, not regulatory competence or delivery quality, so test shortlisted firms against the rules that apply to your data.

Edited by Peter Korpak·Evidence checked ·How we score

Qualifying firms, ranked by evidence score

48 of 50 tracked firms · evidence checked October 23, 2026

  1. 1
    DataArt
    Technology consultancy
    90/100
    Industry page
    Headquarters
    New York, New York, USA
  2. 2
    N-iX
    Engineering partner
    89/100
    Industry page
    Headquarters
    Valletta, Malta
  3. 3
    NTT DATA
    Global systems integrator
    88/100
    Headquarters
    Tokyo, Japan
  4. 4
    Slalom
    Technology consultancy
    87/100
    Headquarters
    Seattle, Washington, USA
  5. 5
    Tiger Analytics
    Data & AI specialist
    87/100
    Headquarters
    Santa Clara, California, USA
  6. 6
    Protiviti
    Technology consultancy
    84/100
    Headquarters
    Menlo Park, California, USA
  7. 7
    Wipro
    Global systems integrator
    84/100
    Headquarters
    Bengaluru, Karnataka, India
  8. 8
    Xebia
    Technology consultancy
    84/100
    Headquarters
    Atlanta, Georgia, USA
  9. 9
    HCLTech
    Global systems integrator
    82/100
    Headquarters
    Noida, India
  10. 10
    Infosys
    Global systems integrator
    81/100
    Headquarters
    Bengaluru, Karnataka, India
  11. 11
    phData
    Data & AI specialist
    80/100
    Headquarters
    Minneapolis, Minnesota, USA
  12. 12
    CGI
    Global systems integrator
    79/100
    Headquarters
    Montreal, Quebec, Canada
  13. 13
    Tata Consultancy Services
    Global systems integrator
    78/100
    Headquarters
    Mumbai, Maharashtra, India
  14. 14
    STX Next
    Engineering partner
    77/100
    Headquarters
    Poznan, Poland
  15. 15
    Accenture
    Global systems integrator
    76/100
    Industry page
    Headquarters
    Dublin, Ireland
  16. 16
    Lovelytics
    Data & AI specialist
    76/100
    Headquarters
    Arlington, Virginia, USA
  17. 17
    Tredence
    Data & AI specialist
    76/100
    Headquarters
    San Jose, California, USA
  18. 18
    Avenga
    Engineering partner
    75/100
    Headquarters
    Prague, Czech Republic
  19. 19
    Analytics8
    Data & AI specialist
    74/100
    Headquarters
    Chicago, Illinois, USA
  20. 20
    Quantiphi
    Data & AI specialist
    74/100
    Headquarters
    Marlborough, Massachusetts, USA
  21. 21
    LatentView Analytics
    Data & AI specialist
    73/100
    Headquarters
    Chennai, Tamil Nadu, India
  22. 22
    Thoughtworks
    Technology consultancy
    73/100
    Headquarters
    Chicago, Illinois, USA
  23. 23
    Capgemini
    Global systems integrator
    72/100
    Headquarters
    Paris, France
  24. 24
    Deloitte
    Big Four
    72/100
    Headquarters
    London, United Kingdom
  25. 25
    EPAM Systems
    Technology consultancy
    72/100
    Headquarters
    Newtown, Pennsylvania, USA
  26. 26
    Fractal
    Data & AI specialist
    72/100
    Headquarters
    Mumbai, Maharashtra, India
  27. 27
    IBM Consulting
    Global systems integrator
    71/100
    Headquarters
    Armonk, New York, USA
  28. 28
    ZS
    Data & AI specialist
    70/100
    Headquarters
    Evanston, Illinois, USA
  29. 29
    Grid Dynamics
    Technology consultancy
    69/100
    Headquarters
    San Ramon, California, USA
  30. 30
    Algoscale
    Data & AI specialist
    68/100
    Headquarters
    Newark, New Jersey, USA
  31. 31
    Avanade
    Technology consultancy
    68/100
    Industry page
    Headquarters
    Seattle, Washington, USA
  32. 32
    Cognizant
    Global systems integrator
    67/100
    Industry page
    Headquarters
    Teaneck, New Jersey, USA
  33. 33
    EY
    Big Four
    67/100
    Headquarters
    London, United Kingdom
  34. 34
    ScienceSoft
    Engineering partner
    67/100
    Industry page
    Headquarters
    McKinney, Texas, USA
  35. 35
    Tech Mahindra
    Global systems integrator
    67/100
    Headquarters
    Pune, India
  36. 36
    Data Reply
    Data & AI specialist
    64/100
    Headquarters
    Italy
  37. 37
    InData Labs
    Data & AI specialist
    63/100
    Industry page
    Headquarters
    Cyprus
  38. 38
    Innowise
    Engineering partner
    63/100
    Industry page
    Headquarters
    Warsaw, Poland
  39. 39
    Vention
    Engineering partner
    63/100
    Industry page
    Headquarters
    New York, New York, USA
  40. 40
    Binariks
    Engineering partner
    62/100
    Headquarters
    Torrance, California, USA
  41. 41
    Future Processing
    Engineering partner
    60/100
    Headquarters
    Gliwice, Poland
  42. 42
    Mu Sigma
    Data & AI specialist
    57/100
    Headquarters
    Austin, Texas, USA
  43. 43
    RTS Labs
    Engineering partner
    57/100
    Headquarters
    Richmond, Virginia, USA
  44. 44
    BCG X
    Strategy consultancy
    54/100
    Headquarters
    Boston, Massachusetts, USA
  45. 45
    Datatonic
    Data & AI specialist
    51/100
    Headquarters
    London, United Kingdom
  46. 46
    QuantumBlack, AI by McKinsey
    Strategy consultancy
    48/100
    Headquarters
    No single headquarters
  47. —
    Palantir Technologies
    Platform vendor servicesNot ranked
    61/100
    Headquarters
    Miami, Florida, USA
  48. —
    Databricks Forward Deployed Engineering
    Platform vendor servicesNot ranked
    50/100
    Headquarters
    San Francisco, California, USA

