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
| # | Firm | Evidence score | Industry evidence | Headquarters |
|---|---|---|---|---|
| 1 | DataArt Technology consultancy | 90/100 |
| New York, New York, USA |
| 2 | N-iX Engineering partner | 89/100 |
| Valletta, Malta |
| 3 | NTT DATA Global systems integrator | 88/100 |
| Tokyo, Japan |
| 4 | Slalom Technology consultancy | 87/100 |
| Seattle, Washington, USA |
| 5 | Tiger Analytics Data & AI specialist | 87/100 |
| Santa Clara, California, USA |
| 6 | Protiviti Technology consultancy | 84/100 |
| Menlo Park, California, USA |
| 7 | Wipro Global systems integrator | 84/100 | Bengaluru, Karnataka, India | |
| 8 | Xebia Technology consultancy | 84/100 |
| Atlanta, Georgia, USA |
| 9 | HCLTech Global systems integrator | 82/100 |
| Noida, India |
| 10 | Infosys Global systems integrator | 81/100 |
| Bengaluru, Karnataka, India |
| 11 | phData Data & AI specialist | 80/100 |
| Minneapolis, Minnesota, USA |
| 12 | CGI Global systems integrator | 79/100 |
| Montreal, Quebec, Canada |
| 13 | Tata Consultancy Services Global systems integrator | 78/100 | Mumbai, Maharashtra, India | |
| 14 | STX Next Engineering partner | 77/100 |
| Poznan, Poland |
| 15 | Accenture Global systems integrator | 76/100 |
| Dublin, Ireland |
| 16 | Lovelytics Data & AI specialist | 76/100 |
| Arlington, Virginia, USA |
| 17 | Tredence Data & AI specialist | 76/100 |
| San Jose, California, USA |
| 18 | Avenga Engineering partner | 75/100 |
| Prague, Czech Republic |
| 19 | Analytics8 Data & AI specialist | 74/100 |
| Chicago, Illinois, USA |
| 20 | Quantiphi Data & AI specialist | 74/100 |
| Marlborough, Massachusetts, USA |
| 21 | LatentView Analytics Data & AI specialist | 73/100 |
| Chennai, Tamil Nadu, India |
| 22 | Thoughtworks Technology consultancy | 73/100 |
| Chicago, Illinois, USA |
| 23 | Capgemini Global systems integrator | 72/100 |
| Paris, France |
| 24 | Deloitte Big Four | 72/100 |
| London, United Kingdom |
| 25 | EPAM Systems Technology consultancy | 72/100 |
| Newtown, Pennsylvania, USA |
| 26 | Fractal Data & AI specialist | 72/100 |
| Mumbai, Maharashtra, India |
| 27 | IBM Consulting Global systems integrator | 71/100 |
| Armonk, New York, USA |
| 28 | ZS Data & AI specialist | 70/100 |
| Evanston, Illinois, USA |
| 29 | Grid Dynamics Technology consultancy | 69/100 |
| San Ramon, California, USA |
| 30 | Algoscale Data & AI specialist | 68/100 |
| Newark, New Jersey, USA |
| 31 | Avanade Technology consultancy | 68/100 |
| Seattle, Washington, USA |
| 32 | Cognizant Global systems integrator | 67/100 |
| Teaneck, New Jersey, USA |
| 33 | EY Big Four | 67/100 |
| London, United Kingdom |
| 34 | ScienceSoft Engineering partner | 67/100 |
| McKinney, Texas, USA |
| 35 | Tech Mahindra Global systems integrator | 67/100 |
| Pune, India |
| 36 | Data Reply Data & AI specialist | 64/100 |
| Italy |
| 37 | InData Labs Data & AI specialist | 63/100 |
| Cyprus |
| 38 | Innowise Engineering partner | 63/100 |
| Warsaw, Poland |
| 39 | Vention Engineering partner | 63/100 |
| New York, New York, USA |
| 40 | Binariks Engineering partner | 62/100 | Torrance, California, USA | |
| 41 | Future Processing Engineering partner | 60/100 | Gliwice, Poland | |
| 42 | Mu Sigma Data & AI specialist | 57/100 |
| Austin, Texas, USA |
| 43 | RTS Labs Engineering partner | 57/100 |
| Richmond, Virginia, USA |
| 44 | BCG X Strategy consultancy | 54/100 |
| Boston, Massachusetts, USA |
| 45 | Datatonic Data & AI specialist | 51/100 |
| London, United Kingdom |
| 46 | QuantumBlack, AI by McKinsey Strategy consultancy | 48/100 |
| No single headquarters |
| — | Palantir Technologies Platform vendor servicesNot ranked | 61/100 |
| Miami, Florida, USA |
| — | Databricks Forward Deployed Engineering Platform vendor servicesNot ranked | 50/100 |
| San Francisco, California, USA |
- 190/100DataArtTechnology consultancy
- Industry page
- Headquarters
- New York, New York, USA
- 289/100N-iXEngineering partner
- Industry page
- Headquarters
- Valletta, Malta
- 388/100NTT DATAGlobal systems integrator
- Industry page
- Headquarters
- Tokyo, Japan
- 487/100SlalomTechnology consultancy
