Delivery that inspires.

A new and refreshing approach.






Advanced Capability Solutions (ACS) is a consultancy that specialises in delivering business, data and technology solutions. Our rapid growth to date is based on the results we deliver and the thought leadership we hold across a host of industry issues. We are functional experts in finance, risk, treasury and compliance.



Our comprehensive range of business services, technology services and data solutions will help you navigate the complex headwinds you face and harness the opportunities you see. Our business services span strategy, finance, risk, treasury, compliance and analytics verticals across all industries and geographies.

Business Services


Business Transofrmation, Risk Management, Treasury Solutions, Finance Transformation, Regulatory Compliance.

Technology Services


Technology Transformation, Programme Delivery, Application Development,Quality Assurance, Managed Services

Data Services


Big Data, Collaboration, Interconnectivity, Data Quality Framework, Business and Data Analytics


Basel III Liquidity

Basel III Calculation and Liquidity Risk Reporting

The tier 1 wholesale banking client engaged ACS to calculate and report:

  • • Short term liquidity - measured by Liquidity Coverage Ratio - maintain liquid asset buffer equal to total net cash outflow over a 30 day stressed period; and
  • • Long term liquidity - measured by Net Stable Funding Ratio - available amount of stable funding to exceed funding requirements over a one year stressed period
  • Consideration
  • • Focus on Tier 2 capital
  • • Utilise common data for management reporting and regulatory reporting



  • • Design incorporates credit value adjustment measures as applied to counterparty credit
  • • Calculate counterparty credit exposures for derivatives, repo and securities financing
  • • Ensure OTC derivative contracts cleared through central counterparties
  • • Convert loss estimates into capital and liquidity requirements
  • • Stress testing results a pre requisite to the calculation process
  • • Define and include liquid assets as per policy
  • • Calculated cash flow mismatches based on contractual obligations
  • • Solution is built on a defined data set comprising stressed results
  • • Integrated management and regulatory reporting
  • • Provision of dashboard for group and business unit management reporting
  • • Summarized management reports output to mobile media



  • • Satisfied Basel III Calculation and LCR / NSFR regulatory requirements
  • • Enable management to plan and manage liquidity requirements more effectively
  • • Reduce funding costs for the bank


DWH Testing

End to End Data Warehouse Testing (FDSF)


The PRA initiated an industry-wide programme called FDSF (Firm Data Submission Framework) - working with the largest UK Banks to standardise and automate the CP Data Collection process and to address common issues identified in previous collection exercises across the industry. For this Tier 1 banking client, the PRA had raised concerns on submissions around data quality, particularly within the investment banking balance sheet, structured finance and wholesale RWA submissions. The PRA had indicated that these issues must be addressed before the next Core Stress Testing Programme was to be run and indicated that the bank must have a framework in place to run the CP Stress Testing Programme on a quarterly basis.

The reporting solution comprised:

  • • ETL: ETL processes (using PL/SQL) to load the data with the required transformations and aggregations
  • • Data: a data model and meta data that represents an accurate picture of the PRA - FDSF Semantic data model
  • • Reporting Tools: a set of required reports as specified in the PRA data request templates for Retail delivered as Advanced Reporting reports ('AR' - using new OBIEE reporting toolsets) and in csv format


Testing Approach

As part of the Agile Methodology, ACS analyzed the requirements and created a Test Strategy for FDSF work streams within the Strategic Risk, Finance and Treasury Transformation using relevant Connect4Change templates.
Working in line with the ACS best practice Test Framework, ACS delivered a comprehensive Test Strategy ensuring full test coverage of all components involved in the end to end flow - from data processing through to data presentation.

Testing Solution

System Testing
An automated PL / SQL based methodology is adopted to perform system testing in which the process validates records count and High Level Measures at various phases of ETL i.e. Staging, Relational and Presetation Layer. The process produces results reported at various logical entity levels including division and data set level which enables the team to identify potential DQ issues and hence resolve them quickly.

System Integration Testing
The PRA have Retail specific requirements for FDSF, and these requirements are peculiar to FDSF only. As a result of this, divisional data submissions for Retail FDSF are uploaded as new data sets within strategic regulatory platform and there is a need to test the integration of these Divisional feeds with existing strategic regulatory platform data. A set of reconciliation dashboards with reconcilable measures were designed for this purpose. These reports were built in OBIEE so that even the end users could download the reports and review the results. The dashboards were supported by detailed Excel based data files which help identify the reasons for differences expedite the resolution process thereon.