Ranked by DCF Evidence Score (0–100), which counts verifiable public evidence and does not measure delivery quality. How we score. Every link opens a public page, checked October 23, 2026; outcomes described on those pages are the firms’ own claims.

Platform vendors’ own services teams are shown for comparison and are not ranked.

What does a financial services data consulting firm do?

It builds the data platforms and models that banks, insurers, asset managers and fintechs use for risk and regulatory reporting, fraud and anti-money-laundering monitoring, credit decisions and customer analytics, under supervisory rules on data quality, technology outsourcing and model risk that most other industries do not face.

ProjectTypical dataWhat makes it hard
Risk data aggregation and regulatory reportingPositions, exposures, counterparties, general ledgerLineage from source system to report, reconciliation, and producing figures quickly under stress
Fraud and anti-money-laundering (AML) monitoringTransactions, payments, customer and device dataLatency, false positives, and explaining alerts to investigators and supervisors
Credit, pricing and underwriting modelsApplications, bureau data, repayment and claims historyDocumentation and independent validation under model risk management
Customer analytics and personalizationCRM, product holdings, digital eventsConsent, privacy and data residency rules
Core system and warehouse migrationCore banking, policy administration, legacy warehousesParallel runs and reconciliation before anyone relies on the new numbers
Payments and card analyticsCard and account transactionsEvery system that stores, processes or transmits cardholder data falls under PCI DSS

For the engineering and platform side, see data engineering consulting, data governance consulting firms and platform modernization consulting.

Which regulations shape financial data projects?

Five sets of rules matter most for data projects: the Basel Committee’s BCBS 239 principles for risk data, the EU’s Digital Operational Resilience Act (DORA) for technology providers, PCI DSS for card data, US banking agencies’ model risk guidance, and the FTC Safeguards Rule for non-bank financial companies. Each changes what a consultant must build and document.

RuleWho it applies toWhat it means for a data project
BCBS 239, Principles for effective risk data aggregation and risk reporting (Basel Committee, January 2013)Global systemically important banks, expected to comply by January 2016; the Committee strongly suggested supervisors apply it to domestic systemically important banks three years after designationFourteen principles: eleven for banks on governance and infrastructure, data aggregation and risk reporting, three for supervisors. Principle 3 asks for data aggregated "on a largely automated basis" to minimize errors, a bar that manual, spreadsheet-based reporting chains struggle to meet
DORA, Regulation (EU) 2022/2554, applies from January 17, 2025EU financial entities, and through their contracts the providers of their ICT servicesFirms must keep a register of all contracts for information and communication technology (ICT) services from third parties (Article 28). Contracts must cover at least the services and any subcontracting, the countries where data is processed and stored, data protection, access to and return of data on exit, service levels and help during ICT incidents (Article 30(2)); for critical or important functions, also unrestricted audit rights and exit strategies with a transition period (Article 30(3))
PCI DSS v4.0.1Entities that store, process or transmit cardholder data, or could affect the security of the cardholder data environmentv4.0.1 has been the only active version since v4.0 was retired on December 31, 2024; 51 of the 64 requirements new in v4.0 took effect on March 31, 2025 (PCI SSC). Any warehouse or pipeline that holds card numbers is in scope
SR 26-2, Revised Guidance on Model Risk Management (Federal Reserve, OCC and FDIC, April 17, 2026)Expected to be most relevant to banking organizations with over $30 billion in total assetsSupersedes SR 11-7 from 2011 and sets a risk-based approach. Vendor and third-party models still need validation, including an understanding of their conceptual soundness, design, development data and performance (guidance)
FTC Safeguards RuleFinancial institutions under FTC jurisdiction, such as mortgage lenders and brokers, payday lenders and finance companiesEncrypt customer information at rest and in transit, require multi-factor authentication, oversee service providers by contract with periodic reassessment, and notify the FTC within 30 days of discovering a breach of 500 or more consumers' unencrypted information

DORA defines ICT services as "digital and data services provided through ICT systems" on an ongoing basis (Article 3(21)). Managed data platforms and ongoing support can fall inside that definition, so agree with your compliance team early whether an engagement counts, and give bidders the contract terms up front.