- Industry page
- Headquarters
- Seattle, Washington, USA
- 587/100Tiger AnalyticsData & AI specialist
- Industry page
- Headquarters
- Santa Clara, California, USA
- 684/100ProtivitiTechnology consultancy
- Industry page
- Headquarters
- Menlo Park, California, USA
- 784/100WiproGlobal systems integrator
- 884/100XebiaTechnology consultancy
- Industry page
- Headquarters
- Atlanta, Georgia, USA
- 982/100HCLTechGlobal systems integrator
- Industry page
- Headquarters
- Noida, India
- 1081/100InfosysGlobal systems integrator
- Industry page
- Headquarters
- Bengaluru, Karnataka, India
- 1180/100phDataData & AI specialist
- Industry page
- Headquarters
- Minneapolis, Minnesota, USA
- 1279/100CGIGlobal systems integrator
- Industry page
- Headquarters
- Montreal, Quebec, Canada
- 1378/100Tata Consultancy ServicesGlobal systems integrator
- 1477/100STX NextEngineering partner
- Industry page
- Headquarters
- Poznan, Poland
- 1576/100AccentureGlobal systems integrator
- Industry page
- Headquarters
- Dublin, Ireland
- 1676/100LovelyticsData & AI specialist
- Industry page
- Headquarters
- Arlington, Virginia, USA
- 1776/100TredenceData & AI specialist
- Industry page
- Headquarters
- San Jose, California, USA
- 1875/100AvengaEngineering partner
- Industry page
- Headquarters
- Prague, Czech Republic
- 1974/100Analytics8Data & AI specialist
- Industry page
- Headquarters
- Chicago, Illinois, USA
- 2074/100QuantiphiData & AI specialist
- Industry page
- Headquarters
- Marlborough, Massachusetts, USA
- 2173/100LatentView AnalyticsData & AI specialist
- Industry page
- Headquarters
- Chennai, Tamil Nadu, India
- 2273/100ThoughtworksTechnology consultancy
- Industry page
- Headquarters
- Chicago, Illinois, USA
- 2372/100CapgeminiGlobal systems integrator
- Industry page
- Headquarters
- Paris, France
- 2472/100DeloitteBig Four
- Industry page
- Headquarters
- London, United Kingdom
- 2572/100EPAM SystemsTechnology consultancy
- Industry page
- Headquarters
- Newtown, Pennsylvania, USA
- 2672/100FractalData & AI specialist
- Industry page
- Headquarters
- Mumbai, Maharashtra, India
- 2771/100IBM ConsultingGlobal systems integrator
- Industry page
- Headquarters
- Armonk, New York, USA
- 2870/100ZSData & AI specialist
- Industry page
- Headquarters
- Evanston, Illinois, USA
- 2969/100Grid DynamicsTechnology consultancy
- Industry page
- Headquarters
- San Ramon, California, USA
- 3068/100AlgoscaleData & AI specialist
- Industry page
- Headquarters
- Newark, New Jersey, USA
- 3168/100AvanadeTechnology consultancy
- Industry page
- Headquarters
- Seattle, Washington, USA
- 3267/100CognizantGlobal systems integrator
- Industry page
- Headquarters
- Teaneck, New Jersey, USA
- 3367/100EYBig Four
- Industry page
- Headquarters
- London, United Kingdom
- 3467/100ScienceSoftEngineering partner
- Industry page
- Headquarters
- McKinney, Texas, USA
- 3567/100Tech MahindraGlobal systems integrator
- Industry page
- Headquarters
- Pune, India
- 3664/100Data ReplyData & AI specialist
- Case study
- Headquarters
- Italy
- 3763/100InData LabsData & AI specialist
- Industry page
- Headquarters
- Cyprus
- 3863/100InnowiseEngineering partner
- Industry page
- Headquarters
- Warsaw, Poland
- 3963/100VentionEngineering partner
- Industry page
- Headquarters
- New York, New York, USA
- 4062/100BinariksEngineering partner
- 4160/100Future ProcessingEngineering partner
- 4257/100Mu SigmaData & AI specialist
- Industry page
- Headquarters
- Austin, Texas, USA
- 4357/100RTS LabsEngineering partner
- Industry page
- Headquarters
- Richmond, Virginia, USA
- 4454/100BCG XStrategy consultancy
- Industry page
- Headquarters
- Boston, Massachusetts, USA
- 4551/100DatatonicData & AI specialist
- Industry page
- Headquarters
- London, United Kingdom
- 4648/100QuantumBlack, AI by McKinseyStrategy consultancy
- Industry page
- Headquarters
- No single headquarters
- —61/100Palantir TechnologiesPlatform vendor servicesNot ranked
- Industry page
- Headquarters
- Miami, Florida, USA
- —50/100Databricks Forward Deployed EngineeringPlatform vendor servicesNot ranked
- Industry page
- 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.
