Financial Data, Infrastructure & Technology

Helping financial data, infrastructure, and technology (FDIT) providers achieve sustained growth and performance, transform their operating model, and enhance their resiliency

What we do

Our practitioners are recognized thought leaders in this field and bring a wide breadth of financial services and technology expertise to support our clients by addressing topics such as:

Business builds

We partner with clients to define and execute initiatives for growth in existing business lines, as well as to launch and scale new businesses like data and analytics, digital assets, and private markets through our proven and unique Leap by McKinsey methodology.

Inorganic growth

We support clients across the M&A deal cycle, including the identification of strategically attractive acquisition opportunities, commercial due diligences on potential targets for both bolt-on and transformative acquisitions, and post-merger integration and planning.

Operating model transformations

We help blueprint new operating models and implement organizational changes to enhance sales effectiveness, become product driven, and innovate with data and technology at the speed of native technology firms.

Large-scale technology value assurance

We assist organizations as they derisk and accelerate their delivery of large-scale technology programs through the adoption of agile methodologies, proven value-assurance techniques, and rigorous project management.

Technology modernizations

We help companies define their future-state functional and technical architecture by means of embracing microservices, APIs, and cloud first, and plan and execute practical transformations from current to target states.

Risk & Resiliency

We work with our clients on non-financial risks (e.g., operational, cyber and technology as well as ESG) and financial risks. With our Risk Dynamics, we support clients in various areas of their financial risk framework (including liquidity risk and stress testing, valuation and margining), model risk management (including model governance, development, and validation standards), and model validation of pricing and risk models (including model review and benchmarking).