We solve problems banks can't solve alone

Specialist expertise for the most complex challenges in capital markets.

Beltis helps financial institutions diagnose complex problems, build new capabilities, and create sustainable solutions combining deep capital-markets expertise, technology, and AI.

Our objective is not to create permanent dependency… It is to leave clients stronger.

Explore our Capabilities
Explore our Capabilities
Start a Conversation

Challenges We Solve

  • Beltis can assess processes, technology, organisational structure, and external expenditure to identify where work should be simplified, automated, internalised, or externally supported.

  • We identify high-value applications, establish baselines, quantify expected benefits, and measure whether AI adoption subsequently delivers them.

  • We provide targeted specialist capability to solve the immediate problem while determining whether that capability should subsequently be transferred internally.

  • We help institutions assess platforms, data architecture, semantic structures, integration, and operating models and translate strategy into executable transformation.

  • We provide specialist independent review, model validation, benchmarking, and assurance where objectivity and deep domain expertise matter.

What We Do

Advisory

Independent expertise for strategic capital-markets decisions.

We help clients diagnose problems, evaluate options and make decisions across technology, quantitative finance, operating models, model risk, AI and transformation.

Solutions

Specialist teams delivering clearly defined outcomes.

Where the problem requires implementation, we assemble the expertise required to design and deliver the solution, increasingly using fixed-scope and outcome-oriented delivery models.

“We build capability with our clients, embed it within their organisation and transfer ownership once it is sustainable.”

Capabilities & Expertise

Capital Markets Advisory & Transformation

Strategy, operating-model design, platform transformation, architecture, vendor selection, and complex change

Quantitative Finance & Model Risk

Quantitative advisory, model development and validation, valuation frameworks, benchmarking, and specialist quantitative expertise.

AI & Productivity

Helping financial institutions determine where AI genuinely creates value, how it should be deployed, and whether expected productivity improvements are being achieved.

Data, Semantics & Architecture

Data architecture, semantic layers, ontologies, and the infrastructure required to make complex financial information usable by both humans and AI systems.

Specialist Technology

Deep expertise in complex capital-markets platforms, architectures, and legacy environments where specialist knowledge is difficult to source internally.

  • Front-to-back platform transformation

  • Model validation

  • Market-data lineage

  • Front-office pricing & P&L

| Selected Experience |

Front-to-back platform transformation • Model validation • Market-data lineage • Front-office pricing & P&L

View Selected Work >

Capital Markets Operating Models Are Changing

Banks are being asked to reduce cost, deploy AI, modernise legacy platforms and internalise strategically important capability without weakening control, resilience or client service. The difficult question is not whether to change, but where and how.

Beltis helps leadership teams determine what to internalise, automate, standardise, partner for or retain as specialist capability.

Build or buy?

Internalise or partner?

Automate or retain human judgement?

Standardise or preserve specialist capability?

Capital Markets Productivity Diagnostic

An independent diagnostic to determine where a capital-markets function can reduce cost and improve productivity without eroding control, resilience or critical expertise.

01

Establish the baseline

Map current cost, capacity, productivity, quality, control and resilience.

02

Determine what should change

Assess activities across the available levers: internalise, automate, outsource, relocate, retain as specialist or eliminate, and quantify the expected benefit and risk.

03

Define and assure the outcome

Establish the target operating model, priorities, implementation roadmap and measures required to determine whether the expected improvement has actually been achieved.

What you receive: a quantified opportunity assessment, decision map and prioritised implementation roadmap.

Request a Diagnostic

Selected Work

Structured Products Front-to-Back & Risk Platform Transformation

A global structured-products business relied on a legacy front-office and risk platform, middleware, desk tools and multiple upstream and downstream data exchanges. The resulting architecture involved significant manual processing and operational complexity and constrained the firm's ability to scale its structured-products franchise. The client initiated a multi-year programme to implement a new front-office booking and risk-management platform, integrate it with its proprietary pricing and analytics capabilities, strengthen front-to-back processing and progressively retire legacy components.

Outcomes:

  • Successful production go-live covering equity structured products and associated hedges.

  • Target architecture implemented around the new platform, an enterprise integration layer and proprietary pricing and analytics services.

  • End-to-end implementation support spanning data migration, SIT/UAT, production readiness, cutover and post-go-live.

  • Foundation established for phased legacy-platform retirement and expansion to additional asset classes.

    • Business: Structured Products

    • Asset classes: Equities, FX, Commodities, Rates and Credit

    • Coverage: Global

    • Users: Front Office, Risk, Operations, Product Control and Finance

    • Duration: Multi-year transformation

    • Front-to-back platform architecture

    • Front-office and risk-platform migration

    • Pricing and analytics integration

    • Data migration and integration architecture

    • Risk, Operations, Product Control and Finance integration

    • Phased legacy-platform decommissioning

    • Vendor due diligence and solution validation

    • Functional and technical requirements assessment

    • TCO and commercial analysis
      Target architecture and implementation roadmap

    • Programme governance and implementation support

    • SIT, UAT, cutover and post-go-live support

  • The existing environment relied on a legacy front-office and risk platform, middleware and a number of desk and downstream components, with multiple data exchanges and quasi-manual processes across the trade lifecycle.