Business Acceptance Testing
Providing a testing team with strong Retail business expertise, ACS proved that Business Acceptance Testing builds customer confidence in the solution provided and smoothens the UAT completion process. To this end, the testing team performed functional testing of the data and reports and ensured that all applicable business validations / checks were passed thereon prior to UAT commencement. With this testing already completed successfully upfront, the business acknowledged that approximately 40 percent of their test cases had already passed successfully at the initiation of the UAT phase.


ACS believe that the testing phase should not just be used to identify issues, but should be used to correct the root cause of the issue itself. The testing team, with strong business expertise, were able to work with the development team to investigate and resolve root causes of testing problems. Other benefits include:

  • • Quick and accurate testing as a result of automated testing.
  • • Time and cost savings due to early identification of issues.
  • • Improved business confidence and a smoothed UAT process, with minimal defects.
  • • Robust solutions validated by nonfunctional testing including performance, data volume, transformation and negative testing.


DQ validation

Data Quality Validation Framework


Obtaining adequate data quality (DQ) is one of the key challenges the banking Industry faces. The Tier 1 UK Banking Client engaged ACS to design and implement a suitable data quality validation framework for Group Risk and regulatory reporting purposes. It is critical that data is generally processed efficiently through appropriate checks and cleansing processes to ensure that the data warehouse is both complete and contains data that reflects the true business facts, thus adequately supporting stakeholders in the decision making process.
Given the huge number of DQ checks that occur in most data warehousing processes, the proposed solutions needed to be both effective and reusable.

  • • ETL: ETL processes (using PL/SQL) to load the data with the required transformations and aggregations
  • • Data: a data model and meta data that represents an accurate picture of the PRA - FDSF Semantic data model
  • • 3. Reporting Tools: a set of required reports as specified in the PRA data request templates for Retail delivered as Advanced Reporting reports ('AR' - using new OBIEE reporting toolsets) and in csv format



A Validation Framework solution was proposed and implemented by ACS. This not only addressed the inherent data quality issues across Group risk and Regulatory reporting but also provided a scalable and reusable solution that could be integrated with any Business Intelligence tool for Dashboard Reporting purposes (e.g. OBIEE, Crystal Reports, Business Objects etc.)


  • • Performs all data quality validation
  • • Performs the Business rules validation which, depending upon the severity of data quality, classifies the issues either in warnings or blockers.
  • • Dashboard reporting using BI tools.
  • • Minimal time required to implement new business rules
  • • Reduced overall project costs since there is no need to write code for project specific validations and most of the rules are reusable across various work streams
  • • Supports multiple databases (Oracle, SQL Server etc).


FTP Strategy

FTP Data Transformation Strategy

To implement an FTP and Liquidity Risk measurement capability for all divisions at a Tier 1 global bank. This would involve determining the liquidity characteristics of all aspects of the bank's on balance sheet items and using those to select a rate on a funding curve which could be used to determine funding charges and rebates.

Data Constraints

  • • Divisions did not agree their own data sets within Treasury reporting systems.
  • • Treasury reporting systems did not reconcile to financial and risk data.
  • • Current Treasury reporting systems did not contain sufficient data for FTP purposes.
  • • Granular historic data used to price the back book was unavailable.
  • • Strategic Treasury transformation programme would deliver enhanced Treasury technical landscape in 3 years.


Solution Design and Benefits
As a result of the constraints the agreed approach involved divisions supplying their own granular data that met defined standards. This approach had the following benefits:

  • • Defined ownership and greater understanding. Detailed operating model and control framework documentation clearly stated responsibilities that divisions owned data in Treasury system, whilst Treasury performed data quality reviews and relied on divisions control framework for data assurance.
  • • Defined data quality standards. Treasury defined and communicated minimum standards. e.g. Divisional transfers must agree on both sides before processing.
  • • Solution design ensured reconciliation to golden source. Golden source balances were overlaid with granular data that highlighted gaps.
  • • Divisions motivated to fix data quality issues. Data quality gaps were priced using most prudent assumptions which quantified cost to division of data quality failings.
  • • Flexible change framework. As division feeds were separated a division was able to enhance its own feeds with no subsequent knock on effect to other divisions.
  • • Realistic data expectations e.g. historic data was required to assess the back book which was unavailable. A solution was devised to infer the historic data that would achieve a broadly similar outcome without creating a cottage industry in recalculating history.
  • • Solution sharing. Many data enrichment solutions created by Treasury that were later embedded within divisions.
  • • Common data used for multiple purposes with Treasury. Data shared with funding plan and cost allocation solutions.


