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.

Build. Transfer. Assure.

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Explore our Capabilities
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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.

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.

Capital Markets are Changing

Banks must modernise, deploy AI, reduce costs, and strengthen control, without losing strategically important capability

Beltis helps clients answer these questions and execute the decisions that follow:

What should we build?

What should we automate?

What should we internalise?

Where is specialist expertise still essential?

Capital Markets Productivity Diagnostic

Determine what to internalise, automate, outsource, relocate, retain as specialist—or eliminate.

  1. Establish the baseline
    Measure current cost, productivity, quality, control, and resilience.

  2. Identify the opportunity
    Determine which activities should change and quantify the expected benefit.

  3. Assure the outcome
    Test whether implemented changes deliver sustainable improvement.

Lower cost is not enough. The objective is a better operating model.

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Selected Work

Front-to-Back Platform Transformation & Legacy Decommissioning

Beltis supported a capital markets business in the design and delivery of a full front-to-back trading platform, including migration from legacy front-office systems and retirement of fragmented middle- and back-office tooling. The programme aimed to establish a single, integrated platform and eliminate end-user tools and shadow systems.

Outcomes:

The programme delivered full platform consolidation and successful migration from legacy front-office systems, removing

reliance on tactical tools and fragmented platforms.

Key outcomes included:

  • Single source of truth for trades, risk, and P&L

  • Retirement of EUCs and legacy MO/BO platforms

  • Improved STP rates and reduced operational risk

  • Scalable platform enabling future product and asset expansion

  • ● Structured Products Business

    ● Asset Class: Cross-asset (Equities, Rates, FI, Commodities, FX)

    ● Coverage: Global

    ● Users: FO, MO, BO, Product Control, Finance

    ● Duration: Multi-year transformation

  • ● Front-to-back platform architecture

    ● Front-office migration (FO systems replacement)

    ● Middle/back office consolidation

    ● Data model and golden source design

    ● Legacy system and EUC decommissioning

  • ● Target operating model definition

    ● Platform architecture and vendor strategy

    ● Migration planning and execution support

    ● Data and workflow consolidation

    ● Governance and delivery model setup

  • The client operated a highly fragmented landscape, with multiple front-office systems, extensive Excel/EUC usage, and

    disconnected middle- and back-office platforms. This led to reconciliation breaks, elevated operational risk, and no single source

    of truth.

    Key requirements focused on:

    • End-to-end platform across FO → MO → BO → Finance

    • Elimination of EUCs and manual processes

    • Scalable data model with full lifecycle management

    • Improved STP and operational efficiency

  • Beltis defined a target front-to-back architecture integrating trading, pricing, risk, and post-trade processing within a unified

    platform and centralised data layer.

    Key delivery elements included:

    • Phased FO migration with parallel run and controlled cutover

    • EUC and legacy decommissioning with platform-native replacement

    • Standardised trade lifecycle (capture → processing → reporting)

    • Cross-functional delivery model across FO, Risk, Ops, and Finance

Sales Trader Workflow + Pricing Development Framework + Real-Time P&L (RFI+RFP)

Beltis supported a global bank in the evaluation and selection of a next-generation front-office platform, covering Sales Trader Workflow (STW), Pricing Development Framework (PDF), and Real-Time P&L. The objective was to replace fragmented Excel-based processes with a scalable, integrated, and real-time trading stack.

Outcomes:

Selected best-fit solution for unified STW + PDF + P&L stack

Defined target-state front-office architecture with:

  • Real-time pricing and risk

  • Embedded P&L and analytics

Reduced dependency on Excel and manual workflows

Accelerated product onboarding and time-to-market

Established scalable foundation for multi-asset expansion and eTrading integration

  • ● Global Tier 1 Bank

    ● Business: Rates & Derivatives Trading

    ● Coverage: Europe, US, LATAM

    ● Stakeholders: Trading, Sales, Quants, IT, eTrading

    ● Duration: 9 months

  • ● Sales Trader Workflow (UI & workflow automation)

    ● Pricing Development Framework (models, calibration, simulation)

    ● Real-Time P&L & Risk

    ● Target architecture alignment

    ● Vendor selection (RFI + RFP)

  • ● Full RFI/RFP lifecycle management

    ● Vendor scoring and benchmarking

    ● Technical architecture validation

    ● Cost and implementation modelling

    ● Strategic recommendation & roadmap

  • The client relied heavily on Excel-based processes and fragmented tooling across the front office, resulting in disconnected

    pricing, trading, and risk workflows. This environment limited intraday visibility, with no consolidated real-time P&L or risk view,

    and significantly increased time-to-market for new products.