    The client needed to introduce a scalable front-office booking and risk-management platform while retaining its proprietary pricing and analytics capabilities, improving integration across Front Office, Risk, Operations, Product Control and Finance, and progressively removing legacy technology.

  • Our team initially led the advisory and due-diligence phase, assessing functional and technical requirements, benchmarking the selected solution against alternatives, analysing total cost of ownership and defining the target architecture and implementation roadmap.

    We subsequently supported the implementation through data migration, platform integration, configuration, SIT/UAT, end-to-end workflow testing, production readiness, cutover and post-go-live support.

    The target architecture combined the new front-office and risk platform with an enterprise integration layer and the client's proprietary pricing and analytics services, minimising platform customisation while preserving ownership of core quantitative capabilities.

    The programme was delivered in phases, with the first structured-products portfolio successfully moved into production before coverage was expanded to further asset classes.

Defining Next-Gen Pricing, Sales-Trader Workflow & Real-Time P&L Platform

A global investment bank was undertaking a strategic transformation of its Interest Rate Derivatives technology stack. Heavy reliance on Excel for pricing, calibration and risk analysis, fragmented front-office workflows and regional infrastructure constraints were limiting scalability, increasing operational complexity and slowing the delivery of new capabilities. Our team was engaged to translate the client's target-state strategy into actionable platform decisions across Pricing Development Framework (PDF), Sales & Trader Workflow (STW) and Real-Time P&L/Risk.

Outcomes:

  • Structured platform evaluation completed across PDF, STW and Real-Time Risk P&L

  • Functional and technical requirements consolidated into a consistent decision framework

  • Vendor and internal-build options benchmarked across functionality, architecture, performance, integration, cost and delivery effort

  • Target-state integration and platform implications validated with business, Quant and Technology stakeholders

  • Phased implementation roadmap, resource model and investment requirements established for execution

    • Global Investment Bank

    • Business: Global Rates / Interest Rate Derivatives

    • Coverage: Europe, UK, US and Americas/LATAM expansion

    • Stakeholders: Trading, Sales, Quant, eTrading, Product Strategy, IT & Architecture

    • Duration: Approximately 6 months

    • Pricing Development Framework: pricing, calibration, model development and simulation

    • Sales & Trader Workflow: UI and front-office workflow automation

    • Real-Time Risk & P&L

    • Target-state architecture and integration

    • Vendor vs internal-build evaluation

    • TCO, implementation and resource modelling

    • Requirements refinement and validation

    • RFI/RFP design and lifecycle management

    • Vendor workshops, demos and technical deep dives

    • Quantitative scoring and comparative assessment

    • Architecture and integration assessment

    • TCO and implementation modelling

    • Executive recommendation and phased roadmap

  • The client relied heavily on Excel-based processes for pricing, calibration and risk analysis, alongside fragmented tooling across front-office workflows. This increased operational complexity, constrained real-time risk and P&L visibility and extended the time required to introduce new functionality.

    The transformation therefore focused on:

    • establishing a scalable Pricing Development Framework;

    • providing an integrated Sales & Trader Workflow for voice and electronic flows;

    • enabling real-time intraday risk and P&L capabilities;

    • integrating the new components with the existing eTrading and post-trade architecture; and

    • reducing reliance on strategic Excel-based workflows while retaining control of proprietary models and analytics.

  • Our team converted the client's strategic technology vision into a structured platform-selection and execution framework across Pricing Development Framework, Sales & Trader Workflow and Real-Time P&L/Risk.

    We refined functional and technical requirements, designed and managed the RFI/RFP process, coordinated vendor workshops and technical deep dives, and applied a consistent scoring methodology across functionality, integration, architecture, security, performance, development effort and support.

    The assessment compared external platforms with internal-build alternatives and incorporated implementation effort, total cost of ownership, resource requirements and time-to-market.

    We then translated the findings into an executive recommendation and phased roadmap, covering MVP sequencing, integration dependencies, organisational requirements and the path from platform decision to implementation.

    Key delivery elements included:

    • Vendor workshops, demos, and technical deep dives

    • Side-by-side comparison and final recommendation

    • MVP-led implementation roadmap (12 months) with phased rollout

    • Embedded P&L approach within the pricing layer

End-to-End Market Data Lineage, Usage Control & Cost Optimisation

A global Tier 1 bank needed greater transparency over how market data was sourced, distributed and consumed across its technology estate. With large volumes of data flowing through multiple applications and business functions, the bank lacked a consolidated view of where individual data points travelled, whether they were actively used and what they ultimately cost. The engagement established an end-to-end market-data control framework and supporting Market Data Hub, providing visibility from provider ingestion through to downstream application and business consumption.