Risk Reporting

Strategic Risk and Regulatory Reporting Platform

The engagement encompassed the delivery of a single consolidated strategic platform at a Tier 1 global bank to handle Risk and Regulatory Reporting, MI reporting and other external stakeholder reporting commitments.


Post the global financial crisis, the bank had undergone massive scrutiny for their Risk management and regulatory reporting practices. To satisfy Regulators and other stakeholders, the bank had undertaken a strategic transformation of their Risk reporting platform.

  • • Legacy reporting platform had different physical instances of daily / weekly / monthly reporting.
  • • Legacy reporting tool was on outdated technology.
  • • Legacy reporting process had many manual steps and dependencies for final report productions and submissions to regulators.
  • • Legacy platform had many data quality and reference data issues leading to data adjustments and corrections.



  • • Designed single consolidated platform for daily / weekly / monthly reporting.
  • • Defined industry standard conceptual data model and data warehouse.
  • • Gap analysis on data sourcing and processing mechanism to reduce data quality impact.
  • • Analyzed Retail and Wholesale data requirements for complete coverage of Exposures and Limits for Risk Reporting.
  • • Replaced ALL regulatory reporting, including IFRS, Basel 11, inflight Basel III, COREP, FINREP and other MI reporting such as Intra Group Reporting, Large Exposure Reporting, etc. Analyzed Country Risk reporting framework.
  • • Gap analysis of current data feeds against data requirements.
  • • Defined revised Target Operating Model for risk reporting.
  • • Service level agreements with source data providers.
  • • Operational level agreement with data consumers.



  • • Single and common data warehouse for all Risk reporting.
  • • ETL solution for Exposure v/s Limit reporting.
  • • ETL solution for Collateral management and Risk Mitigation.
  • • Country Risk Transfer and Reporting framework.
  • • OBIEE based risk reporting for all Regulatory Reporting, COREP, Capital and other stakeholder reporting.
  • • Enhanced reporting for day to day analysis for business community.
  • • Automated data capture and control framework for reducing manual processes and data adjustments requirements.



  • • Supported Bank's license to operate. Improved Regulatory confidence with the Financial Services Authority (FSA).
  • • Improved timing of reporting, accuracy and completeness.
  • • Reduced reconciliation and data input effort and improved process workflow.
  • • Reduced infrastructure maintenance and support cost due to single platform for different reporting.


Treasury Data

Common Treasury Data Source

The engagement was to align a capital planning model with a common Treasury data source in the Group Treasury function of Tier 1 global bank.


  • • Capital planning model developed to address immediate needs after financial crisis. Data sourcing was not originally in scope as development dependencies on source systems would slow implementation.
  • • Initial data sourcing heavily dependent on manual ETL and reconciliation processes.
  • • Treasury data strategy to leverage common data source wherever possible.



  • • Granular data requirements analysed across Treasury functions to define common data requirement. This involved listing required data attributes with definitions and Treasury uses.
  • • Gap analysis of current data feeds against data requirements.
  • • Target source data agreed.
  • • Defined plans for data remediation.
  • • Defined workaround and exceptions process.
  • • Design, build and test of feeds and data warehouse extension.
  • • Service level agreements with source data providers.


Agreed Solution
Treasury common data would be enhanced to enable capital planning:

  • • Legal and management hierarchy.
  • • Credit risk weighted asset feeds.

Feeds from common data warehouse to capital planning application built:
  • • Hierarchy.
  • • Credit risk weighted assets.
  • • Granular Treasury issuance data.

Although capital planning required financial planning data, it was excluded from the common Treasury data framework for the following reasons:
  • • Near term implementation of new planning solution would render development work obsolete.
  • • Planning data was to be summarised for use by other applications e.g. new business was not in enough detail to enable NII modelling.


Realised Benefits

Treasury issuance consistent between financial forecasting, ALM and capital planning applications.

  • • Consistent RWA's between Risk, capital planning and regulatory reporting systems.
  • • Consistent hierarchies between Finance systems and those Treasury applications using common data warehouse.
  • • Reduced reconciliation and data input effort.