    Key requirements focused on:

    • Delivering an integrated Sales & Trader Workflow (STW) for front-office users

    • Establishing a scalable pricing and model development framework

    • Embedding real-time P&L within the pricing and risk layer

    • Reducing operational complexity and development overhead

  • Beltis defined a target platform architecture integrating STW, Pricing Development Framework, and Real-Time P&L into a

    unified front-office stack. A structured RFI/RFP process was executed across leading vendors, supported by a quantitative

    scoring framework covering product coverage, integration, performance, and development effort.

    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

Enabling End-to-End Market Data Lineage and Control at Scale

Over six months, Beltis partnered with a Tier 1 global bank to deliver an enterprise-wide solution for market data transparency and control. Faced with fragmented tooling, rising data volumes, and increasing cost pressures, Beltis deployed a cross-functional team to design and implement a full-stack market data hub, enabling end-to-end lineage tracking, real-time alerting, and significant cost optimisation across the client's data ecosystem.

Outcomes:

  • Enhanced Transparency: Daily identification and traceability of all collected data.

  • Cost Reduction: Clear visibility into unused data enabled the client to decommission redundant feeds.

  • Operational Efficiency: Alerting system highlighted data not being used or requiring intervention.

  • Governance: Reinforced audit trails and downstream impact tracking for each consumer of the data.

  • Strategic Control: Empowered the business to review costs by application, business line, and portfolio and make informed decisions about provider alignment.

  • ● Tier 1 Global Bank

    ● Core Business Function: Front-to-back

    ● Coverage: All asset classes

    ● Geography: Global

    ● Duration: 6 months

  • ● Data Lineage & Quality

    ● Market Data Management

    ● Risk & Regulatory Reporting

    ● Cost Attribution & Optimisation

  • ● End-to-end data control framework design

    ● Dashboard & alerting tool implementation

    ● Market Data Hub setup

    ● Advisory on governance and quality control

  • The client sought full transparency and control over market data usage across its global IT ecosystem. Key requirements included:

    • Identification of redundant or unused data to reduce unnecessary processing

    • Stronger control over how data propagates between systems to improve consistency and accuracy

    • Optimisation of market data acquisition and usage for cost efficiency

    • Centralised visibility of data flow through a real-time dashboard and notification system

    • Regulatory compliance readiness, with full cost transparency across high-volume environments

  • Beltis deployed a multidisciplinary data team with deep investment banking experience to execute a structured, phased approach:

    • Project & Data Management: Establishment of lineage, tracking, and governance frameworks.

    • Analysis & Architecture: Business and data analysts worked alongside architects to define target states.

    • Solution Engineering: Development of dashboards and a data notification framework.

    • Testing & Implementation: A full-stack data hub solution was implemented and validated across all domains.

    • DevOps & QA: Automation pipelines, quality frameworks, and release management ensured smooth delivery and evolution.

Accelerating Model Validation at Scale for a Tier 1 Global Bank

Over two years, Beltis supported the Quant Analytics and Model Validation teams of a leading global bank in accelerating the validation of complex risk and pricing models. With internal capacity limited and regulatory demands rising, Beltis deployed senior specialists to deliver rigorous methodology reviews, automated testing, and complete documentation — enabling faster, compliant model deployment with reduced operational risk.

Outcomes:

  • Accelerated Delivery: Completed all validations ahead of schedule — first 9 models in 9 months, remaining completed within the next 7–9 months.

  • Improved Efficiency: Automation led to reduced manual effort and consistent testing execution.

  • Stronger Compliance: Enhanced documentation and governance minimised regulatory risk and improved audit outcomes.

  • ● Tier 1 Global Bank

    ● Core Business Function: Quant Analytics and Model Validation

    ● Coverage: Equity and Cross-Asset Models

    ● Geography: Global

    ● Duration: 2 years

  • ● Quantitative Model Validation

    ● Regulatory Documentation

    ● Model Risk Management

    ● Python-based Automation

  • ● Full-scope model validation

    ● Calibration testing & QA

    ● Automated testing library deployment

    ● Documentation production and support

  • The client had a backlog of partially validated models that had not been productionised.

    Faced with limited internal validation resources, they needed to accelerate validation timelines without compromising on

    regulatory compliance or documentation quality.

    Sought external support to:

    • Validate models across asset classes

    • Automate repeatable testing

    • Strengthen regulatory audit-readiness

  • Deployed a senior model validation team with deep domain expertise across quant model testing, documentation, and automation.

    Methodology Evaluation: Reviewed model design and structure, ensuring appropriate theoretical and practical foundations.

    Stability & Calibration Testing: Conducted independent testing across multiple market conditions to confirm robustness and consistency.

    Automation of Testing: Used Python-based frameworks to enable reproducible and efficient model testing workflows.

    Documentation Delivery: Produced comprehensive validation reports aligning with best practices and internal governance standards.

    Provided ongoing support for production-readiness and change governance.

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