Outcomes:

  • End-to-end traceability of market data from source to downstream consumers

  • Centralised visibility of data usage across applications and business functions

  • Identification of unused or redundant data subscriptions and feeds

  • Automated notification of data requiring addition, review or removal

  • Market-data cost attribution by application, business line and portfolio

  • Stronger evidence base for vendor rationalisation and sourcing decisions

    • Global Tier 1 Bank

    • Business Function: Market Data supporting Sales, Trading & Equity Research

    • Coverage: Global

    • Asset classes: Cross-asset / enterprise market data

    • Duration: 6 months

    • Market-data lineage and traceability

    • Data consumption and usage analytics

    • Market-data cost attribution

    • Data quality and governance

    • Dashboarding and exception notification

    • Market Data Hub architecture and implementation

    • Business and data analysis

    • Data-lineage and governance framework design

    • High-level and low-level solution architecture

    • Dashboard and notification design

    • Market Data Hub implementation and testing

    • QA, DevOps and release management

    • Ongoing platform evolution and control

  • The client consumed large volumes of market data across a complex global application estate but lacked a consolidated view of how individual data elements flowed from providers through to downstream users.

    This made it difficult to determine which data was genuinely required, identify redundant or unused consumption, attribute costs accurately and assess whether market-data providers remained economically optimal.

    The client therefore required a solution capable of tracing data end to end, monitoring consumption and exceptions, and providing actionable information to both technology and business stakeholders.

  • Our team designed and implemented an end-to-end market-data lineage and control framework supported by a central Market Data Hub.

    Business and data analysts mapped data flows and consumption patterns across the application estate, while architecture and engineering specialists designed the target solution, dashboarding and notification framework.

    The resulting capability provided traceability from market-data provider through to individual consuming applications, identified unused or redundant data, and enabled costs to be analysed at application, business-line and portfolio level.

    Automated notifications supported ongoing control by identifying data that was no longer being consumed as well as new data requirements, turning lineage from a static documentation exercise into an operational management capability.

Accelerating Quantitative Model Validation for a Tier 1 Global Bank

A Tier 1 global bank faced a growing backlog of complex pricing and risk models progressing through its validation lifecycle. Limited capacity within the model-control function, combined with demanding testing and documentation requirements, was constraining throughput and delaying production readiness. Our team provided specialist quantitative validation capability across methodology review, calibration and stability testing, Python-based test automation and validation documentation, supporting the bank through a multi-phase programme covering a portfolio of models.

Outcomes:

  • Expanded validation capacity across a multi-phase portfolio of complex pricing and risk models

  • Introduced Python-based automation to improve reproducibility and consistency of model testing

  • Strengthened validation documentation, methodology review and governance across the model lifecycle

  • Supported models through testing, challenge and production-readiness processes across Quant Analytics, Model Control and Independent Validation

    • Tier 1 Global Investment Bank
      Business Function: Quant Analytics & Model Integrity and Validation

    • Coverage: Complex pricing and risk models

    • Primary focus: Equity & Hybrid Derivatives

    • Geography: Global

    • Duration: 15-month multi-phase engagement

    • Quantitative model validation

    • Model methodology and assumptions review

    • Calibration and stability testing

    • Python-based test automation

    • Validation documentation

    • Model governance and production readiness

    • Embedded specialist quantitative validation support

    • Full methodology and model-assumption review

    • Independent calibration, scenario and stability testing

    • Reproducible Python-based testing workflows

    • Validation report and documentation production

    • Support through Model Control review and Independent Validation

  • The client's Quant Analytics function had a portfolio of models progressing through development and validation, while capacity within Model Integrity & Control was insufficient to meet the required delivery timetable.

    The validation process required detailed methodology assessment, independent calibration and stability testing, reproducible testing evidence and comprehensive documentation before models could proceed through Independent Validation and into production.

    Additional specialist capacity was therefore required to increase validation throughput while maintaining the bank's model-governance and documentation standards.

  • Our team provided embedded quantitative model-validation expertise within the client's Model Integrity & Control framework.

    For each model, the engagement included review of the underlying methodology, assumptions and model structure; independent calibration and stability testing across different market conditions; and assessment of pricing and risk behaviour.

    Python-based testing workflows were introduced to improve reproducibility, consistency and efficiency, reducing reliance on manually repeated validation procedures.

    The team also produced and supported comprehensive validation documentation and worked with the client's Quant Analytics, Model Integrity & Control and Independent Validation functions through the review and production-readiness lifecycle.

    Following the initial phase, the engagement was extended to cover an additional portfolio of models and broader validation activities.

Start a Confidential Conversation.

Briefly tell us what you are trying to solve. A senior Beltis adviser will respond directly.