What should you ask a financial services data consultant?

Ask how they would trace a reported figure back to its source, how they reconcile a migration before cutover, what documentation their models come with for validation, which DORA or vendor-risk contract terms they already accept, and who on their team or among their subcontractors will see customer or card data, and from where.

  1. Lineage. Show us how a number in a regulatory or board report traces back to source systems in a platform you built. How do you capture lineage and data quality checks?
  2. Reconciliation. How do you prove a migrated warehouse produces the same figures as the old one, and for how long do you run both in parallel?
  3. Models. What documentation do you deliver for a credit, fraud or AML model so our validation team can assess conceptual soundness, development data and performance? Who owns the code and the training data afterwards?
  4. Contract terms. Will you accept our standard ICT third-party clauses, including audit rights, data location, exit assistance and incident support? Which subcontractors will you use?
  5. Data access. Who will see customer or card data, from which countries, and through which controls? Can the work be done on tokenized or masked data?
  6. Evidence. Which financial services case studies on your website did this team deliver, for what kind of institution, and can we speak to that client?

Our RFP template lets you send the same questions and scope to every shortlisted firm.

How are financial services data projects usually structured?

In regulated work the build is only part of the plan. Expect discovery and lineage mapping, a build with reconciliation designed in, a parallel run against the old figures, and model validation or internal audit review before anyone relies on the output. Budget time and money for each step, not just for engineering.

  1. Discovery. Map sources, critical data elements, owners and report consumers; agree the contract terms that DORA or your vendor-risk policy require.
  2. Build. Ingestion, data quality rules, lineage capture and access controls, with reconciliation checks written alongside the pipelines rather than after them.
  3. Parallel run. Produce old and new figures side by side until differences are explained and signed off.
  4. Validation and sign-off. Model validation for any credit, fraud or AML model; internal audit or risk review for regulatory reporting.
  5. Run. Monitoring, incident handling and the exit plan your contract requires.

The Basel Committee's 2023 review of BCBS 239 adoption is a useful warning about underestimating this work: of 31 global systemically important banks assessed, only two were fully compliant with all the principles, and supervisors found adoption programs often underfunded, limited in scope and short of board attention, with several banks failing to assess the complexity of legacy IT (BIS, 2023). For budgets, see the pricing page.

Frequently asked questions

Does BCBS 239 apply to my bank?

It was written for global systemically important banks, and the Basel Committee strongly suggested that supervisors also apply it to domestic systemically important banks. The Committee notes that not every jurisdiction has transposed the principles into national rules, though all support their adoption, so check what your supervisor expects.

Does DORA apply to a data consulting firm?

DORA’s obligations fall on EU financial entities, which must build its contract and register requirements into their arrangements with ICT third-party service providers. A consultant that provides ICT services, meaning digital and data services provided on an ongoing basis, should expect those contract terms from its financial services clients.

Is generative AI covered by US bank model risk guidance?

Not by the April 2026 interagency guidance. It states that generative and agentic AI models are outside its scope, while traditional statistical models and non-generative AI models remain covered, and it expects banks to govern other tools through their broader risk management and governance practices.

How much do financial services data consultants charge?

No public dataset isolates financial services rates. As a reference point, the median US federal GSA ceiling rate is $149 an hour for data engineers, $158 for data architects and $169 for data scientists (October 2026); these are maximum prices for federal buyers, and commercial quotes for the same roles are often higher.

Sources

  1. Principles for effective risk data aggregation and risk reporting (BCBS 239) — Basel Committee on Banking Supervision, Bank for International Settlements, 2013bis.org
  2. Progress in adopting the Principles for effective risk data aggregation and risk reporting — Basel Committee on Banking Supervision, Bank for International Settlements, 2023bis.org
  3. Regulation (EU) 2022/2554 on digital operational resilience for the financial sector (DORA) — EUR-Lex, Official Journal of the European Union, 2022eur-lex.europa.eu
  4. PCI Data Security Standard (PCI DSS) — PCI Security Standards Council, 2024pcisecuritystandards.org
  5. Just Published: PCI DSS v4.0.1 — PCI Security Standards Council, 2024blog.pcisecuritystandards.org
  6. Now is the Time for Organizations to Adopt the Future-Dated Requirements of PCI DSS v4.x — PCI Security Standards Council, 2024blog.pcisecuritystandards.org
  7. SR 26-2: Revised Guidance on Model Risk Management — Board of Governors of the Federal Reserve System, 2026federalreserve.gov
  8. Revised Guidance on Model Risk Management (interagency guidance, attachment to SR 26-2) — Federal Reserve, OCC and FDIC, 2026federalreserve.gov
  9. FTC Safeguards Rule: What Your Business Needs to Know — Federal Trade Commissionftc.gov
  10. CALC+ Quick Rate: Hourly Labor Ceiling Rates (accessed October 23, 2026) — U.S. General Services Administration, 2026buy.gsa.gov
  11. Databricks partner directory — Databricks, 2026databricks.com