Treasury Function

Treasury Transformation Business Architecture

The requirement was to structure Treasury functional components to facilitate a Treasury transformation in a Tier 1 global bank.


  • • Organise Treasury operations into end state functional components i.e. produce an end state capability model.
  • • This would be used to facilitate a gap analysis and validate end state architecture.
  • • The requirement should address emerging regulatory requirements (Basel III impact etc.)


Solution Design

The Treasury functional model was organised into the following deliverable components based on the functional control approach to integrate Treasury interfacing / dependant business units.

  • • Capital Management comprising capital planning, intra group limits and capital reporting.
  • • Funding comprising unsecured funding and securitisation.
  • • Balance Sheet Management comprising net interest income sensitivity and gap modelling, funding and balance sheet planning.
  • • Liquidity Management & Reporting comprising liquidity risk, stress testing, behavioural modelling, regulatory and statutory reporting.
  • • Transfer Pricing comprising funds transfer pricing.
  • • Non-Trading Risk Management comprising interest rate risk, FX and hedge accounting.
  • • Limit Management.
  • • Reporting & Monitoring comprising internal and external reporting and management information.
  • • Policy & Governance comprising treasury policies and procedures, strategy and controls.



  • • Capability model drawn out for strategic transformation.
  • • Touchpoints drawn out with Finance and Risk driving effective integration across the enterprise.


Stress Testing

Integrated Stress Testing Solution

ACS were engaged to develop an integrated stress testing solution for a Tier 1 UK retail and wholesale bank.


  • • Centrally administered stress testing function.
  • • Stress tests aligned with Bank's risk policy and regulatory requirement.
  • • Top Down stress test model to be available for business operations decisions.
  • • Integrate various stress tests across risk types.
  • • Availability of accepted and agreed common data used for stress tests.


Solution Design

  • • The approach was to organise the stress testing effort so as to require minimum overlap with other risk functions, be centrally administered and comply with the Bank's risk policy.
  • • Develop a stress test policy for the Bank comprising the sum of its retail and wholesale operations.
  • • Define integrated stress testing to include top down, bottom up, enterprise wide, across all major risk types and embedded in business operations.
  • • Develop a central stress test engine to run tests which consolidated financial measures and business scenarios.
  • • Define data to be used for stress testing and usage policies including creation of a specific reconciled data warehouse.
  • • Define finance infrastructure to support the data required - transactions and accounting data.
  • • Define risk specific data requirements.
  • • Develop impairment, capital, profitability and liquidity models for stress tests.
  • • Define model selection criteria and calculation methods.
  • • Define reporting requirements for management, business operations, regulatory and rating agencies.



  • • Satisfy regulatory requirements.
  • • Enable management to plan for contingency scenarios.
  • • Make appropriate credit and capital allocation decisions.
  • • Manage enterprise risk better.


About Us

We build genuine partnerships with our clients and technology partners, and create relationships driven by results. We deliver strategic but practical programmes of work that deliver real, discernible value. Working with our clients to help solicit their strategic vision, we provide the capability to harness that vision.

Rohit Arora

Senior Partner & MD

Rohit is an accomplished technology leader with over 15 years of international experience in managing portfolios of multi-million dollar technology initiatives for many Fortune 100 organisations including Goldman Sachs, Bank of America Merrill Lynch and NBC. Rohit is responsible for Global IT Delivery through our offshore development centre in Gurgaon, India.

Matt Good

Senior Partner

Matt has 20 years business change and technology implementation experience across a global portfolio of investment, corporate and retail banks including RBS, Lloyds Banking Group, HSBC, CitiGroup and ABN AMRO. Over the last 2 years Matt has led a team of leading Industry Practitioners to create ACS, a delivery focused consultancy and technology delivery house.

Graham Burchell

Senior Partner

Graham is an experienced business leader with over 15 years of business strategy and operational management experience in large, small and early stage companies across multiple industries including financial services and media. In addition to managing the day to day operations Graham is responsible for overseeing ACS strategic partnerships and growth strategy.


Contact Us

  • ACAP Solutions India Pvt Ltd

    351, 3rd Floor,

    Tower B-2 Spaze I-Tech Park

    Sohna Road, Sector 49

    Gurgaon - 122018

  • +91 124-4360757

  • +91 124-4360754