| Project | Typical data | What makes it hard |
|---|---|---|
| Risk data aggregation and regulatory reporting | Positions, exposures, counterparties, general ledger | Lineage from source system to report, reconciliation, and producing figures quickly under stress |
| Fraud and anti-money-laundering (AML) monitoring | Transactions, payments, customer and device data | Latency, false positives, and explaining alerts to investigators and supervisors |
| Credit, pricing and underwriting models | Applications, bureau data, repayment and claims history | Documentation and independent validation under model risk management |
| Customer analytics and personalization | CRM, product holdings, digital events | Consent, privacy and data residency rules |
| Core system and warehouse migration | Core banking, policy administration, legacy warehouses | Parallel runs and reconciliation before anyone relies on the new numbers |
| Payments and card analytics | Card and account transactions | Every 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.
| Rule | Who it applies to | What 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 designation | Fourteen 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, 2025 | EU financial entities, and through their contracts the providers of their ICT services | Firms 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 v4.0.1 | Entities that store, process or transmit cardholder data, or could affect the security of the cardholder data environment | v4.0.1 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 assets | Supersedes 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 Rule | Financial institutions under FTC jurisdiction, such as mortgage lenders and brokers, payday lenders and finance companies | Encrypt 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.
- 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?
- 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?
- 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?
- 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?
- 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?
- 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.
- Discovery. Map sources, critical data elements, owners and report consumers; agree the contract terms that DORA or your vendor-risk policy require.
- Build. Ingestion, data quality rules, lineage capture and access controls, with reconciliation checks written alongside the pipelines rather than after them.
- Parallel run. Produce old and new figures side by side until differences are explained and signed off.
- Validation and sign-off. Model validation for any credit, fraud or AML model; internal audit or risk review for regulatory reporting.
- 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
- Principles for effective risk data aggregation and risk reporting (BCBS 239) — Basel Committee on Banking Supervision, Bank for International Settlements, 2013bis.org
- Progress in adopting the Principles for effective risk data aggregation and risk reporting — Basel Committee on Banking Supervision, Bank for International Settlements, 2023bis.org
- 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
- PCI Data Security Standard (PCI DSS) — PCI Security Standards Council, 2024pcisecuritystandards.org
- Just Published: PCI DSS v4.0.1 — PCI Security Standards Council, 2024blog.pcisecuritystandards.org
- Now is the Time for Organizations to Adopt the Future-Dated Requirements of PCI DSS v4.x — PCI Security Standards Council, 2024blog.pcisecuritystandards.org
- SR 26-2: Revised Guidance on Model Risk Management — Board of Governors of the Federal Reserve System, 2026federalreserve.gov
- Revised Guidance on Model Risk Management (interagency guidance, attachment to SR 26-2) — Federal Reserve, OCC and FDIC, 2026federalreserve.gov
- FTC Safeguards Rule: What Your Business Needs to Know — Federal Trade Commissionftc.gov
- CALC+ Quick Rate: Hourly Labor Ceiling Rates (accessed October 23, 2026) — U.S. General Services Administration, 2026buy.gsa.gov
- Databricks partner directory — Databricks, 2026databricks.com
Related directory pages
By capability